Datasets:

ahmedabdelwahed

/

Medical_papers_title_and_abstract_NLP_dataset

like 0

MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks

Neural network-based recommendation algorithms have become the state-of-the-art in recommender systems and can achieve very high predictive accuracy. However, these models are usually considered as black boxes in terms of their interpretability due to the complex structure of their hidden layers. In this research work, we propose MP4Rec, a recommender system using heterogeneous information networks to provide both accurate and explainable recommendations. MP4Rec uses of user-user and item-item similarity matrices and applies a newly proposed pair-wise objective function to make top-N recommendations which are transparent and explainable. The similarity matrices are created from metapaths constructed with the PathSim algorithm, node embeddings with cosine similarity or their combinations. The proposed pair-wise objective function incorporates an additional soft constraint for pushing more explainable items into the top-N recommendations. We have performed several experiments that show the effectiveness of our model by outperforming the state-of-the-art and providing both accurate and explainable recommendations in three well-known datasets.

New calculation method for exact length weighting factor in cone-beam computed tomography

In iterative reconstruction algorithms, it is very important to calculate the weighting factor quickly and accurately, which directly affects the reconstruction efficiency and accuracy of computed tomography images. In this paper, a new calculation method for accurate length weighting factor is developed. Firstly, the ray equation is determined according to the coordinates of the source and the ray's projection. Second, the intersections of the ray and the equidistant parallel planes along three coordinate axes are calculated, and the points intersecting with the reconstructed object are put into a set. Finally, when the X-axis coordinates of all points in the set are different, the points are arranged according to their X-axis coordinates; when the X-axis coordinates of all points are same, the points are arranged according to their Y-axis coordinates. The sorted point set is the intersections that the ray traverses the reconstructed object in sequence. The index and weighting factor of all voxels that the ray passes through can be determined by the sorted point set. Compared with the classical Siddon's method, it avoids the step of describing the intersections with parameters, and its calculation speed is increased by nearly 20%. The numerical simulations and real data reconstruction experiments are conducted to validate the proposed calculation method of exact length weighting factor.

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. This paper proposes a weighted broadcasting method that emphasizes the latest data of the time step to improve the nowcasting performance. This weighted broadcasting method allows the most recent rainfall patterns to have a greater impact on the forecasting network by extending the architecture of the existing encoding-forecasting model. Experimental results show that the proposed model is 1.74% and 2.20% better than the existing encoding-forecasting model in terms of mean absolute error and critical success index, respectively. In the case of heavy rainfall with an intensity of 30 mm/h or higher, the proposed model was more than 30% superior to the existing encoding-forecasting model. Therefore, applying the weighted broadcasting method, which explicitly places a high emphasis on the latest information, to the encoding-forecasting model is considered as an improvement that is applicable to the state-of-the-art implementation of data-driven radar-based precipitation nowcasting.

Traffic sign detection based on improved faster R-CNN for autonomous driving

The timely and accurate identification of traffic signs plays a significant role in realizing the autonomous driving of vehicles. However, the size of traffic signs accounts for a low proportion of the input picture, which increases the difficulty of detection. This paper proposes an improved faster R-CNN traffic sign detection method. ResNet50-D feature extractor, attention-guided context feature pyramid network (ACFPN), and AutoAugment technology are designed for the faster R-CNN model. ResNet50-D is selected as the backbone network to obtain more characteristic information. ACFPN is performed to decrease the loss of contextual information. And data augmentation and transfer learning are adopted to make model training more convenient and time-saving. To prove the availability of the proposed method, we compare it with mainstream approaches (SSD, YOLOv3, RetinaNet, cascade R-CNN, FCOS, and CornerNet-Squeeze) and state-of-the-art methods. Experimental results on the CCTSDB dataset show that the improved faster R-CNN achieves the frames per second of 29.8 and the mean average precision of 99.5%, which is superior to the state-of-the-art methods and more suitable for traffic sign detection. Moreover, the proposed model is extended to the Tsinghua-Tencent 100 K (TT100K) dataset and also achieves a competitive detection result.

The development and application of a simple methodology for recording rock art using consumer-grade digital cameras

A simple methodology for recording rock art has been recently developed in Australia and tested on Aboriginal rock art, including both petroglyphs and pictographs (engraved and painted images, respectively). The approach was based on commercial photogrammetric software and consumer-grade digital cameras, because it was believed that archaeologists, conservators and site managers need simple and cost-effective methods to record and document rock art. This methodology has been adopted subsequently by the Northumberland and Durham Rock Art Project working in conjunction with English Heritage, to assist in recording 1500 prehistoric engraved panels located across the north-east of England. Significantly, the fieldwork was carried out by enthusiastic volunteers, willing to sacrifice their weekends to capture imagery suitable for digital photogrammetry. This paper explains briefly how the recording technique was developed in Australia before indicating how expertise and equipment were integrated to allow UK-based volunteers to carry out data acquisition and, perhaps surprisingly, also the photogrammetry. This will demonstrate the value of mobilising the voluntary sector for heritage recording, which is feasible only if recording methodologies are based on cheap and simple instrumentation.

UBR2 targets myosin heavy chain IIb and IIx for degradation: Molecular mechanism essential for cancer-induced muscle wasting

Cancer cachexia is a lethal metabolic syndrome featuring muscle wasting with preferential loss of fast-twitching muscle mass through an undefined mechanism. Here, we show that cancer induces muscle wasting by selectively degrading myosin heavy chain (MHC) subtypes IIb and IIx through E3 ligase UBR2-mediated ubiquitylation. Induction of MHC loss and atrophy in C2C12 myotubes and mouse tibialis anterior (TA) by murine cancer cells required UBR2 up-regulation by cancer. Genetic gain or loss of UBR2 function inversely altered MHC level and muscle mass in TA of tumor-free mice. UBR2 selectively interacted with and ubiquitylated MHC-IIb and MHC-IIx through its substrate recognition and catalytic domain, respectively, in C2C12 myotubes. Elevation of UBR2 in muscle of tumor-bearing or free mice caused loss of MHC-IIb and MHC-IIx but not MHC-I and MHC-IIa or other myofibrillar proteins, including α-actin, troponin, tropomyosin, and tropomodulin. Muscle-specific knockout of UBR2 spared KPC tumor-bearing mice from losing MHC-IIb and MHC-IIx, fast-twitching muscle mass, cross-sectional area, and contractile force. The rectus abdominis (RA) muscle of patients with cachexia-prone cancers displayed a selective reduction of MHC-IIx in correlation with higher UBR2 levels. These data suggest that UBR2 is a regulator of MHC-IIb/IIx essential for cancer-induced muscle wasting, and that therapeutic interventions can be designed by blocking UBR2 up-regulation by cancer.

Estimation of the instantaneous pitch of speech

An accurate estimation of the pitch is essential for many speech processing applications, such as speech synthesis, speech coding, and speech enhancement. A widely used assumption in most common pitch estimation methods is that pitch is constant over a segment of short duration. This assumption does not apply in reality and leads to inaccurate pitch estimates. In this paper, we present a method for continuous pitch estimation that is able to track fast changes. In the presented framework, the pitch is modeled by a B-spline expansion and optimized in a multistage procedure for increased robustness. The performance of the continuous optimization procedure is compared to state-of-the-art pitch estimation methods and is evaluated both for artificial speech-like signals with known pitch, and for real speech signals. The results of the experiments show that our method leads to a higher accuracy of the estimate of the pitch than state-of-the-art methods.

Facile Synthesis of Transition-Metal-Doped (Fe, Co, and Ni) CuS/CuO/CS Nanorod Arrays for Superior Electrocatalytic Oxygen Evolution Reaction

Electrochemical water splitting is a promising way to produce sustainable, renewable, and clean H-2 fuel. The anodic half-reaction of electrochemical water splitting, oxygen evolution reaction (OER) lowers the overall efficiency of the system due to this four-electron process with sluggish reaction kinetics. The state-of-the-art catalysts for OER are based on precious metals (Ru and Ir). In this article, transition-metal-doped (Fe, Co, and Ni) CuS/CuO nanorod arrays on copper sheet (CS) substrates were synthesized for the first time via the facile solvothermal method, and the electrocatalytic activity of the synthesized material toward OER in alkaline media was investigated. Fe-doped CuS/CuO/CS showed superior electrochemical performance with an overpotential of only 340 mV at 10 mA/cm(2) current density. The OER performance of the material was compared with the state-of-the-art catalyst for RuO2/CS. The overpotential of RuO2/CS was 320 mV at 10 mA/cm(2) current density which is only 20 mV lower than the state-of-the-art catalyst. The enhancement of the OER activity was obtained by valence regulation upon doping Fe3+ and Fe2+ into CuS/CuO/CS nanorod arrays (NAs). The charge transfer resistance was lowered from 10.2 Omega/cm(2) for pristine CuS/CuO/CS NA to 1.2 Omega/cm(2) upon Fe doping. The electrochemically active surface area was increased from 36 to 51 cm(EcsA)(2) upon Fe doping. The Fe-doped CuS/CuO/CS shows an exceptionally high turnover frequency (TOF) of 0.68 s(-1). The catalyst was stable up to 1000 cycles of OER over 10 h. The increase in electrical conductivity, increase in electrochemically active surface area (ECSA), and creation of defect sites leading to preferential absorption of OH- upon Fe doping led to enhanced OER activity of Fe-CuS/CuO/CS. This is the first report of an Fe-doped CuS/CuO/CS nanoarray that shows superior OER activity with high TOF and excellent stability.

Links between the concentrations of gaseous pollutants measured in different regions of Estonia

The factors that determine the concentrations of air pollutants (NO, NO2, SO2, O3), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO2 emission quota, differences between rural and urban stations, maritime-continental difference for NO2 and ozone, and specific industrial impact in case of SO2. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO2, and CO2, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R 2 = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R 2 = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO2 concentration measured in the Tahkuse station did appear a good predictor for ozone (R 2 = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO2 influence during transport) overrides the local effects of NO and/or NO2.

Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification

We present AiTLAS: Benchmark Arena - an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena.

Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model

Studies of global hydrologic cycles, carbon cycles and climate change are greatly facilitated when. global estimates of evapotranspiration (E) are available. We have developed an air-relative-humidity-based two-source (ARTS) E model that simulates the surface energy balance, soil water balance, and environmental constraints on E. It uses remotely sensed leaf area index (L-ai) and surface meteorological data to estimate E by: 1) introducing a simple biophysical model for canopy conductance (G(c)), defined as a constant maximum stomatal conductance g(smax) of 12.2 mm s(-1) multiplied by air relative humidity (R-h) and L-ai (G(c) = g(srnax) x R-h X L-ai); 2) calculating canopy transpiration with the G(c)-based Penman-Monteith (PM) E model; 3) calculating soil evaporation from an air-relative-humidity-based model of evapotranspiration (Yan & Shugart, 2010); 4) calculating total E (E-0) as the sum of the canopy transpiration and soil evaporation, assuming the absence of soil water stress; and 5) correcting E-0 for soil water stress using a soil water balance model. This physiological ARTS E model requires no calibration. Evaluation against eddy covariance measurements at 19 flux sites, representing a wide variety of climate and vegetation types, indicates that daily estimated E had a root mean square error = 0.77 mm d(-1). bias = -0.14 mm d(-1), and coefficient of determination, R-2 = 0.69. Global, monthly, 0.5 degrees-gridded ARTS E simulations from 1984 to 1998, which were forced using Advanced Very High Resolution Radiometer Lai data, Climate Research Unit climate data, and surface radiation budget data, predicted a mean annual land E of 58.4 x 10(3) km(3). This falls within the range (58 x 10(3)-85 x 10(3) km(3)) estimated by the Second Global Soil Wetness Project (GSWP-2: Dirmeyer et al., 2006). The ARTS E spatial pattern agrees well with that of the global E estimated by GSWP-2. The global annual ARTS E increased by 15.5 mm per decade from 1984 to 1998, comparable to an increase of 9.9 mm per decade from the model tree ensemble approach (Jung et al., 2010). These comparisons confirm the effectivity of the ARTS E model to simulate the spatial. pattern and climate response of global E. This model is the first of its kind among remote-sensing-based PM E models to provide global land E estimation with consideration of the soil water balance. (C) 2012 Elsevier Inc. All rights reserved.

Correlated Primary Visual Texton Histogram Features for Content Base Image Retrieval

In this paper, a new feature descriptor, named correlated primary visual texton histogram features (CPV-THF), for image retrieval is proposed. CPV-THF integrates the visual content and semantic information of the image by finding correlations among the colour, texture orientation, intensity, and local spatial structure information of an image. Based on texton theory, box-shaped structural elements are designed for image texture analysis. The colour, texture orientation, and intensity feature histograms that are proposed in CPV-THF are represented by correlated attributes of the co-occurrence matrix. The performance of the proposed descriptor is compared with those of state-of-the-art texture, colour, shape, and local-pattern-based CBIR descriptors. Experiments are conducted on multiple standard natural image datasets, namely, corel1k, core15k, and corel 10k. Experimental results indicate that the proposed descriptor outperforms various state-of-the-art descriptors, such as GLCM, MTH, MSD, MS-LSP, SED, STH, and MTSD.

Revealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis

To identify whether an image has been JPEG compressed is an important issue in forensic practice. The state-of-the-art methods fail to identify high-quality compressed images, which are common on the Internet. In this paper, we provide a novel quantization noise-based solution to reveal the traces of JPEG compression. Based on the analysis of noises in multiple-cycle JPEG compression, we define a quantity called forward quantization noise. We analytically derive that a decompressed JPEG image has a lower variance of forward quantization noise than its uncompressed counterpart. With the conclusion, we develop a simple yet very effective detection algorithm to identify decompressed JPEG images. We show that our method outperforms the state-of-the-art methods by a large margin especially for high-quality compressed images through extensive experiments on various sources of images. We also demonstrate that the proposed method is robust to small image size and chroma subsampling. The proposed algorithm can be applied in some practical applications, such as Internet image classification and forgery detection.

A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery

Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant component and a piecewise linear component. The Fourier coefficients of each component satisfy an annihilation relation, which results in a structured Toeplitz matrix. We exploit the low-rank property of the matrices to formulate a combined regularized optimization problem. In order to solve the problem efficiently and to avoid the high-memory demand resulting from the large-scale Toeplitz matrices, we introduce a fast and a memory-efficient algorithm based on the half-circulant approximation of the Toeplitz matrix. We demonstrate our algorithm in the context of single and multi-channel MR images recovery. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the state-of-the-art approaches.

Kif26b contributes to the progression of interstitial fibrosis via migration and myofibroblast differentiation in renal fibroblast

Kinesin family member 26b (Kif26b) is essential for kidney development, and its deletion in mice leads to kidney agenesis. However, the roles of this gene in adult settings remain elusive. Thus, this study aims to investigate the role of Kif26b in the progression of renal fibrosis. A renal fibrosis model with adenine administration using Kif26b heterozygous mice and wild-type mice was established. Renal fibrosis and the underlying mechanism were investigated. The underlying pathways and functions of Kif26b were evaluated in an in vitro model using primary renal fibroblasts. Kif26b heterozygous mice were protected from renal fibrosis with adenine administration. Renal expressions of connective tissue growth factor (CTGF) and myofibroblast accumulation were reduced in Kif26b heterozygous mice. The expression of nonmuscle myosin heavy chain II (NMHCII), which binds to the C-terminus of Kif26b protein, was also suppressed in Kif26b heterozygous mice. The in vitro study revealed reduced expressions of CTGF, α-smooth muscle actin, and myosin heavy chain 9 (Myh9) via transfection with siRNAs targeting Kif26b in renal fibroblasts (RFB). RFBs, which were transfected by the expression vector of Kif26b, demonstrated higher expressions of these genes than non-transfected cells. Finally, Kif26b suppression and NMHCII blockage led to reduced abilities of migration and collagen gel contraction in renal fibroblasts. Taken together, Kif26b contributes to the progression of interstitial fibrosis via migration and myofibroblast differentiation through Myh9 in the renal fibrosis model. Blockage of this pathway at appropriate timing might be a therapeutic approach for renal fibrosis.

Chest X-ray image denoising method based on deep convolution neural network

To improve the visual effect of chest X-ray images and reduce the noise interference in disease diagnosis based on the chest X-ray images, the authors proposed an image denoising model based on deep convolution neural network. They utilise batch normalisation to solve the problem of performance degradation due to the increase of neural network layers, and use residual learning of the distribution of noise in noisy X-ray images. Specifically, the depthwise separable convolution is used to accelerate the convergence speed of network model, shorten the training time, and improve accuracy of the model. Compared to the several popular or the state-of-the-art denoising algorithms, their extensive experiments demonstrate that their method can not only achieve better denoising effects, but also significantly reduce the complexity of the network and shorten the computation time.

Higher Order Crossings Analysis of Signals Over Graphs

We propose the extension of the Higher Order Crossings (HOC) analysis concept for signals over graphs. The HOC over graphs sequence, (HOCg), is defined and its efficacy on graph signal discrimination is investigated. Compared with state of the art graph signal discrimination algorithms, the proposed HOCg combined with a Support Vector Machine classifier achieves fast and similar or better performance over five different benchmark data sets.

Clinical Significance of Preoperative Naples Prognostic Score in Patients With Non-Small Cell Lung Cancer

Background: Naples Prognostic Score (NPS) is a novel score based on inflammatory-nutritional indicators. We aimed to analyze the prognostic value of the Naples Prognostic Score in non-small cell lung cancer (NSCLC) patients following surgery. Methods: A total of 319 NSCLCpatients following surgery were analyzed in the retrospective cohort study. We analyzed the predictive value of Naples Prognostic Score for overall survival and recurrence-free survival in postoperative non-small cell lung cancer patients by using Kaplan-Meier survival curves and multivariate Cox regression analysis. At the same time, the time-dependent ROC and the area under curves were also created to compare the accuracy of different scoring systems. Results: According to NPS, we divided all patients into 3 groups，120 patients were divided into group 0, 161 patients were divided into group 1, and 38 patients were divided into group 2. The median survival time for all patients is 32 months, and the median survival times for different groups were 35 months, 31 months, and 28 months, respectively. The overall survival and recurrence-free survival survival curves of different groups were significantly different (both P < .05), and patients in the higher NPS groups had a disappointing prognosis. NPS may be an independent prognostic factor for overall survival and recurrence-free survival, according to the results of multivariate analysis (both P < .05). The area under curve showed that the accuracy of the NPS was significantly better than other score systems. Conclusions: The NPS is closely related to the long-term survival prognosis of patients with NSCLC, especially in stage III patients.

Integrated analyses of brain and platelet omics reveal their common altered and driven molecules in Alzheimer's disease

Platelets may serve as a perfect peripheral source for exploring diagnostic biomarkers for Alzheimer's disease (AD); however, the molecular linkage between platelet and the brain is missing. To find the common altered and driving molecules in both brain and the platelet, we performed an integrated analysis of our platelet omics and brain omics reported in the literature, and analyzed their correlations with AD-specific pathology and cognitive impairment. By integrating the gene and protein expression profiles from 269 AD patients, we deduced 239 differentially expressed proteins (DEPs) appeared in both brain and the platelet, and 70.3% of them had consistent changes. Further analysis demonstrated that the altered brain and peripheral regulations were pinpointed into 10 imbalanced pathways. We also found that 117 DEPs, including ADAM10, were closely associated to the AD-specific β-amyloid and tau pathologies; and the changes of IDH3B and RTN1 had a potential diagnostic value for cognitive impairment analyzed by machine learning. Finally, we identified that HMOX2 and SERPINA3 could serve as driving molecules in neurodegeneration, and they were increased and decreased in AD patients, respectively. Together, this integrated brain and platelet omics provides a valuable resource for establishing efficient peripheral diagnostic biomarkers and potential therapeutic targets for AD.

An approach to Palaeolithic networks: The question of symbolic territories and their interpretation through Magdalenian art

The Magdalenian (20,500-13,000 cal. BP) is an important period for the cultural evolution of societies in the European Upper Palaeolithic. It is characterized by a great increase in settlements and products, probably reflecting demographic growth. Along with other material evidence, symbolic products (art and ornaments) clearly expand. In association with the multiplication of representations, some of them become highly normalized (included geometric signs) whereas others develop a great diversity of forms (such as human depictions). The density of sites and more-or-less normalized products make possible an original spatial interpretation, that is probably unprecedented for Palaeolithic societies. Beyond highlighting the diffusion of some concepts over long distances, these rich data allow a deeper analysis of exchange, influence, and non-influence connections. Consequently, the question arises of the identification of what can be called "territories" (spaces built and delimited by human occupations) or "symbolic territories". We define "symbolic territories" as spaces without visible material frontiers, symbolic through the weight of the ideas, the shared social norms and the installation of concepts by the societies in their close environment. We propose to examine this question through the prism of symbolic productions: body ornaments, portable art, rock art. Thousands of pieces of symbolic evidence assigned to the Magdalenian and a better stratigraphic definition for this period offer a favourable context for discussing the social implications of the results of several analyses: the distribution of themes in space, the presence of types of sign or figurative subjects contained in a narrow space, the identification of individual images and collective graphic norms. Comparing these approaches to series from the Middle and Upper Magdalenian, in which the evidence is the most abundant, will allow us to consider the nature of perceptible "territories". Then we will question the relationships between human groups through identity and mechanisms of otherness in Magdalenian societies. (c) 2017 Elsevier Ltd and INQUA. All rights reserved.

An interaction between SLC35A1 and ST3Gal4 is differentially affected by CDG-causing mutations in the SLC35A1 gene

The sialylation of glycoconjugates is performed by a variety of sialyltransferases using CMP-sialic acid (CMP-Sia) as a substrate. Sialylation requires the translocation of CMP-Sia across the Golgi membranes. This function has been assigned to SLC35A1, the only CMP-Sia transporter identified to date. Mutations in the SLC35A1 gene cause a subtype of congenital disorder of glycosylation (CDG). Over the past several years, heterologous complexes formed in the Golgi membrane by some SLC35A subfamily members and functionally related glycosyltransferases have been reported. However, to date no interaction between SLC35A1 and a sialyltransferase has been identified. In this study we attempted to clarify the role of SLC35A1 in α2,3 sialylation of N-glycans. We showed that SLC35A1 associates with ST3Gal4, the main α2,3-sialyltransferase acting on N-glycans. This phenomenon is compromised by the E196K (but not T156R) mutation in the SLC35A1 gene. We also demonstrated that the E196K mutant is less efficient in restoring N-glycan sialylation upon expression in the SLC35A1 knockout cells. On the basis of our findings, we propose that the interaction between SLC35A1 and ST3Gal4 may be important for proper sialylation.

Deep 1D Landmark Representation Learning for Space Target Pose Estimation

Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.

Relational abilities index: A experimental study of the procedure at different trial durations

The relational abilities index (RAI) has been shown to consistently correlate with standardized measures of intellectual aptitude, such as the Wechsler Adult Intelligence Scale. However, the procedure has not been systematically studied and, when used with adults, it has problems in discriminating between medium and high-ability participants. Therefore, the aim of this experiment was to study participants' performance at different levels of analysis and under different trial durations. Sixty-two participants were randomly assigned to one of two groups (30 s or 20 s). We used a version of the RAI involving 55 syllogistic premises (trials), each associated with a YES/NO question. Trials were dived into four blocks, each testing a different relational frame. Results showed that total scores were lower when trials were shortened. However, this reduction was evident only for lower-ability participants. RAI scores in the 20 s group approximated a normal distribution and trial difficulty increased when trial duration decreased. Trial difficulty increased as a function of trial sequence within a block in only half of the blocks. Nodal distance was predictive of trial difficulty. Based on these results, a list of proposed changes to the procedure is provided and discussed.

MSFgNet: A Novel Compact End-to-End Deep Network for Moving Object Detection

Moving object detection (MOD) in videos is a challenging task. Estimation of accurate background is the key to extracting the foreground from video frames. In this paper, we have proposed a novel compact end-to-end convolutional neural network architecture, motion saliency foreground network (MSFgNet), to estimate the background and to extract the foreground from video frames. Initially, the long streaming video is divided into a number of small video streams (SVS). The proposed network takes the SVS as an input and estimates the background frame for each SVS. Second, the saliency map is extracted using the current video frame and estimated background. Furthermore, a compact encoder-decoder network is proposed to extract the foreground from the estimated saliency maps. The performance of the proposed MSFgNet is tested on three benchmark datasets (CDnet-2014, LASIESTA, and PTIS) for MOD. The computational complexity (handling of number of parameters and execution time) and the performance of the proposed MSFgNet are compared with the existing state-of-the-art methods for MOD in terms of precision, recall, and F-measure. Performance analysis shows that the proposed network is very compact and outperforms the existing state-of-the-art methods for MOD in videos.

A Case of Elephantiasic Pretibial Myxedema Successfully Treated With Intralesional Triamcinolone Acetate

Graves' dermopathy is one of the extra-thyroidal manifestations of Graves' disease (GD) and is characterized by the accumulation of glycosaminoglycans in the reticular dermis. In the majority of cases, pretibial myxedema is self-limiting but, in some cases, it can lead to structural and functional damage. Topical steroids with occlusive dressing remain the conventional treatment, but intralesional steroids have shown promising results. We hereby present a case of pretibial myxedema treated successfully with intralesional triamcinolone acetate.

Creatine supplementation enhances immunological function of neutrophils by increasing cellular adenosine triphosphate

Creatine is an organic compound which is utilized in biological activities, especially for adenosine triphosphate (ATP) production in the phosphocreatine system. This is a well-known biochemical reaction that is generally recognized as being mainly driven in specific parts of the body, such as the skeletal muscle and brain. However, our report shows a novel aspect of creatine utilization and ATP synthesis in innate immune cells. Creatine supplementation enhanced immune responses in neutrophils, such as cytokine production, reactive oxygen species (ROS) production, phagocytosis, and NETosis, which were characterized as antibacterial activities. This creatine-induced functional upregulation of neutrophils provided a protective effect in a murine bacterial sepsis model. The mortality rate in mice challenged with Escherichia coli K-12 was decreased by creatine supplementation compared with the control treatment. Corresponding to this decrease in mortality, we found that creatine supplementation decreased blood pro-inflammatory cytokine levels and bacterial colonization in organs. Creatine supplementation significantly increased the cellular ATP level in neutrophils compared with the control treatment. This ATP increase was due to the phosphocreatine system in the creatine-treated neutrophils. In addition, extracellular creatine was used in this ATP synthesis, as inhibition of creatine uptake abolished the increase in ATP in the creatine-treated neutrophils. Thus, creatine is an effective nutrient for modifying the immunological function of neutrophils, which contributes to enhancement of antibacterial immunity.

On the design of an ECOC-Compliant Genetic Algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches. (C) 2013 Elsevier Ltd. All rights reserved.

Robustness Testing of Embedded Software Systems: An Industrial Interview Study

Embedded software is at the core of current and future telecommunication, automotive, multimedia, and industrial automation systems. The success of practically any industrial application depends on the embedded software system's dependability, and one method to verify the dependability of a system is testing its robustness. The motivation behind this paper is to provide a knowledge base of the state of the practice in robustness testing of embedded software systems and to compare this to the state of the art. We have gathered the information on the state of the practice in robustness testing from seven different industrial domains (telecommunication, automotive, multimedia, critical infrastructure, aerospace, consumer products, and banking) by conducting 13 semi-structured interviews. We investigate the different aspects of robustness testing, such as the general view of robustness, relation to requirements engineering and design, test execution, failures, and tools. We highlight knowledge from the state of the practice of robustness testing of embedded software systems. We found different robustness testing practices that have not been previously described. This paper shows that the state of the practice, when it comes to robustness testing, differs between organizations and is quite different from the state of the art described in the scientific literature. For example, methods commonly described in the literature (e.g., the fuzzy approach) are not used in the organizations we studied. Instead, the interviewees described several add hoc approaches that take specific scenarios into account (e.g., power failure or overload). Other differences we found concern the classification of robustness failures, the hypothesized root causes of robustness failures, and the types of tools used for robustness testing. This paper is a firs step in capturing the state of the practice of robustness testing of embedded software systems. The results can be used by both researchers and practitioners. Researchers can use our findings to understand the gap between the state of the art and the state of the practice and develop their studies to fill this gap. Practitioners can also learn from this knowledge base regarding how they can improve their practice and acquire other practices.

Hydrogen Drives Part of the Reverse Krebs Cycle under Metal or Meteorite Catalysis

Hydrogen (H2 ) is a geological source of reducing electrons that is thought to have powered the metabolism of the last universal common ancestor to all extant life, and that is still metabolized by various modern organisms. It has been suggested that H2 drove a geochemical analogue of some or all of the reverse Krebs cycle at the emergence of the metabolic network, catalyzed by metals, but this has yet to be demonstrated experimentally. Herein, we show that three consecutive steps of the reverse Krebs cycle, converting oxaloacetate into succinate, can be driven without enzymes and in one-pot by H2 as the reducing agent under mild conditions compatible with biological chemistry. Low catalytic amounts of nickel (10-20 mol %) or platinum group metals (0.1-1 mol %) or even small amounts of ground meteorites were found to promote the reductive chemistry at temperatures between 5 and 60 °C and over a wide pH range, including pH 7. These results lend additional support to the hypothesis that geologically produced hydrogen and metal catalysts could have initiated early metabolic networks.

Extended Range Electric Vehicle With Driving Behavior Estimation in Energy Management

Battery and energy management methodologies have been proposed to address the design challenges of driving range and battery lifetime in electric vehicles (EVs). However, the driving behavior is a major factor which has been neglected in these methodologies. In this paper, we propose a novel context-aware methodology to estimate the driving behavior in terms of future vehicle speeds and integrate this capability into EV energy management. We implement a driving behavior model using a variation of artificial neural networks called nonlinear autoregressive model with eXogenous inputs (NARX). We train our novel context-aware NARX model based on historical behavior of real drivers, their recent driving reactions, and route average speed retrieved from Google Maps in order to enable driver-specific and self-adaptive driving behavior modeling and long-term estimation. We analyze the estimation error of our methodology and its impact on a battery lifetime-aware automotive climate control, comparing to the state-of-the-art methodologies for various estimation window sizes. Our methodology shows only 12% error for up to 30-s speed prediction which is an improvement of 27% compared to the state-of-the-art. Therefore, the higher accuracy helps the controller to achieve up to 82% of the maximum energy saving and battery lifetime improvement achievable in ideal methodology where the future vehicle speeds are known.

Low-Power Switching Scheme with Quarter Reference Voltage Sources for SAR ADCs

In this paper, an energy-efficient switching scheme with additional quarter-reference voltage sources in a successive approximation register (SAR) analog-to-digital converter (ADC) is proposed for a low power and small area device for frequency modulated continuous wave (FMCVV) radar transceivers. Recently, state-of-the-art ADCs have adopted a configuration that also uses V-ref/2 as the reference voltage of the ADCs to improve the switching energy of capacitive digital-to-analog converter (CDAC). The proposed switching configuration additionally uses V-ref/4 and 3V(ref)/4 reference voltages as the reference voltage of CDAC. Compared to state-of-the-art configurations that use the additional reference voltage of V-ref/2, the average switching energy, and the total capacitance of CDAC in the proposed configuration are reduced by about 87.5% and 50%, respectively. In this switching scheme, the CDAC output voltage gradually converges to V-ref/2, like with conventional SAR ADCs, which minimizes the dynamic offset that deteriorates the linearity of the SAR ADC.

Deblurring Low-Light Images with Light Streaks

Images acquired in low-light conditions with handheld cameras are often blurry, so steady poses and long exposure time are required to alleviate this problem. Although significant advances have been made in image deblurring, state-of-the-art approaches often fail on low-light images, as a sufficient number of salient features cannot be extracted for blur kernel estimation. On the other hand, light streaks are common phenomena in low-light images that have not been extensively explored in existing approaches. In this work, we propose an algorithm that utilizes light streaks to facilitate deblurring low-light images. The light streaks, which commonly exist in the low-light blurry images, contain rich information regarding camera motion and blur kernels. A method is developed in this work to detect light streaks for kernel estimation. We introduce a non-linear blur model that explicitly takes light streaks and corresponding light sources into account, and pose them as constraints for estimating the blur kernel in an optimization framework. For practical applications, the proposed algorithm is extended to handle images undergoing non-uniform blur. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods on deblurring real-world low-light images.

Concerted Uranium Research in Europe (CURE): toward a collaborative project integrating dosimetry, epidemiology and radiobiology to study the effects of occupational uranium exposure

The potential health impacts of chronic exposures to uranium, as they occur in occupational settings, are not well characterized. Most epidemiological studies have been limited by small sample sizes, and a lack of harmonization of methods used to quantify radiation doses resulting from uranium exposure. Experimental studies have shown that uranium has biological effects, but their implications for human health are not clear. New studies that would combine the strengths of large, well-designed epidemiological datasets with those of state-of-the-art biological methods would help improve the characterization of the biological and health effects of occupational uranium exposure. The aim of the European Commission concerted action CURE (Concerted Uranium Research in Europe) was to develop protocols for such a future collaborative research project, in which dosimetry, epidemiology and biology would be integrated to better characterize the effects of occupational uranium exposure. These protocols were developed from existing European cohorts of workers exposed to uranium together with expertise in epidemiology, biology and dosimetry of CURE partner institutions. The preparatory work of CURE should allow a large scale collaborative project to be launched, in order to better characterize the effects of uranium exposure and more generally of alpha particles and low doses of ionizing radiation.

Recovery of Damped Exponentials Using Structured Low Rank Matrix Completion

We introduce a structured low rank matrix completion algorithm to recover a series of images from their under-sampled measurements, where the signal along the parameter dimension at every pixel is described by a linear combination of exponentials. We exploit the exponential behavior of the signal at every pixel, along with the spatial smoothness of the exponential parameters to derive an annihilation relation in the Fourier domain. This relation translates to a low-rank property on a structured matrix constructed from the Fourier samples. We enforce the low-rank property of the structured matrix as a regularization prior to recover the images. Since the direct use of current low rank matrix recovery schemes to this problem is associated with high computational complexity and memory demand, we adopt an iterative re-weighted least squares algorithm, which facilitates the exploitation of the convolutional structure of the matrix. Novel approximations involving 2-D fast Fourier transforms are introduced to drastically reduce the memory demand and computational complexity, which facilitates the extension of structured low-rank methods to large scale 3-D problems. We demonstrate our algorithm in the MR parameter mapping setting and show improvement over the state-of-the-art methods.

NMPC-based controller for vehicle longitudinal and lateral stability enhancement under extreme driving conditions

This paper proposes a real-time NMPC-based controller for four-wheel independent motor-drive electric vehicles to improve vehicle longitudinal and lateral stability under extreme driving conditions. First, considering the interactive and highly coupled longitudinal-lateral vehicle dynamics, a combined-slip tire model is applied to develop the stability controller on low friction coefficient surfaces. Second, the wheel slip ratios and slip angles are selected as the virtual control inputs of the NMPC controller to concurrently achieve three main control objectives: Slip control, lateral stability control, and handling performance improvement. Simultaneously, multiple safety constraints are contained. Then, based on the dynamic relationships between the longitudinal tire force and virtual control inputs, the wheel slip ratios and slip angles obtained from the NMPC controller are converted into additional torques acting directly on each wheel. Finally, the control performance is investigated by co-simulation with MATLAB/Simulink and CarSim, and a hardware-in-the-loop simulation system. The effect of uncertainties on control performance is also verified. The results show that the proposed controller can rapidly solve the optimization problem, and vehicle overall stability are efficiently enhanced under extreme conditions. The robustness of the controller is proved with uncertainties on the road adhesion coefficient and vehicle mass.

Maternal glucocorticoids do not directly mediate the effects of maternal social stress on the fetus

Stress during pregnancy negatively affects the fetus and increases the risk for affective disorders in adulthood. Excess maternal glucocorticoids are thought to mediate fetal programming; however, whether they exert their effects directly or indirectly remains unclear. During pregnancy, protective mechanisms including maternal hypothalamic-pituitary-adrenal (HPA) axis hyporesponsiveness and placental 11β-hydroxysteroid dehydrogenase (11βHSD) type 2, which inactivates glucocorticoids, limit mother-to-fetus glucocorticoid transfer. However, whether repeated stress negatively impacts these mechanisms is not known. Pregnant rats were exposed to repeated social stress on gestational days (GD) 16-20 and several aspects of HPA axis and glucocorticoid regulation, including concentrations of glucocorticoids, gene expression for their receptors (Nr3c1, Nr3c2), receptor chaperones (Fkbp51, Fkbp52) and enzymes that control local glucocorticoid availability (Hsd11b1, Hsd11b2), were investigated in the maternal, placental and fetal compartments on GD20. The maternal HPA axis was activated following stress, though the primary driver was vasopressin, rather than corticotropin-releasing hormone. Despite the stress-induced increase in circulating corticosterone in the dams, only a modest increase was detected in the circulation of female fetuses, with no change in the fetal brain of either sex. Moreover, there was no change in the expression of genes that mediate glucocorticoid actions or modulate local concentrations in the fetal brain. In the placenta labyrinth zone, stress increased Hsd11b2 expression only in males and Fkbp51 expression only in females. Our results indicate that any role glucocorticoids play in fetal programming is likely indirect, perhaps through sex-dependent alterations in placental gene expression, rather than exerting effects via direct crossover into the fetal brain.

See how they fly! Some considerations on symbolic transfers and territories at the end of Upper Palaeolithic

Paleolithic portable art is constitutive of social and cultural identities, as images create a link between territories and men. Therefore, the study of some symbols can lead to a better understanding of the status and the complexity of symbolic territories. In this paper we present the history of diffusion of two symbols from Upper Magdalenian, macrocephalic horses and complex signs coming from a specific site. After a theoretical reflections on the status of symbol, we are considering what criteria should be taken into account for defining each of these symbols and understand how they have diffused - or not - in their immediate or more distant environment. The focus upon designs shareable but that haven't been transferred around suggests new perspectives for thinking the investment of human groups in their symbolic territory, through their artistic production. Therefore, we aim at demonstrating that immobility of images should, as their mobility, be taken in account in the cultural geography of the Upper palaeolithic. (c) 2018 Elsevier Ltd and INQUA. All rights reserved.

Supporting Carers as Patients Move between Hospital and Home: A Systematic Review of Interventions to Support These Transitions in Care

Background: Hospital-to-home transitions become more frequent and complex as people approach end of life. Although carers are critical to enabling these transitions, they report high levels of unmet need. A review of the interventions to assist these care transitions, along with understanding those intervention components and mechanisms that support carers of people with advanced illness, is required to inform an optimal care model for palliative care practice. Aim: To describe the characteristics and reporting quality of intervention studies aimed at improving hospital-to-home transitions for carers of people with advanced illness. Design: This is a systematic review with a narrative synthesis. (international prospective register of systematic reviews [PROSPERO] ID: CRD42020192088). Data Sources: MEDLINE, EMCare, and PsychINFO databases were searched (2000-2021) for prospective studies reporting on interventions that (1) aimed to improve hospital-to-home transitions and (2) targeted carers of people with advanced illness. The Template for Intervention Description and Replication (TIDieR) checklist and constructs of the Care Transition Framework were used to assess the reporting quality of intervention design, delivery, and outcomes. Results: In total, 37 articles were analyzed that included a range of study designs, interventions, and outcomes. Health care utilization (n = 29) and clinical patient-related (n = 21) measures were the most reported outcome. Theoretical discussion was minimal (n = 5) with most studies using efficacy data from past research to justify intervention choice. Conclusion: Carers are critical partners in hospital-to-home transitions at end of life; yet they are largely under-represented in intervention design, delivery, and outcomes. Improving the reporting quality of carer-focused care transition interventions will inform future study design and support translation into practice and policy.

A Continual Learning Survey: Defying Forgetting in Classification Tasks

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern: (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods; and (4) baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time, and storage.

Return to Sport Activities and Risk of Reinjury Following Primary Anterior Cruciate Ligament Reconstruction

This article examines the elements that affect the return to sport (RTS) and the risk and percentages of reinjury following a prior primary anterior cruciate ligament reconstruction (ACLR). The prevalence of RTS following ACLR ranges from 71% to 83%. Concerning elements affecting RTS, a limb symmetry index score of 90 or more duplicates the likelihood of RTS and triplicates it when the International Knee Documentation Committee (IKDC) score is 95 or more, irrespective of age. Other elements recognized to be preindicative of RTS at 1 year include complete rehabilitation, age ≤25, and higher IKDC scores. The prevalence of reinjury following ACLR ranges from 1.5% to 37.5% (between 9% and 29% in the majority of reports). It has been published that 1 in 5 individuals suffers reinjury to either knee, and that male individuals are more prone to reinjure following ACLR. The highest percentage of ACLR reinjury happens in younger male (<18 years), being substantially higher than in female of the same age. Passing a combination of functional tests with predetermined cut-off points utilized as RTS criteria are related diminished ACLR reinjury percentages.

Off-Label Prescribing by Psychiatrists: What is the Practitioner's Liability?

In psychiatry, the molecules available and the dosages recommended when a drug receives marketing authorization are not always adequate to treat patients with major behavioral disturbances. Off-label prescribing is frequent in this context, with regard to the indications and the dosages given as well as to the drug combinations used. However, if complications or death occur, the practitioner's liability may be engaged. The authors report three deaths attributed to off-label prescribing in psychiatry and which led to charges against the physicians. They review the precautions to be taken when prescribing in such conditions (no other possible treatment, existence of sound scientific evidence, consent obtained from the patient, or their legal representatives except in cases of force majeure) and the physician's liability if adverse events occur that could be attributed to off-label prescribing.

Hermann Hoffmann's 1921 Monograph: "The Offspring of Endogenous Psychoses: Genealogical-Characterological Examinations"

Five years after the publication of Rüdin's major sibling study, Hermann Hoffmann, working with Rüdin, performed the first systematic study of the risk for dementia praecox (DP) in offspring of DP probands. Field work was limited to 3 months. Hoffmann ascertained families with at least one parent with certain DP, after Kraepelin, with children the youngest of whom were at least 30 years old. These families contained 103 offspring 30 years or older of whom 7 had definite DP and two possible DP for an estimated risk of 6.8%-8.7%. Hoffmann assessed schizoidia in these children, reporting the quite high risk figure of 47.6%. Hoffmann explored a wide range of two and three locus recessive models in his modest sample. He finds Rüdin's two locus recessive model at the boundary of his results and then reviews three additional more complex models. The simplest is a three-locus recessive model which fits his data better. He also explores an oligogenic three locus model with risk classes of individuals with 1 to 6 risk alleles and an epistatic model where two loci form a di-recessive model for schizoidia, and the third locus is a dominant required for the expression of psychosis. Hoffman questioned whether DP was a "unit-character" appropriate for Mendelian analysis and advocated for a much larger study of offspring. His work should be appreciated in light of his enthusiastic endorsement of Nazi eugenic goals.

A 9.2-g Fully-Flexible Wireless Ambulatory EEG Monitoring and Diagnostics Headband With Analog Motion Artifact Detection and Compensation

An 8-channel wearable wireless device for ambulatory surface EEG monitoring and analysis is presented. The entire multi-channel recording, quantization, and motion artifact removal circuitries are implemented on a 4-layer polyimide flexible substrate. The recording electrodes and active shielding are also integrated on the same substrate, yielding the smallest form factor compared to the state of the art. Thanks to the dry non-contact electrodes, the system is quickly mountable with minimal assistance required, making it an ideal ambulatory front- and temporal-lobe EEG monitoring device. The flexible main board is connected to a rechargeable battery on one end and to a 13 x 17 mm(2) rigid board on the other end. The mini rigid board hosts a low-power programmable FPGA and a BLE 5.0 transceiver, which add diagnostic capability and wireless connectivity features to the device, respectively. Design considerations for a wearable EEG monitoring and diagnostic device are discussed in details. The theory of the novel fully-analog method for motion artifact detection and removal is described and the detailed circuit implementation is presented. The device performance in terms of voltage gain (260 V/V), bandwidth (DC-300 Hz), motion artifact removal, and wireless communication throughput (up to 1 Mbps) is experimentally validated. The entire wearable solution with the battery weighs 9.2 grams.

Prior Guided Feature Enrichment Network for Few-Shot Segmentation

State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation is thus proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses frameworks still face the challenge of generalization ability reduction on unseen classes due to inappropriate use of high-level semantic information of training classes and spatial inconsistency between query and support targets. To alleviate these issues, we propose the Prior Guided Feature Enrichment Network (PFENet). It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks. Extensive experiments on PASCAL-5(i) and COCO prove that the proposed prior generation method and FEM both improve the baseline method significantly. Our PFENet also outperforms state-of-the-art methods by a large margin without efficiency loss. It is surprising that our model even generalizes to cases without labeled support samples.

Redefining Cardiovascular Health to Include Sleep: Prospective Associations With Cardiovascular Disease in the MESA Sleep Study

Background Although sufficient and healthy sleep is inversely associated with cardiovascular disease (CVD) and its risk factors, the American Heart Association's Life's Simple 7 (LS7), as a measure of cardiovascular health (CVH), did not include sleep. We evaluated an expanded measure of CVH that includes sleep as an eighth metric in relation to CVD risk. Methods and Results The analytic sample consisted of MESA (Multi-Ethnic Study of Atherosclerosis) Sleep Study participants who had complete data on sleep characteristics from overnight polysomnography, 7-day wrist actigraphy, validated questionnaires, and the outcome. We computed the LS7 score and 4 iterations of a new CVH score: score 1 included sleep duration, score 2 included sleep characteristics linked to CVD in the literature (sleep duration, insomnia, daytime sleepiness, and obstructive sleep apnea), scores 3 and 4 included sleep characteristics associated with CVD in MESA (score 3: sleep duration and efficiency, daytime sleepiness, and obstructive sleep apnea; score 4: score 3+sleep regularity). Multivariable-adjusted logistic and Cox proportional hazards models evaluated associations of the LS7 and CVH scores 1 to 4 with CVD prevalence and incidence. Among 1920 participants (mean age: 69±9 years; 54% female), there were 95 prevalent CVD events and 93 incident cases (mean follow-up, 4.4 years). Those in the highest versus lowest tertile of the LS7 score and CVH scores 1 to 4 had up to 80% lower odds of prevalent CVD. The LS7 score was not significantly associated with CVD incidence (hazard ratio, 0.62 [95% CI, 0.37-1.04]). Those in the highest versus lowest tertile of CVH score 1, which included sleep duration, and CVH score 4, which included multidimensional sleep health, had 43% and 47% lower incident CVD risk (hazard ratio, 0.57 [95% CI, 0.33-0.97]; and hazard ratio, 0.53 [95% CI, 0.32-0.89]), respectively. Conclusions CVH scores that include sleep health predicted CVD risk in older US adults. The incorporation of sleep as a CVH metric, akin to other health behaviors, may enhance CVD primordial and primary prevention efforts. Findings warrant confirmation in larger cohorts over longer follow-up.

Toward automated discovery of artistic influence

Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works.

Structural and functional investigation of ABC transporter STE6-2p from Pichia pastoris reveals unexpected interaction with sterol molecules

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters are multidomain transmembrane proteins, which facilitate the transport of various substances across cell membranes using energy derived from ATP hydrolysis. They are important drug targets since they mediate decreased drug susceptibility during pharmacological treatments. For the methylotrophic yeast Pichia pastoris, a model organism that is a widely used host for protein expression, the role and function of its ABC transporters is unexplored. In this work, we investigated the Pichia ABC-B transporter STE6-2p. Functional investigations revealed that STE6-2p is capable of transporting rhodamines in vivo and is active in the presence of verapamil and triazoles in vitro. A phylogenetic analysis displays homology among multidrug resistance (MDR) transporters from pathogenic fungi to human ABC-B transporters. Further, we present high-resolution single-particle electron cryomicroscopy structures of an ABC transporter from P. pastoris in the apo conformation (3.1 Å) and in complex with verapamil and adenylyl imidodiphosphate (AMP-PNP) (3.2 Å). An unknown density between transmembrane helices 4, 5, and 6 in both structures suggests the presence of a sterol-binding site of unknown function.

A changing cultural climate: Realising the value of artists working in Antarctica

The ratification of the Antarctic Treaty established a unique construct for human presence and activity in Antarctica. The designation of the continent for peace and science has inspired and informed the work of artists from across the world. This paper explores relationships between the Treaty and contemporary visual artists' responses to Antarctica. Using data from interviews with scientists, cultural professionals and exhibition audiences, I explore the value to science and society of artists' presence in Antarctica. I look at why in the last 2 years the number of artists being supported to work in Antarctica has declined and conclude with some observations on how this downward trend might be addressed.

A Graph-Based Method for Active Outlier Detection With Limited Expert Feedback

Labeled data, particularly for the outlier class, are difficult to obtain. Thus, outlier detection is typically regarded as an unsupervised learning problem. However, it still has an opportunity to obtain few labeled data. For example, a human analyst can give feedback to few data when he/she examines the results of an unsupervised outlier detection method. Moreover, the widely used unsupervised method for outlier detection cannot only take the labeled data into consideration nor use them properly. In this study, we first propose a graph-based method to endow the unsupervised method with the ability to consider few labeled data. Then, we extend our semi-supervised method to active outlier detection by incorporating the query strategy that selects top-ranked outliers. Comprehensive experiments on 12 real-world datasets demonstrate that our semi-supervised outlier detection method is comparable with the best of state-of-the-art approaches, and our active outlier detection method outperforms state-of-the-art methods.

Video resolution enhancement by using discrete and stationary wavelet transforms with illumination compensation

This paper proposes a new video resolution enhancement technique, in which a state-of-the-art illumination compensation procedure is applied to the respective frames before the registration process. After illumination compensation process, the respective frames are registered by using Vandewalle technique. In parallel, the corresponding frame is decomposed into its frequency subbands by using discrete wavelet transform (DWT) and stationary wavelet transform (SWT). Furthermore, the high-frequency subbands (LH, HL, and HH) have been super resolved by using Vandewalle super resolution technique. Afterward, the super resolved high-frequency subbands are being enhanced by the ones obtained through SWT as the latter ones contain more information. The enhanced high-frequency subbands and the output of the registration technique, which is regarded as the low-frequency subband, have been combined by using inverse DWT (IDWT) in order to construct the high-resolution frame. The quantitative (PSNR) results show the superiority of the proposed technique over the conventional and state-of-the-art video resolution enhancement techniques, in which for Akiyo video sequence there are 5.45 dB improvements over the average PSNR compared to Vandewalle registration technique with structure adaptive normalized convolution.

Unintended consequences of heavy air pollution control: efficiency losses, resource misallocation, and firm innovation in China

Disentangling the heterogeneous effects of environmental regulations on firms' productivity and reducing the destructive effects of policies are important for achieving the goal of sustainable development. However, there is insufficient discussion of the micro-mechanisms of regulation. This study takes advantage of the good exogenous impact of China's first action plan, the Atmosphere Ten Articles, which has been formulated and implemented to solve prominent environmental problems, and constructs a difference-in-difference model based on the data of listed companies from 2012 to 2019. Results show that the policy generally has a significantly negative impact on the companies' TFP. This finding proved to be valid after a series of robustness tests, such as replacing the explained variables, eliminating the impact of penecontemporaneous policies, using propensity score matching, and applying instrumental variable methods. Due to the resource allocation mechanism, the policy undermines the companies' TFP, especially those in PM2.5 regulatory areas and state-owned companies. However, the strict emission reduction constraint instead pushes firms to innovate, and as a result, the policy's innovation mechanism promotes firms' productivity, which eventually offsets the negative effects arising from resource allocation distortions.

Development of beryllium-based neutron target system with three-layer structure for accelerator-based neutron source for boron neutron capture therapy

The iBNCT project team with University of Tsukuba is developing an accelerator-based neutron source. Regarding neutron target material, our project has applied beryllium. To deal with large heat load and blistering of the target system, we developed a three-layer structure for the target system that includes a blistering mitigation material between the beryllium used as the neutron generator and the copper heat sink. The three materials were bonded through diffusion bonding using a hot isostatic pressing method. Based on several verifications, our project chose palladium as the intermediate layer. A prototype of the neutron target system was produced. We will verify that sufficient neutrons for BNCT treatment are generated by the device in the near future.

High Dynamic Range Video Synthesis Using Superpixel-Based Illuminance-Invariant Motion Estimation

We propose a robust high dynamic range (HDR) video synthesis algorithm using the superpixel-based illuminance-invariant motion estimation technique. The proposed algorithm first selects an input frame in an alternating exposed input video as the reference. Then, the correspondences between two adjacent frames are estimated by employing a feature descriptor, which is robust against illuminance variation, and a superpixel segmentation technique. Next, the input frames are warped to the reference frame using the estimated motion maps. Finally, the final HDR frame is synthesized by constructing a weight map, which can handle complex motions and poor exposures by considering the underlying structures in the input frames. Experimental results on real test sequences show that the proposed algorithm can provide high-quality HDR videos compared with those obtained by state-of-the-art algorithms in terms of both subjective and objective evaluations.

Alternative antiviral approaches to combat influenza A virus

Influenza A (IAV) is a major human respiratory pathogen that contributes to a significant threat to health security, worldwide. Despite vaccinations and previous immunisations through infections, humans can still be infected with influenza several times throughout their lives. This phenomenon is attributed to the antigenic changes of hemagglutinin (HA) and neuraminidase (NA) proteins in IAV via genetic mutation and reassortment, conferring antigenic drift and antigenic shift, respectively. Numerous findings indicate that slow antigenic drift and reassortment-derived antigenic shift exhibited by IAV are key processes that allow IAVs to overcome the previously acquired host immunity, which eventually leads to the annual re-emergence of seasonal influenza and even pandemic influenza, in rare occasions. As a result, current therapeutic options hit a brick wall quickly. As IAV remains a constant threat for new outbreaks worldwide, the underlying processes of genetic changes and alternative antiviral approaches for IAV should be further explored to improve disease management. In the light of the above, this review discusses the characteristics and mechanisms of mutations and reassortments that contribute to IAV's evolution. We also discuss several alternative RNA-targeting antiviral approaches, namely the CRISPR/Cas13 systems, RNA interference (RNAi), and antisense oligonucleotides (ASO) as potential antiviral approaches against IAV.

The art museum as lab to re-calibrate values towards sustainable development

Innovation and change have become major issues on the agenda of commercial, not-for-profit, government and local public organisations alike and are considered to be essential for our future. More recently companies have begun to recognize that current business models based on an abundance of natural resources are no longer sustainable and as yet unplanned but fundamental social system change is required. The implementation of such radical change requires combined efforts across a range of disciplines and fields of knowledge, including interpretative and expressive forms of cultural communication such as art. Indeed, culture is at the heart of system change because it directly influences individual behaviour. Museums, concert halls, opera houses and others present art as ways to explore how to stay meaningful in a world as both individuals and members of collectives or groups. Given the highly dynamic ICT driven environment and an increasing pluralistic culture at a global level, these institutions have the potential to play a crucial role in preparing for change, introducing new social desires and managing fear. At the same time as this growing recognition of the need for system change, the financial pressures on publicly funded cultural institutions are increasing due to stressed public budgets. A way needs to be found in which private companies and cultural institutions can develop mutually beneficial systems of support in order to promote sustainable social change across the,board. This paper introduces a case study of a public cultural institution exploring its possible role inspiring and facilitating culture change towards sustainable development. It focuses on how an art museum can find ways to productively complement business innovation and sustainability agendas. Since 2004 the Van Abbemuseum, a publicly funded museum for contemporary art in Eindhoven, The Netherlands, has performed a broad variety of experiments in artistic programming and public mediation aiming to deepen and renew its purpose and meaning. The focus of this case study will be on a business model innovation project that was executed in 2014, developing the idea of "art as tool - museum as lab" facilitating sustainable social innovation. It had four steps: (1) Value Proposition, (2) Value Creation, (3) Value Capture and (4) Lead User test. This business approach was deliberately chosen to create a "learning by doing" environment. The Van Abbemuseum staff aimed to develop a new value creation approach, to learn the language and understand the thinking logic to effectively engage with the new target audience: (business) leaders, decision makers and change facilitators. (C) 2016 Elsevier Ltd. All rights reserved.

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we proposea 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each imagemodality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of differentmodalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 x 1 x 1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusionmechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-contextLA-GANsmodel to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Caregivers to older adults require support: A scoping review of their priorities

The vast majority of older adults who are chronically ill rely on informal caregivers for support. Caregivers often require additional support to facilitate their role. To the best of our knowledge, there has yet to be a collation of caregiver-identified priorities for support. Using existing research, this scoping review provides a comprehensive picture of what caregivers have indicated as priorities for support. Arksey and O'Malley's scoping review framework guides this review. We searched MEDLINE, CINAHL and PsycINFO databases on July 2, 2021. We selected databases based on their relevance to nursing, health and social science. Inclusion criteria were peer-reviewed research of any design, a sample population of caregivers to older adults (>55 years), manuscripts published in English and the priorities for caregiver support identified by caregivers themselves. We screened a total of 3591 records, and 33 articles met the inclusion criteria. These studies were from geographic settings across the globe and used various quantitative, qualitative and mixed-method study designs. In our synthesis, we quantified the identified priorities within the studies using coding and content analysis. We present the following list of caregiver-identified priorities: (1) orientation to the caregiving role; (2) self-care and respite; (3) adapting healthcare; (4) improved supports; (5) information needs; (6) access to resources; (7) financial assistance. Policymakers, healthcare professionals and non-profit organisations can use evidence from this review to guide decisions when developing support services and interventions for caregivers.

A CMOS Magnetoresistive Sensor Front-End With Mismatch-Tolerance and Sub-ppm Sensitivity for Magnetic Immunoassays

Magnetic biosensing is an emerging technique for ultra-sensitive point-of-care (PoC) biomolecular detection. However, the large baseline-to-signal ratio and sensor-to-sensor mismatch in magnetoresistive (MR) biosensors severely complicates the design of the analog front-end (AFE) due to the high dynamic range (DR) required. The proposed AFE addresses these issues through new architectural and circuit level techniques including fast settling duty-cycle resistors (DCRs) to reduce readout time and a high frequency interference rejection (HFIR) sampling technique embedded in the ADC to relax the DR requirement. The AFE achieves an input-referred noise of 46.4 nT/root Hz, an input-referred baseline of less than 0.235 mT, and a readout time of 11 ms while consuming just 1.39 mW. Implemented in a 0.18 m CMOS process, this work has state-of-the-art performance with 22.7 faster readout time, > 7.8 lower baseline, and 2.3 lower power than previously reported MR sensor AFEs.

A High-Performance Dual-Topology CMOS Rectifier With 19.5-dB Power Dynamic Range for RF-Based Hybrid Energy Harvesting

This brief reports a dual-topology CMOS rectifier with an extended power dynamic range (PDR) for radio frequency (RF)-based hybrid energy harvesting (RF-HEH) systems. By leveraging both the cross-coupled differential drive (CCDD) and the Dickson topologies with high forward conduction and low reverse leakage, we obtain an extension of the rectifier's PDR by adaptively disabling the CCDD counterpart and enabling the Dickson counterpart to dominate the rectifier's performance during high-power operation. Apart from that, we formulate a rectifier-performance index (RPI), which accounts for the power conversion efficiency (PCE), the PDR, the sensitivity, and the load resistance of the rectifier to provide an adequate performance benchmark with the state-of-the-art rectifiers. Fabricated in a 130-nm CMOS, the proposed dual-topology rectifier measures a wide PDR of 19.5 dB with a peak PCE of 78.4% for a 100-kO load operating at 900 MHz. Besides, our prototype records the highest RPI of 19.2 compared to the recent arts operating at GSM900.

Visual tracking based on edge field with object proposal association

In this paper, we present a novel tracking system based on edge-based object proposal and data association called object proposal association. Our object proposal method accurately detects and localizes objects in an image by searching for object-like regions, with the assumption that an object is represented by a closed boundary. To search for closed boundaries in an image, we present a new Edge Fields (EFs) technique. Using this technique, our method can extract high-quality edges and can obtain accurate boundaries from the image. The EFs technique consists of blurring and thresholding steps, where the former helps extract high-quality edges and the latter prevents the method from losing image details while blurring. After the method extracts object-like regions, we associate the regions in the previous frame with those in the current frame. For this purpose, using the Markov chain Monte Carlo data association (MCMCDA) algorithm, we can find pairs of similar regions across two frames. Experimental results demonstrate that our object proposal method is competitive with state-of-the-art object proposal methods on the PASCAL VOC 2007 dataset. Our tracking method is also competitive with state-of-the-art tracking methods on Object Tracking Benchmark dataset. (C) 2017 Elsevier B.V. All rights reserved.

A Modified Similarity Metric for Unit Testing of Object-Oriented Software Based on Adaptive Random Testing

Finding an effective method for testing object-oriented software (OOS) has proven elusive in the software community due to the rapid development of object-oriented programming (OOP) technology. Although significant progress has been made by previous studies, challenges still exist in relation to the object distance measurement of OOS using Adaptive Random Testing (ART). This is partly due to the unique features of OOS such as encapsulation, inheritance and polymorphism. In a previous work, we proposed a new similarity metric called the Object and Method Invocation Sequence Similarity (OMISS) metric to facilitate multi-class level testing using ART. In this paper, we broaden the set of models in the metric (OMISS) by considering the method parameter and adding the weight in the metric to develop a new distance metric to improve unit testing of OOS. We used the new distance metric to calculate the distance between the set of objects and the distance between the method sequences of the test cases. Additionally, we integrate the new metric in unit testing with ART and applied it to six open source subject programs. The experimental result shows that the proposed method with method parameter considered in this study is better than previous methods without the method parameter in the case of the single method. Our finding further shows that the proposed unit testing approach is a promising direction for assisting software engineers who seek to improve the failure-detection effectiveness of OOS testing.

Cooperative assembly of filopodia by the formin FMNL2 and I-BAR domain protein IRTKS

Filopodia are long finger-like actin-based structures that project out from the plasma membrane as cells navigate and explore their extracellular environment. The initiation of filopodia formation requires release of tension at the plasma membrane followed by the coordinated assembly of long unbranched actin filaments. Filopodia growth is maintained by a tip complex that promotes actin polymerization and protects the growing barbed ends of the actin fibers from capping proteins. Filopodia growth also depends on additional F-actin bundling proteins to stiffen the actin filaments as well as extension of the membrane sheath projecting from the cell periphery. These activities can be provided by a number of actin-binding and membrane-binding proteins including formins such as formin-like 2 (FMNL2) and FMNL3, and Inverse-Bin-Amphiphysin-Rvs (I-BAR) proteins such as IRTKS and IRSp53, but the specific requirement for these proteins in filopodia assembly is not clear. We report here that IRTKS and IRSp53 are FMNL2-binding proteins. Coexpression of FMNL2 with either I-BAR protein promotes cooperative filopodia assembly. We find IRTKS, but not IRSp53, is required for FMNL2-induced filopodia assembly, and FMNL2 and IRTKS are mutually dependent cofactors in this process. Our results suggest that the primary function for FMNL2 during filopodia assembly is binding to the plasma membrane and that regulation of actin dynamics by its formin homology 2 domain is secondary. From these results, we conclude that FMNL2 initiates filopodia assembly via an unexpected novel mechanism, by bending the plasma membrane to recruit IRTKS and thereby nucleate filopodia assembly.

Multiple Roles of Alkanethiolate-Ligands in Direct Formation of H2 O2 over Pd Nanoparticles

Coadsorbed organic species including thiolates can promote direct synthesis of hydrogen peroxide from H2 and O2 over Pd particles. Here, density functional theory based kinetic modeling, augmented with activity measurements and vibrational spectroscopy are used to provide atomistic understanding of direct H2 O2 formation over alkylthiolate(RS) Pd. We find that the RS species are oxidized during reaction conditions yielding RSO2 as the effective ligand. The RSO2 ligand shows superior ability for proton transfer to the intermediate surface species OOH, which accelerates the formation of H2 O2 . The ligands promote the selectivity also by blocking sites for unselective water formation and by modifying the electronic structure of Pd. The work rationalizes observations of enhanced selectivity of direct H2 O2 formation over ligand-funtionalized Pd nanoparticles and shows that engineering of organic surface modifiers can be used to promote desired hydrogen transfer routes.

Residual RNN Models With Pruning for Digital Predistortion of RF Power Amplifiers

The paper presents several novel residual recurrent neural network (RNN) models for digital predistortion of radio-frequency power amplifiers. Then, unstructured and structured neural network (NN) pruning algorithms are proposed to reduce the per-sample computational complexity and the required memory, without compromising their modeling performance. The mathematical equations of the complexity and the memory are derived to analyze the hardware cost and design low cost models. An experimental platform is developed to measure the modeling performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error, error vector magnitude (EVM) and bit error rate. The platform uses an envelope tracking PA excited by 80 MHz IEEE 802.11ac signals. Compared with the state-of-the-art RNN model in (Sun et (a, 2019), the proposed models reduce the per-sample complexity and required memory by 94-99%, they also improve the ACLR and EVM by at least 3 dB and 1 dB at 0.45 dB and 3.15 dB gain compression, respectively. Furthermore, an observation path with time alignment is used to remove the transmit latency in the state-of-the-art approach. At 3.15 dB gain compression, the proposed models outperform the state-of-the-art feedforward and convolutional NN models by at least 1.3 dB in EVM, outperform the general memory polynomial model by at least 3.3 dB in EVM. The above evidences indicate that the proposed RNN models can be implemented with relatively low hardware cost and achieve the best EVM in high power efficiency scenario.

LncRNA XR_351665 Contributes to Chronic Pain-Induced Depression by Upregulating DNMT1 via Sponging miR-152-3p

Chronic pain is frequently comorbid with depression. However, the mechanisms underlying chronic pain-induced depression remain unclear. Here, we found that DNA methyltransferase 1 (DNMT1) was upregulated in the central amygdala (CeA) of spared nerve injury (SNI)-induced chronic pain-depression rats, and knockdown of DNMT1 could improve the depression-like behaviors in SNI rats. Additionally, a panel of differentially expressed lncRNAs, including 38 upregulated and 12 downregulated lncRNAs, were identified by microarray analysis. Bioinformatics analysis suggested that the upregulated lncRNA XR_351665 was the upstream molecule to regulate DNMT1 expression. The knockdown of XR_351665 significantly alleviated the depression-like behaviors in SNI rats, whereas overexpression of XR_351665 induced the depression-like behaviors in naïve rats. Further mechanism-related researches uncovered that XR_351665 functioned as a competing endogenous RNA (ceRNA) to upregulate DNMT1 by competitively sponging miR-152-3p, and subsequently promoted the development of chronic pain-induced depression. Our findings suggest that lncRNA XR_351665 is involved in the development of chronic pain-induced depression by upregulating DNMT1 via sponging miR-152-3p. These data provide novel insight into understanding the pathogenesis of chronic pain-induced depression and identify a potential therapeutic target. PERSPECTIVE: LncRNA XR_351665 in CeA functions as a ceRNA to block the inhibitory effect of miR-152-3p on DNMT1 and contributes to the development of chronic pain-induced depression. These data suggest that manipulation of XR_351665/miR-152-3p/DNMT1 axis may be a potential method to attenuate chronic pain-induced depression.

The role of friction in the seismic risk mitigation of freestanding art objects

The problem of reducing the seismic risk for art objects, that are the objects generally contained within Museums, is of great interest. The first studies were performed in Japan and were successively organized in a general framework by a research program performed at Southern California University and sponsored by the Getty Museum at Malibu, California. In these papers and in the following Italian studies, the theoretical models for the problem concerning vases and statues are based on the dynamic behavior of rigid blocks and have been deeply developed. Unfortunately, because of the great lack of experimental data, determinant parameters for the problem characterization (like the friction between two superimposed blocks or between the art object and the support plane) are often assumed without reference to real values derived from laboratory tests. This paper presents the results of a research program containing the experimental determination of the friction coefficient between the art object and the support (by means of a testing apparatus on purpose realized) together with dynamic tests performed on simple-shaped objects made of different materials. The dynamic tests were performed using an unidirectional shaking table and different supporting surfaces, so that the influence of different friction coefficients has been analyzed.

Hybrid Discriminator With Correlative Autoencoder for Anomaly Detection

Advances in deep neural networks (DNNs) have led to impressive results and in recent years many works have exploited DNNs for anomaly detection. Among others, generative/reconstruction model-based methods have been frequently used for anomaly detection because they do not require any labels for training. The anomaly detection performance of these methods, however, varies a lot, due to the change of the intra-class variance and the difference in complexity of input samples. In addition, most previous state-of-the-art works on anomaly detection have empirically adjusted several hyperparameters to heighten their performance of anomaly detection. These sorts of procedures are known to be impractical and create obstacles in real world anomaly detection. To solve these problems, we propose a hybrid discriminator with a correlative autoencoder for anomaly detection. In the proposed framework, the discriminator implicitly estimates the conditional probability density function and the autoencoder has improved ability to control the reconstruction error. We provide theoretical foundation of our method and verify it through various experiments. We also confirm practical benefits of our interpretation of the conditional expectation and the proposed framework by comparing our results with other state-of-the-art methods.

A review of state-of-the-art and proposal for high frequency inductive step-down DC-DC converter in advanced CMOS

This paper reviews the state-of-the-art of high switching frequency, integrated DC-DC converters and presents the main trade-offs and challenges emerging from this review. Various converter structures (1-phase buck, 2-phase buck, 2-phase coupled buck and 3-level converter) are then discussed and analyzed through simulation from a losses point-of-view. Considering the review, the architecture analysis and the technology model, 4 converters are designed for a given set of specifications: 3.3-1.2 V, 280 mA output current at high switching frequency (100-200 MHz) in 40 nm bulk CMOS. A cascode power stage is used in order to enhance power conversion efficiency, and 1-phase and 2-phase structures are designed. Post-layout simulation results are presented, showing an efficiency above 90 % for a 2-phase converter.

Detecting Curvilinear Features Using Structure Tensors

Few published articles on curvilinear structures exist compared with works on detecting lines or corners with high accuracy. In medical ultrasound imaging, the structures that need to be detected appear as a collection of microstructures correlated along a path. In this paper, we investigated techniques that extract meaningful low-level information for curvilinear structures, using techniques based on structure tensor. We proposed a novel structure tensor enhancement inspired by bilateral filtering. We compared the proposed approach with five state-of-the-art curvilinear structure detectors. We tested the algorithms against simulated images with known ground truth and real images from three different domains (medical ultrasound, scanning electron microscope, and astronomy). For the real images, we employed experts to delineate the ground truth for each domain. Techniques borrowed from machine learning robustly assessed the performance of the methods (area under curve and cross validation). As a practical application, we used the proposed method to label a set of 5000 ultrasound images. We conclude that the proposed tensor-based approach outperforms the state-of-the-art methods in providing magnitude and orientation information for curvilinear structures. The evaluation methodology ensures that the employed feature-detection method will yield reproducible performance on new, unseen images. We published all the implemented methods as open-source software.

Novel approach for tracking interdisciplinary research productivity using institutional databases

This study proposes a new practical approach for tracking institutional changes in research teamwork and productivity using commonly available institutional electronic databases such as eCV and grant management systems. We tested several definitions of interdisciplinary collaborations based on number of collaborations and their fields of discipline. We demonstrated that the extent of interdisciplinary collaboration varies significantly by academic unit, faculty appointment and seniority. Interdisciplinary grants constitute 24% of all grants but the trend has significantly increased over the last five years. Departments with more interdisciplinary grants receive more research funding. More research is needed to improve efficiency of interdisciplinary collaborations.

A Single-Center Experience of Internal Pancreatic Fistulas

Background Internal pancreatic fistula (IPF) is a complex disease with different etiologies, varied clinical presentations, and multiple management options. Unlike postoperative pancreatic fistula, IPF lacks guidelines for classification and management. The rarity of the disease makes randomized control studies unlikely and difficult to formulate guidelines. This has resulted in different approaches to managing IPF. IPF associated with both acute and chronic pancreatitis is treated with a step-up approach. Chronic pancreatitis-associated IPF treated with the traditional step-up approach is associated with increased morbidity. Prolonged fasting, drainage of protein-rich pancreatic fluid, and extended hospital stay add to the morbidity. Early surgical intervention in patients with IPF associated with chronic pancreatitis can treat both the fistula and underlying disease processes simultaneously. This may contribute to reduced morbidity and hospital stay. Methodology A retrospective observational study was conducted between June 2018 and May 2019. IPF patients with fluid amylase >1,000 IU/L and fluid albumin >3 g/dL were included in the study. Results In total, 32 patients were included in the study. A total of 13 patients had acute pancreatitis and 19 were associated with chronic pancreatitis. Pseudocyst and walled-off pancreatic necrosis were present in 18 patients. The duration of treatment for the traditional group was 8-14 weeks, and for the early surgery group, it was 8-10 days. Patients were followed up for two years, and none of the patients in the early surgery group had a recurrence. Conclusions The overall mortality of IPF is low but it has high morbidity. The delay in treatment may contribute to high morbidity; hence, early surgical intervention may change the clinical course. The primary pathology of the pancreas can be addressed simultaneously as well. In our study, early surgical intervention was associated with lesser morbidity and decreased duration of hospital stay while recurrence rates and mortality were comparable to the traditional management protocol.

Pseudomonas aeruginosa Quorum Sensing

Pseudomonas aeruginosa, like many bacteria, uses chemical signals to communicate between cells in a process called quorum sensing (QS). QS allows groups of bacteria to sense population density and, in response to changing cell densities, to coordinate behaviors. The P. aeruginosa QS system consists of two complete circuits that involve acyl-homoserine lactone signals and a third system that uses quinolone signals. Together, these three QS circuits regulate the expression of hundreds of genes, many of which code for virulence factors. P. aeruginosa has become a model for studying the molecular biology of QS and the ecology and evolution of group behaviors in bacteria. In this chapter, we recount the history of discovery of QS systems in P. aeruginosa, discuss how QS relates to virulence and the ecology of this bacterium, and explore strategies to inhibit QS. Finally, we discuss future directions for research in P. aeruginosa QS.

When Van Gogh meets Mandelbrot: Multifractal classification of painting's texture

Recently, a growing interest has emerged for examining the potential of Image Processing tools to assist Art Investigation. Simultaneously, several research works showed the interest of using multifractal analysis for the description of homogeneous textures in images. In this context, the goal of the present contribution is to study the benefits of using the wavelet leader based multifractal formalism to characterize paintings. After a brief review of the underlying key theoretical concepts, methods and tools, two sets of digitized paintings are analyzed. The first one, the Princeton Experiment, consists of a set of seven paintings and their replicas, made by the same artist. It enables examination of the potential of multifractal analysis in forgery detection. The second one is composed of paintings by Van Gogh and contemporaries, made available by the Van Gogh and Kroller-Muller Museums (Netherlands) in the framework of the Image processing for Art Investigation research program. It enables us to show various differences in the regularity of textures of Van Gogh's paintings from different periods, or between Van Gogh's and contemporaries' paintings. These preliminary results plead for the constitution of interdisciplinary research teams consisting of experts in art, image processing, mathematics and computer sciences. (C) 2012 Published by Elsevier B.V.

PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

Pansharpening is a typical image fusion problem, which aims to produce ahigh resolution multispectral(HRMS) image by integrating a high spatial resolutionpanchromatic(PAN) image with a low spatial resolutionmultispectral(MS) image. Prior arts have used eithercomponent substitution(CS)-based methods ormultiresolution analysis(MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose apansharpening weight network(PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainableneural network(NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the deficiency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.

"A whole new perspective on how the body fits together"-An evaluation of a cadaver laboratory experience for high school students

The Center for Anatomy and Physiology Education has hosted interactive human cadaver laboratory tours for local high schools (ages 14-18) and undergraduate university students since 2014 to expose students to healthcare careers. Students receive information on the history of body donation and healthcare careers and observe human anatomy on prosections and with isolated organs. The goal of this study was to evaluate students' perceptions of the anatomy laboratory tours and their impact on students' interests in healthcare careers. Students completed pre- and post-tour questionnaires. Responses were analyzed using thematic analysis and linguistic inquiry. Of the 261 students who completed pre-tour questionnaires, 204 (78%) completed the post-tour questionnaire. Before the tour, students anticipated learning about human anatomy and expected to only see but not touch a cadaver. Most students expressed excitement and/or nervousness. A few students viewed the laboratory tour as an opportunity to test if they could see themselves in a healthcare career. After the tour, most students indicated that the tour either met or exceeded their expectations. Students found the laboratory tour to be educational and interesting and were surprised by the opportunity to interact with the donor. Numerous students expressed an increased interest in healthcare careers after the tour. Overall, students perceived the tour as an engaging experience that improved their anatomical knowledge and reinforced/increased their interest in healthcare careers. Academic institutions can positively impact local students by implementing an anatomy tour, sharing access to their in-house human cadaver laboratory, and recruiting instructors to share their anatomy expertise.

Bilateral Parainfectious Optic Neuritis in Young Patient

Parainfectious optic neuritis arises from infectious aetiology either from pathogen direct invasion or after an infectious disease which can be immunologically mediated demyelination of optic nerve or, from inflammation of optic disc vasculature. We report a case of bilateral optic neuritis in a young patient. A 13-year-old boy presented with painless profound vision loss in both eyes preceded by an episode of fever two weeks prior. Visual acuity in both eyes was a perception of light. Fundoscopy showed a bilateral hyperemic swollen disc. Blood investigations were normal except for C-reactive protein and ESR was elevated. CSF analysis was also normal with no growth of micro-organisms. Both CT scans and MRIs of the brain and orbit showed normal findings. The patient was diagnosed to have parainfectious optic neuritis. He was started on intravenous methylprednisolone for five days followed by a tapering dose of oral prednisolone for a total of one month. His final visual acuity improved to 6/6 in both eyes with a normal optic disc appearance.

Optimization of CMOS-integrated LC oscillators using the genetic algorithm

This paper reports on state-of-the-art power-frequency-normalized phase noise of 21 dB, obtained from a CMOS-integrated LC oscillator with a measured phase noise of -112 dBc/Hz at 100 kHz from the 5.3-GHz center frequency and with power consumption of 15 mW. The excellent performance (if the oscillator is attributed to the careful selection of the oscillator-circuit parameters, which determined using the genetic algorithm. (C) 2004 Wiley Periodicals, Inc.

FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations

Learning low-bitwidth convolutional neural networks (CNNs) is challenging because performance may drop significantly after quantization. Prior arts often quantize the network weights by carefully tuning hyperparameters such as nonuniform stepsize and layerwise bitwidths, which are complicated since the full-and low-precision representations have large discrepancies. This work presents a novel quantization pipeline, named frequency-aware transformation (FAT), that features important benefits: 1) instead of designing complicated quantizers, FAT learns to transform network weights in the frequency domain to remove redundant information before quantization, making them amenable to training in low bitwidth with simple quantizers; 2) FAT readily embeds CNNs in low bitwidths using standard quantizers without tedious hyperparameter tuning and theoretical analyses show that FAT minimizes the quantization errors in both uniform and nonuniform quantizations; and 3) FAT can be easily plugged into various CNN architectures. Using FAT with a simple uniform/logarithmic quantizer can achieve the state-of-the-art performance in different bitwidths on various model architectures. Consequently, FAT serves to provide a novel frequency-based perspective for model quantization.

DLTTA: Dynamic Learning Rate for Test-Time Adaptation on Cross-Domain Medical Images

Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions. Previous TTA methods have a common limitation of using a fixed learning rate for all the test samples. Such a practice would be sub-optimal for TTA, because test data may arrive sequentially therefore the scale of distribution shift would change frequently. To address this problem, we propose a novel dynamic learning rate adjustment method for test-time adaptation, called DLTTA, which dynamically modulates the amount of weights update for each test image to account for the differences in their distribution shift. Specifically, our DLTTA is equipped with a memory bank based estimation scheme to effectively measure the discrepancy of a given test sample. Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample. The effectiveness and general applicability of our DLTTA is extensively demonstrated on three tasks including retinal optical coherence tomography (OCT) segmentation, histopathological image classification, and prostate 3D MRI segmentation. Our method achieves effective and fast test-time adaptation with consistent performance improvement over current state-of-the-art test-time adaptation methods. Code is available at https://github.com/med-air/DLTTA.

Somatostatin-Positive Neurons in the Rostral Zona Incerta Modulate Innate Fear-Induced Defensive Response in Mice

Defensive behaviors induced by innate fear or Pavlovian fear conditioning are crucial for animals to avoid threats and ensure survival. The zona incerta (ZI) has been demonstrated to play important roles in fear learning and fear memory, as well as modulating auditory-induced innate defensive behavior. However, whether the neuronal subtypes in the ZI and specific circuits can mediate the innate fear response is largely unknown. Here, we found that somatostatin (SST)-positive neurons in the rostral ZI of mice were activated by a visual innate fear stimulus. Optogenetic inhibition of SST-positive neurons in the rostral ZI resulted in reduced flight responses to an overhead looming stimulus. Optogenetic activation of SST-positive neurons in the rostral ZI induced fear-like defensive behavior including increased immobility and bradycardia. In addition, we demonstrated that manipulation of the GABAergic projections from SST-positive neurons in the rostral ZI to the downstream nucleus reuniens (Re) mediated fear-like defensive behavior. Retrograde trans-synaptic tracing also revealed looming stimulus-activated neurons in the superior colliculus (SC) that projected to the Re-projecting SST-positive neurons in the rostral ZI (SC-ZIrSST-Re pathway). Together, our study elucidates the function of SST-positive neurons in the rostral ZI and the SC-ZIrSST-Re tri-synaptic circuit in mediating the innate fear response.

Current State-of-the-Art and New Directions in Strategic Environmental Noise Mapping

Environmental noise mapping has the potential to act as a powerful resource for policymakers as a decision support tool for the mitigation of the negative effects of environmental noise pollution and its impact on public health. The aim of this paper is to review current state-of-the-art developments in how the strategic noise mapping (SNM) process has progressed at the EU level since the introduction of the Environmental Noise Directive (END) in 2002. Reviewing such developments is important because of the relevance of SNM to public health. In this regard, the development of a new standardized noise calculation method (i.e. CNOSSOS-EU) is also considered, as well as the future potential for noise mapping and the impact of technology on the development of noise pollution assessment.

Elections have (health) consequences: Depression, anxiety, and the 2020 presidential election

In this paper, we examine the effect of the 2020 presidential election on anxiety and depression among Americans. We use data from the 2020 Household Pulse Survey (HPS), a nationally representative rapid response survey conducted weekly from April to July of 2020 and then bi-weekly until December of 2021. The high-frequency nature of the survey implies that we can identify week-to-week changes in mental health outcomes. We find that self-reported symptoms of moderate to severe anxiety and depression increased steadily up to the presidential election and declined after the election. The anxiety and depression levels are significantly higher around the 2020 election than in April 2020, when most of the U.S. was under mandatory or advisory stay-at-home orders due to the COVID-19 pandemic. Furthermore, anxiety and depression-specific office visits and usage of mental-health-specific prescription drugs show similar patterns. Robustness checks rule out alternative explanations such as a COVID-19 surge or vaccine development.

Robust Object Tracking Using Affine Transformation and Convolutional Features

The state-of-the-art trackers using deep learning technology have no special strategy to capture the geometric deformation of the target. Based on that the affine manifold can better capture the target shape change and that the higher level of Convolutional Neural Network (CNN) can better describe semantic information of objects, we propose a new tracking algorithm combining affine transformation with convolutional features to track targets with dramatic deformation. First, the affine transformation is applied to predict possible locations of a target, then a correlative filter is designed to compute the appearance confidence score for determining the final target location. Furthermore, a standard discriminative correlation filter is used to develop the effect of convolutional features, which is more efficient than other methods used for CNN Networks. Comprehensive experiments demonstrate the outstanding performance of our tracking algorithm compared to the state-of-the-art techniques in the public benchmarks.

Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks

Human pose estimation in images is challenging and important for many computer vision applications. Large improvements in human pose estimation have been achieved with the development of convolutional neural networks. Even though, when encountered some difficult cases even the state-of-the-art models may fail to predict all the body joints correctly. Some recent works try to refine the pose estimator. GAN (Generative Adversarial Networks) has been proved to be efficient to improve human pose estimation. However, GAN can only learn local body joints structural constrains. In this paper, we propose to apply Self-Attention GAN to further improve the performance of human pose estimation. With attention mechanism in the framework of GAN, we can learn long-range body joints dependencies, therefore enforce the entire body joints structural constrains to make all the body joints to be consistent. Our method outperforms other state-of-the-art methods on two standard benchmark datasets MPII and LSP for human pose estimation.

LSTM Fully Convolutional Networks for Time Series Classification

Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our proposed models significantly enhance the performance of fully convolutional networks with a nominal increase in model size and require minimal preprocessing of the data set. The proposed long short term memory fully convolutional network (LSTM-FCN) achieves the state-of-the-art performance compared with others. We also explore the usage of attention mechanism to improve time series classification with the attention long short term memory fully convolutional network (ALSTM-FCN). The attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose refinement as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared with other techniques.

Flavonol-Aluminum Complex Formation: Enhancing Aluminum Accumulation in Tea Plants

Polyphenol-rich tea plants are aluminum (Al) accumulators. Whether an association exists between polyphenols and Al accumulation in tea plants remains unclear. This study revealed that the accumulation of the total Al and bound Al contents were both higher in tea samples with high flavonol content than in low, and Al accumulation in tea plants was significantly and positively correlated with their flavonol content. Furthermore, the capability of flavonols combined with Al was higher than that of epigallocatechin gallate (EGCG) and root proanthocyanidins (PAs) under identical conditions. Flavonol-Al complexes signals (94 ppm) were detected in the tender roots and old leaves of tea plants through solid-state 27Al nuclear magnetic resonance (NMR) imaging, and the strength of the signals in the high flavonol content tea samples was considerably stronger than that in the low flavonol content tea samples. This study provides a new perspective for studying Al accumulation in different tea varieties.

Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification

Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.

Tunable lasers in optical networks

Tunable lasers have been the subject of considerable interest ever since the start of the wavelength division multiplexing (WDM) revolution. In this paper, we bring together views on tunable lasers from different types of companies in the value chain, from operators to laser manufacturers. The purpose of the paper is to give an overview of the state of the art in both deployment and development, as well as to try to predict trends over the coming years.

Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression

The number of images produced each day increased significantly. The ability to detect and correct an image's orientation can provide several advantages in computer vision. This paper presents a new framework based on a transfer learning technique for automatically detecting image orientation. To implement the power of deep neural networks, we applied a convolutional neural network model pre-trained on the ImageNet database for feature extraction. Then, we built a multi-class logistic regression classifier to detect the four image orientation probabilities corresponding to the following orientations (0 for no orientation, 90, 180, and 270). We tested our model on the SUN-397 dataset, one of the most extensive data sets currently used for image-orientation detection tasks. We conducted a cross-dataset evaluation for in-depth testing and analysis. We also examined our model using different old and recent state-of-the-art convolutional neural network (CNN) baselines. We demonstrate that our model yields promising results based on transfer learning for feature extraction combined with a one-vs-rest logistic regression classifier. Our proposed model surpassed the state-of-the-art results in terms of accuracy and performance.

Hybrid global gridded snow products and conceptual simulations of distributed snow budget: evaluation of different scenarios in a mountainous watershed

Considering snowmelt in mountainous areas as the important source of streamflow, the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes. The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models. To tackle these challenges, Global Gridded Snow Products (GGSPs) are introduced, which effectively simplify the identification of the spatial characteristics of snow hydrological variables. This research aims to investigate the performance of multi-source GGSPs using multi-stage calibration strategies in hydrological modeling. The used GGSPs were Snow-Covered Area (SCA) and Snow Water Equivalent (SWE), implemented individually or jointly to calibrate an appropriate water balance model. The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow. The results showed that using GGSPs as complementary information in the calibration process, besides streamflow time series, could improve the modeling accuracy compared to the conventional calibration, which is only based on streamflow data. The SCA with NSE, KGE, and RMSE values varying within the ranges of 0.47-0.57, 0.54-0.65, and 4-6.88, respectively, outperformed the SWE with the corresponding metrics of 0.36-0.59, 0.47-0.60, and 5.22-7.46, respectively, in simulating the total streamflow of the watershed. In addition to the superiority of the SCA over SWE, the two-stage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy. On the other hand, the consistent contribution of snowmelt to the total generated streamflow (ranging from 0.9 to 1.47) and the ratio of snow melting to snowfall (ranging from 0.925 to 1.041) in different calibration strategies and models resulted in a reliable simulation of the model.

Integrated Circuits for Medical Ultrasound Applications: Imaging and Beyond

Medical ultrasound has become a crucial part of modern society and continues to play a vital role in the diagnosis and treatment of illnesses. Over the past decades, the development of medical ultrasound has seen extraordinary progress as a result of the tremendous research advances in microelectronics, transducer technology and signal processing algorithms. However, medical ultrasound still faces many challenges including power-efficient driving of transducers, low-noise recording of ultrasound echoes, effective beamforming in a non-linear, high-attenuation medium (human tissues) and reduced overall form factor. This paper provides a comprehensive review of the design of integrated circuits for medical ultrasound applications. The most important and ubiquitous modules in a medical ultrasound system are addressed, i) transducer driving circuit, ii) low-noise amplifier, iii) beamforming circuit and iv) analog-digital converter. Within each ultrasound module, some representative research highlights are described followed by a comparison of the state-of-the-art. This paper concludes with a discussion and recommendations for future research directions.

Deep learning for rare disease: A scoping review

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of each rare disease causes formidable challenges in accurately diagnosing and caring for these patients and engaging participants in research to advance treatments. Deep learning has advanced many scientific fields and has been applied to many healthcare tasks. This study reviewed the current uses of deep learning to advance rare disease research. Among the 332 reviewed articles, we found that deep learning has been actively used for rare neoplastic diseases (250/332), followed by rare genetic diseases (170/332) and rare neurological diseases (127/332). Convolutional neural networks (307/332) were the most frequently used deep learning architecture, presumably because image data were the most commonly available data type in rare disease research. Diagnosis is the main focus of rare disease research using deep learning (263/332). We summarized the challenges and future research directions for leveraging deep learning to advance rare disease research.

The Affective Domain-A Program to Foster Social-Emotional Orientation in Novice Physical Education Teachers

The present study aimed to assess the influence of an emotional-based program for novice physical education teachers on their perception of the affective domain in teaching, and the influence of the program on their social-emotional orientation. Thirty-two physical educators in their induction year participated. Instrumentations included reflective assignments: individual tasks, a group artwork task, short videos containing student-teacher scenarios, and summary reflections. The study covered tasks that contained a variety of emotional expressions-verbalizing, acting, and art creation. Content analysis was conducted for each of the assignments. The results indicate that the participants felt that they gradually developed an awareness of the role of emotions in their practice. In addition to personal gain, they felt that their empathy for others-especially their students-was enhanced. These results highlight the important influence that an emotional-based program has on physical educators' social-emotional orientation.

Exploring Advertising Effectiveness of Tourist Hotels' Marketing Images Containing Nature and Performing Arts: An Eye-Tracking Analysis

The beautiful, natural environment in a tourist hotel's marketing images can evoke relaxing and soothing emotions. However, can tourist hotels use nature as a servicescape to make their performing arts services more attractive? Based on attention restoration and servicescape theory, this study explores and compares the influence of tourist hotels' performing arts images with nature- or built-based servicescapes on the advertising effectiveness (i.e., customer visual attention and behavioral intention). To analyze visual attention on the marketing images, this study uses eye-tracking technology to record customer visual trajectories. This experiment used a total of 113 participants. The sample size of the nature-based servicescape group was 59 (age with mean = 39.04), and that of the built-based servicescape group was 54 (age with mean = 40.17). A tourist hotel's (Volando Urai Spring Spa & Resort) marketing images were chosen as stimuli. All participants were randomly assigned to the nature-based or the built-based servicescape group. In each experimental group, all the images were randomly presented to reduce any order effects of the images. By using eye-tracking analysis, the experimental findings were as follows: (1) A nature-based servicescape can arouse more visual attention of customers than a built-based servicescape can; (2) Marketing images with performing arts activities in nature-based servicescapes attract the visual attention of customers; (3) Nature-based servicescapes stimulate higher behavioral intentions of consumers than built-based servicescape.

Reversible Immunosensor for the Continuous Monitoring of Cortisol in Blood Plasma Sampled with Microdialysis

Cortisol is a steroid hormone involved in a wide range of medical conditions. The level of the hormone fluctuates over time, but with traditional laboratory-based assays, such dynamics cannot be monitored in real time. Here, a reversible cortisol sensor is reported that allows continuous monitoring of cortisol in blood plasma using sampling by microdialysis. The sensor is based on measuring single-molecule binding and unbinding events of tethered particles. The particles are functionalized with antibodies and the substrate with cortisol-analogues, causing binding and unbinding events to occur between particles and substrate. The frequency of binding events is reduced when cortisol is present in the solution as it blocks the binding sites of the antibodies. The sensor responds to cortisol in the high nanomolar to low micromolar range and can monitor cortisol concentrations over multiple hours. Results are shown for cortisol monitoring in filtered and in microdialysis-sampled human blood plasma.

NFκB- and AP-1-mediated DNA looping regulates matrix metalloproteinase-9 transcription in TNF-α-treated human leukemia U937 cells

The aim of this study is to explore the spatial association of critical genomic elements in the effect of TNF-α on matrix metalloproteinase-9 (MMP-9) expression in human leukemia U937 cells. TNF-α up-regulated MMP-9 protein expression and mRNA level in U937 cells, and Akt-mediated-NFκB/p65 activation and JNK-mediated c-Jun activation were proven to be involved in TNF-α-induced MMP-9 up-regulation. Promoter luciferase activity assay revealed that NFκB (nt-600) and AP-1 (nt-79) binding sites were crucial for TNF-α-induced transcription of MMP-9 gene. The results of a chromatin immunoprecipitation assay indicated that TNF-α reduced histone deacetylase-1 (HDAC-1) recruitment but increased p300 (a histone acetyltransferase) recruitment to MMP-9 promoter regions surrounding NFκB and AP-1 binding sites. Consistently, TNF-α increased enrichment of the acetylated histone H3 mark on MMP-9 promoter regions. DNA affinity purification assay revealed that p300 and HDAC1 could bind oligonucleotides containing AP-1/c-Jun and NFκB/p65 binding sites. Chromosome conformation capture assay showed that TNF-α stimulated chromosomal loops in the MMP-9 promoter via NFκB/p65 and AP-1/c-Jun. The p300-associated acetyltransferase activity was crucial for p65/c-Jun-mediated DNA looping, and inhibition of HDAC activity increased the level of DNA looping. Reduction in the level of DNA looping eliminated all TNF-α-stimulated MMP-9 up-regulation. Taken together, our data suggest that p65/c-Jun-mediated DNA looping is involved in TNF-α-induced MMP-9 up-regulation and that the recruitment of p300 or HDAC1 to NFκB and AP-1 binding sites modifies the level of DNA looping.

T-2 Toxin Caused Mice Testicular Inflammation Injury via ROS-Mediated NLRP3 Inflammasome Activation

T-2 toxin treatment causes male reproduction system dysfunction, although the exact mechanism remains unclear. In this research, male Kunming mice and TM4 cells were treated with varying concentrations of the T-2 toxin for evaluating the adverse effect of T-2 toxin on male reproductive function. MCC950 or NAC was used to block NLRP3 inflammasome activation and eliminate reactive oxygen species (ROS) accumulation in the TM4 cell, respectively. The results showed that: (1) T-2 toxin caused testicular atrophy, destroyed the microstructure and ultrastructure of the testis, and caused sperm deformities; (2) T-2 toxin increased the content and gene expressions of TNF-α and IL-6 and decreased the IL-10 content and gene expression, causing testis and TM4 cell inflammatory injury; (3) T-2 toxin activated NLRP3 inflammasome in the testis and TM4 cells and caused ROS accumulation in the testis; (4) suppressing NLRP3 inflammasome activation using 20 nM MCC950 alleviated the TM4 cell inflammatory damage caused via the T-2 toxin; nevertheless, 20 nM MCC950 did not reduce ROS accumulation in TM4 cells; and (5) NAC relieved the inflammatory damage in TM4 cells by inhibiting NLRP3 inflammasome activation. Taken together, T-2 toxin caused testicular inflammation injury through ROS-mediated NLRP3 inflammasome activation, resulting in male reproductive dysfunction.

A Machine Learning Approach to Software Requirements Prioritization

Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization method called Case-Based Ranking (CBRank), which combines project's stakeholders preferences with requirements ordering approximations computed through machine learning techniques, bringing promising advantages. First, the human effort to input preference information can be reduced, while preserving the accuracy of the final ranking estimates. Second, domain knowledge encoded as partial order relations defined over the requirement attributes can be exploited, thus supporting an adaptive elicitation process. The techniques CBRank rests on and the associated prioritization process are detailed. Empirical evaluations of properties of CBRank are performed on simulated data and compared with a state-of-the-art prioritization method, providing evidence of the method ability to support the management of the tradeoff between elicitation effort and ranking accuracy and to exploit domain knowledge. A case study on a real software project complements these experimental measurements. Finally, a positioning of CBRank with respect to state-of-the-art requirements prioritization methods is proposed, together with a discussion of benefits and limits of the method.

Multidisciplinary Approach Towards Hypertensive and Chronic Alcoholic Patient With Intracerebral Bleed

Intracerebral haemorrhage, the most lethal form of stroke, accounts for almost a third of all strokes. The brain receives and expels blood through blood arteries. Veins or arteries may rupture due to trauma, improper development, or excessive pressure. Blood itself has the potential to harm brain tissue. Here, we discuss the case of a 36-year-old individual who experienced giddiness, two to three seizure episodes, and left extremity weakness. Investigation revealed an intracerebral bleed. Physiotherapy was necessary to enable the patient to carry out his everyday activities comfortably in addition to medical management. The patient's condition was improved with the help of a physiotherapy protocol.