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Medical_papers_title_and_abstract_NLP_dataset

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Photophysical and Fluorescence Nitroaromatic Sensing Properties of Methylated Derivative of a Pamoic Acid Ester

Rapid and selective detection of nitroaromatic explosives is very important for public safety, life, and environmental health. Current instrumental techniques suffer from high cost and poor site used. In order to investigate fluorescence sensing of nitroaromatics, we prepare a new small fluorescence probe derived from pamoic acid. This study covers the synthesis of Pamoic acid based [diisopropyl 4,4'-methylenebis(3-methoxy-2-naphthoate)] (2) material and characterization of its structure. The methylation of Pamoic acid ester, which we have successfully synthesized in our previous studies, was carried out in this study. Determination of the photophysical and fluorescent nitroaromatic detection properties of the compound forms the basis of the study. Structural characterization of the synthesized compound [diisopropyl 4,4'-methylenebis(3-methoxy-2-naphthoate)] (2) was characterized using spectroscopic methods. In addition, Molecular structure of the synthesized compound was determined by single crystal X-ray diffraction studies. In the final step, compounds [diisopropyl 4,4'-methylenebis(3-hydroxy-2-naphthoate)] (1) and [diisopropyl 4,4'-methylenebis(3-methoxy-2-naphthoate)] (2) were tested as fluorescent probes for the detection of some nitroaromatic explosives. It is seen that Nitrobenzene provides the best quenching effect on the compound [diisopropyl 4,4'-methylenebis(3-hydroxy-2-naphthoate)] (1) containing the -OH group, with lowest the limit of detection (LOD) value. It was observed that Picric acid provided the best quenching effect with lowest the limit of detection (LOD) value in the compound [diisopropyl 4,4'-methylenebis(3-methoxy-2-naphthoate)] (2) obtained by methylation of the -OH group in the compound [diisopropyl 4,4'-methylenebis(3-hydroxy-2-naphthoate)] (1).

Surgery of extrahepatic bile duct cancer - current evidence and recommendations

Extrahepatic cholangiocarcinoma is a rare disease with poor prognosis, requiring comprehensive multidisciplinary management. Only radical resection gives hope for long-term survival. Most patients have either an unresectable disease or their condition does not allow for radical surgery. The method of choice for resectable perihilar cholangiocarcinoma is en-block liver resection including the extrahepatic bile duct, or pancreatoduodenectomy for distal cholangiocarcinoma. Hepatopancreatoduodenectomy can be performed in selected patients if the entire hepato-choledochus is affected. Liver transplantation after neoadjuvant treatment can be also considered in highly selected patients with unresectable perihilar cholangiocarcinoma. These procedures are technically demanding, are associated with high morbidity and relevant mortality, and must be concentrated in tertiary hepatobiliary centers. Preoperative optimization (bile duct drainage, nutrition, portal venous embolization, etc.) plays a crucial role in reducing postoperative morbidity and mortality.

Securing a wireless world

Securing wireless networks poses unique research challenges. In this paper, we survey the state-of-the-art approaches to providing security for three popular wireless networking paradigms, namely, IEEE 802.11 based WLANs, third-gene ration cellular networks, and mobile ad hoe networks. We identify the security threats as well as examine the current solutions. We further summarize lessons learned, discuss open issues, and identify future research directions.

Feature Fusion of Deep Spatial Features and Handcrafted Spatiotemporal Features for Human Action Recognition

Human action recognition plays a significant part in the research community due to its emerging applications. A variety of approaches have been proposed to resolve this problem, however, several issues still need to be addressed. In action recognition, effectively extracting and aggregating the spatial-temporal information plays a vital role to describe a video. In this research, we propose a novel approach to recognize human actions by considering both deep spatial features and handcrafted spatiotemporal features. Firstly, we extract the deep spatial features by employing a state-of-the-art deep convolutional network, namely Inception-Resnet-v2. Secondly, we introduce a novel handcrafted feature descriptor, namely Weber's law based Volume Local Gradient Ternary Pattern (WVLGTP), which brings out the spatiotemporal features. It also considers the shape information by using gradient operation. Furthermore, Weber's law based threshold value and the ternary pattern based on an adaptive local threshold is presented to effectively handle the noisy center pixel value. Besides, a multi-resolution approach for WVLGTP based on an averaging scheme is also presented. Afterward, both these extracted features are concatenated and feed to the Support Vector Machine to perform the classification. Lastly, the extensive experimental analysis shows that our proposed method outperforms state-of-the-art approaches in terms of accuracy.

Self-Training for Class-Incremental Semantic Segmentation

In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods.

X-Ray Scatter Estimation Using Deep Splines

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal's frequency characteristics.

Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem

Volumetric imaging is an essential diagnostic tool for medical practitioners. The use of popular techniques such as convolutional neural networks (CNN) for analysis of volumetric images is constrained by the availability of detailed (with local annotations) training data and GPU memory. In this paper, the volumetric image classification problem is posed as a multi-instance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase. This method uses the extreme value theory to model the feature distribution of the images without a pathology and use it to identify positive instances of an imaged pathology. The experimental results, on three separate image classification tasks (i.e. classify retinal OCT images according to the presence or absence of fluid build-ups, emphysema detection in pulmonary 3D-CT images and detection of cancerous regions in 2D histopathology images) show that the proposed method produces classifiers that have similar performance to fully supervised methods and achieves the state of the art performance in all examined test cases.

Hydrothermal synthesis of PVP-passivated clove bud-derived carbon dots for antioxidant, catalysis, and cellular imaging applications

In recent times, carbon dots (CDs) are emerging for numerous interdisciplinary applications by modulating their inherent chemical functionality during or post-synthesis modification. The current study reports the hydrothermal synthesis of polyvinylpyrrolidone K-30 (PVP) passivated clove bud-derived carbon dots (PPCCDs) for multifaceted applications. The adopted technique is facile and environmentally friendly for the production of CDs with in situ PVP passivation. Physicochemical characterization of CDs is performed using various spectroscopic and microscopic techniques. The study reveals the formation of nitrogen-doped spherical PPCCDs with an average hydrodynamic size of ∼ 4.9 nm. It is also evident that there is modulation in optical properties and quantum efficiency as a result of PVP passivation. The study further demonstrates their suitability in biological environments as observed by pH stability, photostability, and cytocompatibility results. PPCCDs have shown significant antioxidant activity against DPPH (EC50: 57 µg/mL), suppression of superoxide anion radical (EC50: 53 µg/mL), and an efficient catalytic activity towards degradation of Rhodamine-B (Rh-B) dye. UV-Visible spectroscopy unveil the reaction mechanism during antioxidant and catalytic activities of CDs that are validated by Electron paramagnetic resonance (EPR) spectroscopy with an indication of effective electron or proton donating abilities. Its bioimaging potential is evidenced through cellular fluorescence imaging with 3T3 and L929 cell lines.

Epoch Extraction from Telephonic Speech Signal using Stockwell Transform

Speech is produced by exciting time-varying vocal tract with time-varying impulse-like excitations called epochs. In the literature, epoch extraction methods performed well on clean speech, but, detecting epoch locations from the band-limited signal like telephonic speech is difficult due to loss of information at low frequencies. This paper proposes a Stockwell transform (S-Transform)-based method that can find epochs accurately from the telephonic speech. The frequency-dependent Gaussian window and localization capabilities of S-Transform will reduce the effect of the bandpass nature of the telephonic channel. The telephonic channel is simulated using a 300- 3400 Hz bandpass filter. The proposed method is evaluated on five speakers data, namely BDL, SLT, JMK, KED, and RAB, from CMU arctic database. The results are compared with the state-of-the-art methods for both clean speech and telephonic speech. The proposed method produced comparable results with existing methods on clean speech but has shown an improvement of 4.68% over state-of-the-art methods.

Mixed-Sandwich Titanium(III) Qubits on Au(111): Electron Delocalization Ruled by Molecular Packing

Organometallic sandwich complexes are versatile molecular systems that have been recently employed for single-molecule manipulation and spin sensing experiments. Among related organometallic compounds, the mixed-sandwich S = 1/2 complex (η8-cyclooctatetraene)(η5-cyclopentadienyl)titanium, here [CpTi(cot)], has attracted interest as a spin qubit because of the long coherence time. Here the structural and chemical properties of [CpTi(cot)] on Au(111) are investigated at the monolayer level by experimental and computational methods. Scanning tunneling microscopy suggests that adsorption occurs in two molecular orientations, lying and standing, with a 3:1 ratio. XPS data evidence that a fraction of the molecules undergo partial electron transfer to gold, while our computational analysis suggests that only the standing molecules experience charge delocalization toward the surface. Such a phenomenon depends on intermolecular interactions that stabilize the molecular packing in the monolayer. This orientation-dependent molecule-surface hybridization opens exciting perspectives for selective control of the molecule-substrate spin delocalization in hybrid interfaces.

YADA: you always dream again for better object detection

Object detection has been attracting a lot of attention from the computer vision community. It has a wide range of practical applications ranging from the traditional use such as image annotation to modern uses such as self-driving vehicles, robotics, surveillance systems, and augmented reality. Recently, deep learning has significantly improved the state-of-the-art performance of the object detection task. Many works explore various deep network structures to improve the performance. However, the impact of training data is still not well investigated. Although some works focus on data augmentation and data synthesis, there is no guarantee that they are effective for the training process. In this paper, we propose a novel framework addressing the problem of generating relevant data and how to use them effectively. We apply lucid data synthesizing which generates data by mining hard examples and embedding them to the same context locations. Further, we utilize a dual-level deep network leveraged with these generated data to effectively detect hard objects in images. Extensive experiments on two benchmarks, PASCAL VOC and KITTI, demonstrate the superiority of our approach over the state-of-the-art methods.

Assessing Undergraduates' Perception of Risks Related to Body Art in Italy: The SUPeRBA Multicenter Cross-Sectional Study

Tattooing and piercing may lead to health complications. The present multicenter cross-sectional study aimed to assess awareness and knowledge of health risks related to body art and to identify their possible determinants among a large sample of undergraduates in Italy. A web-based questionnaire collecting information on socio-demographic characteristics, awareness, knowledge, and some potential predictors was administered to undergraduates attending twelve Italian universities. The level of knowledge was expressed as the number of correct answers (0-11 for tattooing, 0-14 for piercing). A total of 2985 participants (mean age 23.15 +/- 3.99, 73.9% F) participated in the study. Although 95.4% of the respondents were aware of possible health consequences of body art, a low level of specific knowledge was registered for both tattooing (mean number of correct answers 5.38 +/- 2.39) and piercing (5.93 +/- 3.12) consequences. Lower knowledge was associated with the attendance of non-life science course and with lower duration of academic education for both tattoo and piercing. Lower knowledge of tattooing risks was related with commuter status, while lower knowledge of piercing risks was associated with lower father's education. These findings highlight the need to enhance information campaigns targeted to youths to increase their awareness of possible health risk of body art.

Ultra-Tight Host-Guest Binding with Exceptionally Strong Positive Cooperativity

Cooperativity plays a critical role in self-assembly and molecular recognition. A rigid aromatic oligoamide macrocycle with a cyclodirectional backbone binds with DABCO-based cationic guests in a 2 : 1 ratio in high affinities (Ktotal ≈1013 M-2 ) in the highly polar DMF. The host-guest binding also exhibits exceptionally strong positive cooperativity quantified by interaction factors α that are among the largest for synthetic host-guest systems. The unusually strong positive cooperativity, revealed by isothermal titration calorimetry (ITC) and fully corroborated by mass spectrometry, NMR and computational studies, is driven by guest-induced stacking of the macrocycles and stabilization from the alkyl end chains of the guests, interactions that appear upon binding the second macrocycle. With its tight binding driven by extraordinary positive cooperativity, this host-guest system provides a tunable platform for studying molecular interactions and for constructing stable supramolecular assemblies.

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

Medical image segmentation is fundamental and essential for the analysis of medical images. Although prevalent success has been achieved by convolutional neural networks (CNN), challenges are encountered in the domain of medical image analysis by two aspects: 1) lack of discriminative features to handle similar textures of distinct structures and 2) lack of selective features for potential blurred boundaries in medical images. In this paper, we extend the concept of contrastive learning (CL) to the segmentation task to learn more discriminative representation. Specifically, we propose a novel patch-dragsaw contrastive regularization (PDCR) to perform patch-level tugging and repulsing. In addition, a new structure, namely uncertainty-aware feature re- weighting block (UAFR), is designed to address the potential high uncertainty regions in the feature maps and serves as a better feature re- weighting. Our proposed method achieves state-of-the-art results across 8 public datasets from 6 domains. Besides, the method also demonstrates robustness in the limited-data scenario. The code is publicly available at https://github.com/lzh19961031/PDCR_UAFR-MIShttps://github.com/lzh19961031/PDCR_UAFR-MIS.

Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry

In this paper, we present a new formulation for recovering the fiber tract geometry within a voxel from diffusion weighted magnetic resonance imaging (MRI) data, in the presence of single or multiple neuronal fibers. To this end, we define a discrete set of diffusion basis functions. The intravoxel information is recovered at voxels containing fiber crossings or bifurcations via the use of a linear combination of the above mentioned basis functions. Then, the parametric representation of the intravoxel fiber geometry is a discrete mixture of Gaussians. Our synthetic experiments depict several advantages by using this discrete schema: the approach uses a small number of diffusion weighted images (23) and relatively small b values (1250 s/mm(2)), i.e., the intravoxel information can be inferred at a fraction of the acquisition time required for datasets involving a large number of diffusion gradient orientations. Moreover our method is robust in the presence of more than two fibers within a voxel, improving the state-of-the-art of such parametric models. We present two algorithmic solutions to our formulation: by solving a linear program or by minimizing a quadratic cost function (both with non-negativity constraints). Such minimizations are efficiently achieved with standard iterative deterministic algorithms. Finally, we present results of applying the algorithms to synthetic as well as real data.

Application Research of Virtual Reality Technology in Ocean Environmental Art Design

With the progress of the information age, many scientific and technological products have changed people's lives. In the field of environmental art design, the emergence of computer technology and realistic simulation technology has broken the traditional environmental art design mode and accelerated the environmental art design scheme and its application based on virtual reality technology. This paper introduces realistic simulation technology and its practical application and probes into the profound impact brought by realistic simulation technology in the specific design process of environmental art design so as to provide some inspirations on ideas of environmental art design. The paper suggests the designers to lower the cost of the project design to a certain extent, avoid artificial design error, improve quality and efficiency of environmental art design, and most importantly enhance the resource utilization rate of the environment art design projects and then improve the economic benefits of substantial project in operation process. In addition, it is worthwhile to promote such technology in relative industries.

Recipes for Resilience: Engaging Caribbean Youth in Climate Action and Food Heritage through Stories and Song

This paper presents findings from the Recipes for Resilience project, an international, interdisciplinary collaboration between Caribbean and UK scholars of history, geography, anthropology, cultural studies, development studies, ethnobotany, and climate-risk studies, and the research partners, the Caribbean Youth Environment Network. The purpose of the project was to investigate how agrifood heritage may be mobilized in creative ways to engage Caribbean youth in climate action and justice. The project utilized arts and humanities methods, such as storytelling, songwriting, online games, and brief research-led talks, culminating in the co-created song: "Food and Resistance for Climate Resilience". The results of the project provide evidence that climate action requires arts and humanities methods to appeal to youth, as opposed to purely fact-based or scientific forms of climate communication. We conclude that co-creative methods such as music and storytelling can inspire youth to engage in climate action, in this case through a (re)valuation culinary and agricultural heritage.

Correlation between Redox Potential and Solvation Structure in Biphasic Electrolytes for Li Metal Batteries

The activity of lithium ions in electrolytes depends on their solvation structures. However, the understanding of changes in Li+ activity is still elusive in terms of interactions between lithium ions and solvent molecules. Herein, the chelating effect of lithium ion by forming [Li(15C5)]+ gives rise to a decrease in Li+ activity, leading to the negative potential shift of Li metal anode. Moreover, weakly solvating lithium ions in ionic liquids, such as [Li(TFSI)2 ]- (TFSI = bis(trifluoromethanesulfonyl)imide), increase in Li+ activity, resulting in the positive potential shift of LiFePO4 cathode. This allows the development of innovative high energy density Li metal batteries, such as 3.8 V class Li | LiFePO4 cells, along with introducing stable biphasic electrolytes. In addition, correlation between Li+ activity, cell potential shift, and Li+ solvation structure is investigated by comparing solvated Li+ ions with carbonate solvents, chelated Li+ ions with cyclic and linear ethers, and weakly solvating Li+ ions in ionic liquids. These findings elucidate a broader understanding of the complex origin of Li+ activity and provide an opportunity to achieve high energy density lithium metal batteries.

Investigating the impact of frame rate towards robust human action recognition

Human action recognition from videos is very important for visual analytics. Due to increasing abundance of diverse video content in the era of big data, research on human action recognition has recently shifted towards more challenging and realistic settings. Frame rate is one of key issues in diverse and realistic video settings. While there have been several evaluation studies investigating different aspects of action recognition such as different visual descriptors, the frame rate issue has been seldom addressed in the literature. Therefore, in this paper, we investigate the impact of frame rate on human action recognition with several state-of-the-art approaches and three benchmark data sets. Our experimental results indicate that those state-of-the-art approaches are not robust to the variations of frame rate. As a result, more robust visual features and advanced learning algorithms are required to further improve human action recognition performance towards its more practical deployments. In addition, we investigate key frame selection techniques for choosing a set of suitable frames from an action sequence for action recognition. Promising results indicate that well designed key-frame selection methods can produce a set of representative frames and eventually reduce the impact of frame rate on the performance of human action recognition. (C) 2015 Elsevier B.V. All rights reserved.

Continuous-Time Delta-Sigma ADCs With Improved Interferer Rejection

This paper reviews and analyzes the state of the art of Delta-Sigma modulators for receiver applications. Receiver ADCs require not only steadily increased bandwidth, low power and area consumption, but especially face strong interferer signals. These are outside the band of interest, but they most often determine the overall receiver dynamic range and linearity requirements. In order to avoid or at least relax explicit filtering in front of the ADC, Delta-Sigma architectures have been presented with improved robustness to interferers by filtering. This paper intends to review the motivation, to give a tutorial of the state of the art, as well as to introduce a deeper analysis for DSM with interferer robustness. For this, an introduction to the requirements of Delta-Sigma modulators in receivers is given, and a method is introduced which allows to analyze and to scale the internal states meeting the requirements of the interferer scenarios. The paper additionally analyzes state-of-the-art techniques of modulators with improved interferer rejection in order to outline more clearly their possibilities and drawbacks.

Differential attentional responding in caesarean versus vaginally delivered infants

Little is known about the role that the birth experience plays in brain and cognitive development. Recent research has suggested that birth experience influences the development of the somatosensory cortex, an area involved in spatial attention to sensory information. In this study, we explored whether differences in spatial attention would occur in infants who had different birth experiences, as occurs for caesarean versus vaginal delivery. Three-month-old infants performed either a spatial cueing task or a visual expectation task. We showed that caesarean-delivered infants' stimulus-driven, reflexive attention was slowed relative to vaginally delivered infants', whereas their cognitively driven, voluntary attention was unaffected. Thus, types of birth experience influence at least one form of infants' attention, and possibly any cognitive process that relies on spatial attention. This study also suggests that birth experience influences the initial state of brain functioning and, consequently, should be considered in our understanding of brain development.

NAS-Based CNN Channel Pruning for Remote Sensing Scene Classification

Recently, convolutional neural network (CNN)-based remote sensing scene classification has achieved great success. However, the prohibitively expensive computation and storage requirements of state-of-the-art models have hindered the deployment of CNNs on on- board platforms. In this letter, we propose a differentiable neural architecture search (NAS)-based channel pruning method to automatically prune the CNN models. In the proposed method, the importance of each output channel is measured by a trainable score. The scores are optimized by an NAS method to search a good-performance pruned structure. After the search process, a global score threshold is adopted to derive the pruned model. A cost-awareness loss is proposed for the search process to encourage the floating-point operation (FLOP) compression ratio of the pruned model coverage to a desired value. We apply the proposed method to ResNet-34 and VGG-16 to verify the performance. The NWPU-RESISC-45 and UC Merced Land-Use (UCM) datasets are used for the performance evaluation. A comparison with state-of-the-art pruning methods demonstrates that the proposed method can achieve competitive performance with a similar reduction in FLOP.

Sea Surface Temperature Forecasting With Ensemble of Stacked Deep Neural Networks

Oceanic temperature has a great impact on global climate and worldwide ecosystems, as its anomalies have been shown to have a direct impact on atmospheric anomalies. The major parameter for measuring the thermal energy of oceans is the sea surface temperature (SST). SST prediction plays an essential role in climatology and ocean-related studies. However, SST prediction is challenging due to the involvement of complex and nonlinear sea thermodynamic factors. To address this challenge, we design a novel ensemble of two stacked deep neural networks (DNNs) that uses air temperature, in addition to water temperature, to improve the SST prediction accuracy. To train our model and compare its accuracy with the state-of-the-art, we employ two well-known datasets from the national oceanic and atmospheric administration as well as the international Argo project. Using DNNs, our proposed method is capable of automatically extracting required features from the input time series and utilizing them internally to provide a highly accurate SST prediction that outperforms state-of-the-art models.

State-of-the-art magnetic resonance imaging sequences for pediatric body imaging

Longer examination time, need for anesthesia in smaller children and the inability of most children to hold their breath are major limitations of MRI in pediatric body imaging. Fortunately, with technical advances, many new and upcoming MRI sequences are overcoming these limitations. Advances in data acquisition and k-space sampling methods have enabled sequences with improved temporal and spatial resolution, and minimal artifacts. Sequences to minimize movement artifacts mainly utilize radial k-space filling, and examples include the stack-of-stars method for T1-weighted imaging and the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER)/BLADE method for T2-weighted imaging. Similarly, the sequences with improved temporal resolution and the ability to obtain multiple phases in a single breath-hold in dynamic imaging mainly use some form of partial k-space filling method. New sequences use a variable combination of data sampling methods like compressed sensing, golden-angle radial k-space filling, parallel imaging and partial k-space filling to achieve free-breathing, faster sequences that could be useful for pediatric abdominal and thoracic imaging. Simultaneous multi-slice method has improved diffusion-weighted imaging (DWI) with reduction in scan time and artifacts. In this review, we provide an overview of data sampling methods like parallel imaging, compressed sensing, radial k-space sampling, partial k-space sampling and simultaneous multi-slice. This is followed by newer available and upcoming sequences for T1-, T2- and DWI based on these other advances. We also discuss the Dixon method and newer approaches to reducing metal artifacts.

On the possibility of observing polarisation of vacuum in a magnetic field

The possibility of detecting birefringence induced by a magnetic field in vacuum is analysed. It is shown that the state of the art of the laser measuring technique allows one to hope for setting up successful experiments.

Blind image deblurring using elastic-net based rank prior

In this paper, we propose a new image prior for blind image deblurring. The proposed prior exploits similar patches of an image and it is based on an elastic-net regularization of singular values. We quantitatively verify that it favors clear images over blurred images. This property is able to facilitate the kernel estimation in the conventional maximum a posterior (MAP) framework. Based on this prior, we develop an efficient optimization method to solve the proposed model. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We also extend the prior to deal with non-uniform image deblurring problem. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.

Decorrelating Feature Spaces for Learning General-Purpose Audio Representations

Inspired by the recent progress in self-supervised learning for computer vision, in this paper, through the DeLoRes (Decorrelating latent spaces for Low Resource audio representation learning) framework, we introduce two new general-purpose audio representation learning approaches, the DeLoRes-S and DeLoRes-M. Our main objective is to make our network learn representations in a resource-constrained setting (both data and compute) that can generalize well across a diverse set of downstream tasks. Inspired by the Barlow Twins objective function, we propose learning embeddings invariant to distortions of an input audio sample while ensuring that they contain non-redundant information about the sample. We call this the DeLoRes learning framework, which we employ in different fashions with the DeLoRes-S and DeLoRes-M. In our experiments, we learn audio representations with less than half the number of model parameters and 10% audio samples compared to state-of-the-art algorithms to achieve state-of-the-art results on 7 out of 11 tasks on linear evaluation and 4 out of 11 tasks in the finetuning setup. In addition to being simple and intuitive, our pre-training procedure is amenable to compute through its inherent nature of construction. Furthermore, we conduct extensive ablation studies on our training algorithm, model architecture, and results and make all our code and pre-trained models publicly available(1).

Measurements and model for the satellite-to-aircraft channel in L-band

Wireless radio transmission from a satellite-based emitter to a receiver located on an aircraft is of interest for many applications such as internet access for passengers, air traffic management or positioning by global navigation satellite systems especially when a worldwide service shall be granted. In particular, the last two mentioned applications are related to the safety of life requiring realistic and accurate channel models for software-based system testing. State-of-the-art channel models for the satellite-to-aircraft case lack of accuracy in terms of modelling all propagation impairments. In this contribution, the authors describe airborne propagation experiments using Global Positioning System signals for channel sounding, the data evaluation and the derived channel model.

Comparative study of effective antibiofilm activity of beneficial microbes-mediated zirconia nanoparticles

In the present study, beneficial microbes-mediated zirconia nanoparticles were prepared using endophytic bacteria isolated from the seed of Terminalia chebula which were evaluated on inhibition of bacterial adherence and promotion to exhibit antibiofilm properties. The structure and distribution of the zirconia nanoparticles were examined through SEM (Scanning Electron Microscopy), EDS (Energy-Dispersive X-Ray spectroscopy), and XRD (X-ray diffraction analysis), which reveal the distribution of the particles. The morphology of biogenic zirconia nanoparticles was monoclinic and cubic. The formation of zirconia particle was confirmed using UV spectrum and the functional groups were intensified in FTIR (Fourier-transform infrared spectroscopy). The antibiofilm activity of the synthesized nanoparticles was tested in oral pathogens that cause biofilm by membrane integrity and leads to periodontal associated disease. The results showed that the particles had a significant effect on biofilm removal caused by oral pathogens. For determined concentration, the cytotoxicity of the endophytic bacterial facilitated zirconia nanoparticle (Zr NPs) was examined in HGF (Human gingival fibroblast cell line).

Exploiting objective text description of images for visual sentiment analysis

This paper addresses the problem of Visual Sentiment Analysis focusing on the estimation of the polarity of the sentiment evoked by an image. Starting from an embedding approach which exploits both visual and textual features, we attempt to boost the contribution of each input view. We propose to extract and employ anObjective Textdescription of images rather than the classicSubjective Textprovided by the users (i.e., title, tags and image description) which is extensively exploited in the state of the art to infer the sentiment associated to social images.Objective Textis obtained from the visual content of the images through recent deep learning architectures which are used to classify object, scene and to perform image captioning.Objective Textfeatures are then combined with visual features in an embedding space obtained with Canonical Correlation Analysis. The sentiment polarity is then inferred by a supervised Support Vector Machine. During the evaluation, we compared an extensive number of text and visual features combinations and baselines obtained by considering the state of the art methods. Experiments performed on a representative dataset of 47235 labelled samples demonstrate that the exploitation ofObjective Texthelps to outperform state-of-the-art for sentiment polarity estimation.

Dioxane-Linked Novel Bacterial Topoisomerase Inhibitors Exhibit Bactericidal Activity against Planktonic and Biofilm Staphylococcus aureus In Vitro

The development of novel treatments for Staphylococcus aureus infections remains a high priority worldwide. We previously reported compounds 0147 and 0186, novel bacterial topoisomerase inhibitors (NBTIs) with potent antibacterial activity against S. aureus, including methicillin-resistant S. aureus. Here, we further investigated the in vitro activity of 0147 and 0186 against S. aureus ATCC 29213. Both compounds demonstrated bactericidal activity against planktonic and biofilm S. aureus, which then translated into significant inhibition of biofilm formation. Combinations of NBTIs and glycopeptides yielded indifferent interactions against planktonic S. aureus, but several had synergistic effects against S. aureus biofilms. This work reinforces the potential of NBTIs as future therapeutics for S. aureus infections. IMPORTANCE The pathogen Staphylococcus aureus contributes substantially to infection-related mortality. Biofilms render bacteria more recalcitrant to antibacterial therapy. The manuscript describes the potent activity of a new class of antibacterial agents against both planktonic and biofilm populations of Staphylococcus aureus.

Sparse Regression Driven Mixture Importance Sampling for Memory Design

In this paper, we present a sparse regression (SpaRe) model-based yield analysis methodology and apply it to memory designs with state-of-the-art write-assist circuitry. At the core of its engine is a mixture importance sampling technique which consists of a uniform sampling stage and an importance sampling stage. The proposed methodology allows for fast and accurate statistical analysis of rare fail events. In our approach, a SpaRe model is built using the uniform sampling stage data points obtained via circuit simulation (CktSim). Along with the model, an optimal threshold value is determined for proper pass/fail predict capability. The model and the threshold value are then used to predict the response in the importance sampling stage. This alleviates the need for CktSims in the latter stage and introduces significant speedup compared to fully CktSim-based approaches. The SpaRe model-based yield analysis is tested on a 14-nm FinFET SRAM design, and the results corroborate well with that of full CktSim-based yield analysis. The methodology is used to compare multiple state-of-the-art SRAM designs including selective boost and write-assist designs. The operating Vmin ranges and trends corroborate well with hardware measurements.

Deep Prior Approach for Room Impulse Response Reconstruction

In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T-60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.

Saccharomyces boulardii and bismuth subsalicylate as low-cost interventions to reduce the duration and severity of cholera

We conducted a randomised single-blinded clinical trial of 100 cholera patients in Port-au-Prince, Haiti to determine if the probiotic Saccharomyces cerevisiae var. boulardii and the anti-diarrhoeal drug bismuth subsalicylate (BS) were able to reduce the duration and severity of cholera. Subjects received either: S. boulardii 250 mg, S. boulardii 250 mg capsule plus BS 524 mg tablet, BS 524 mg, or two placebo capsules every 6 hours alongside standard treatment for cholera. The length of hospitalisation plus the number and volume of emesis, stool and urine were recorded every 6 hours until the study subject was discharged (n = 83), left against medical advice (n = 11), or requested removal from the study (n = 6). There were no reported deaths or adverse study-related events. There were no statistically significant differences between the study arms and the outcomes of interest.

Graph-based people segmentation using a genetically optimized combination of classifiers

Many approaches for background subtraction and people detection have been developed so far. However, the best state-of-the-art methods do not yet give satisfactory results in real transportation environments. Indeed, these latter configurations imply several difficulties such as fast brightness changes, noise, shadows, scrolling background, etc., and a single approach cannot deal with all these. We propose an approach for people segmentation and tracking in videos that is suited for real-world conditions. Our strategy combines several state-of-the-art methods for people detection, silhouette appearance modeling and tracking. Each process also uses its own frame preprocessing pipeline. The optimal combination of the people classifiers used, as well as the optimal parameters of each of the combined methods, being too difficult to be determined altogether, a genetic algorithm is used to determine the optimal classifier parameters and their combination weights. The output of the latter is used as an initialization for a multiframe graph-cut operating on superpixel graphs. Our proposed approach is evaluated on the BOSS European project database that was acquired in moving trains and contains typical scientific locks encountered in real transportation systems. (C) 2018 SPIE and IS&T

Modeling Type-1 Iodothyronine Deiodinase with Peptide-Based Aliphatic Diselenides: Potential Role of Highly Conserved His and Cys Residues as a General Acid Catalyst

Type-1 iodothyronine deiodinase (ID-1) catalyzes the reductive elimination of 5'-I and 5-I on the phenolic and tyrosyl rings of thyroxine (T4), respectively. Chemically verifying whether I atoms with different chemical properties undergo deiodination through a common mechanism is challenging. Herein, we report the modeling of ID-1 using aliphatic diselenide (Se-Se) and selenenylsulfide (Se-S) compounds. Mechanistic investigations of deiodination using the ID-1-like reagents suggested that the 5'-I and 5-I deiodinations proceed via the same mechanism through an unstable intermediate containing a Se⋅⋅⋅I halogen bond between a selenolate anion, reductively produced from Se-Se (or Se-S) in the compound, and an I atom in T4. Moreover, imidazolium and thiol groups, which may act as general acid catalysts, promoted the heterolytic cleavage of the C-I bond in the Se⋅⋅⋅I intermediate, which is the rate-determining step, by donating a proton to the C atom.

De novo design of anti-tuberculosis agents using a structure-based deep learning method

Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.

Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. The qualitative and quantitative results demonstrate that the proposed methods achieve better performance than the several existing state-of-the-art methods.

Comprehensive study of aerosols properties over various terrain types

Aerosols are a crucial part of the climate system. Numerous factors, including aerosols, govern Earth's radiation balance. Different aerosols have distinct radiational effects on the earth system, and thus the slight change in their composition may lead to a drastic change in their radiative effects. Aerosols' chemical and physical properties also depend on generation processes, generation source, and geographical location. Significant spatio-temporal inconsistency is noticed in the distribution of aerosols. It makes it much difficult task to assess their radiative properties. We attempted to explore aerosol's optical properties and wavelength dependence over different locations. We have used AERONET (Aerosol Robotic Network) data over various stations (Kanpur, Jaipur, Gandhi College, Pune) with varying terrain properties in the Indian continent. We have studied the variation of different optical parameters: aerosol optical depth (AOD), single scattering albedo (SSA), and Angstrom exponent (α), and their wavelength dependence. This study indicated that Jaipur is the cleanest site, with dust aerosols as a primary aerosol. Though over Pune also aerosol concentration was relatively low but the anthropogenic aerosols contributed primarily over this site. Over the Indo-Gangetic Plain (IGP) sites, dust aerosols dominated the pre-monsoon season, while anthropogenic aerosols dominated the post-monsoon and winter seasons. The scatter plot of AOD with α gives the details of different aerosols (desert dust, continental aerosols, mixed aerosol, biomass burning aerosols, and sulfate aerosols) in the different seasons and places. This study provides an overview of aerosol properties, dominant aerosols in the aerosol system, and their seasonal and spectral variation.

Optimal Cooperative Strategies for PHY Security Maximization Subject to SNR Constraints

The cooperative jamming (CJ) and decode-and-forward (DF) protocols for physical layer security are studied in this paper. We propose a design that aims at maximizing the security gap, which is defined as the signal-to-noise ratio (SNR) difference between the destination and an eavesdropper, subject to security and reliability constraints defining the thresholds on the received signals' SNR values at the destination and the eavesdropper. A fractional quadratically constrained quadratic program (QCQP) is formulated, which is solved analytically and closed-form expressions are determined for both protocols. Numerical results demonstrate that the proposed designs achieve the same performance for the secrecy rate under both strategies compared with state-of-the-art approaches, for a proper choice of thresholds on SNR values. Additionally, for relaxed thresholds and at the cost of a slight decrease on the optimal secrecy rate value, the received SNR values at the eavesdropper are greatly decreased for the CJ protocol and even more for the DF protocol, while guaranteeing target SNR values at the destination, compared with previous state-of-the-art approaches.

Local Learning With Deep and Handcrafted Features for Facial Expression Recognition

We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve the state-of-the-art results in facial expression recognition (FER). To obtain automatic features, we experiment with multiple CNN architectures, pre-trained models, and training procedures, e.g., Dense-Sparse-Dense. After fusing the two types of features, we employ a local learning framework to predict the class label for each test image. The local learning framework is based on three steps. First, a k-nearest neighbors model is applied in order to select the nearest training samples for an input test image. Second, a one-versus-all support vector machines (SVM) classifier is trained on the selected training samples. Finally, the SVM classifier is used to predict the class label only for the test image it was trained for. Although we have used local learning in combination with handcrafted features in our previous work, to the best of our knowledge, local learning has never been employed in combination with deep features. The experiments on the 2013 FER Challenge data set, the FER+ data set, and the AffectNet data set demonstrate that our approach achieves the state-of-the-art results. With a top accuracy of 75.42% on the FER 2013, 87.76% on the FER+, 59.58% on the AffectNet eight-way classification, and 63.31% on the AffectNet seven-way classification, we surpass the state-of-the-art methods by more than 1% on all data sets.

The "Weather Intelligence for Renewable Energies" Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (WIRE) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

Coexistent Routing and Flooding Using WiFi Packets in Heterogeneous IoT Network

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol).

[Study on Test Method of Radiation Emission of Proton Therapy Equipment]

Electromagnetic compatibility testing of proton therapy system is different from that of traditional products in an anechoic chamber. It has high requirements on the division of sample composition, the understanding of applicable standards, the formulation of operation mode, the selection of test location, and the test of ambient noise. According to the requirements of GB 4824-2019 standard, the test method of radiation emission of proton therapy equipment was developed to provide reference advice for the industry, and the problems encountered in the actual test were studied.

Polymersomes with Red/Near-Infrared Emission and Reactive Oxygen Species Generation

In photodynamic therapy (PDT), the uses of nanoparticles bearing photosensitizers (PSs) can overcome some of the drawbacks of using a PS alone (e.g., poor water solubility and low tumor selectivity). However, numerous nano-formulations are developed by physical encapsulation of PSs through Van der Waals interactions, which have not only a limited load efficiency but also some in vivo biodistribution problems caused by leakage or burst release. Herein, polymersomes made from an amphiphilic block copolymer, in which a PS with aggregation-induced emission (AIE-PS) is covalently attached to its hydrophobic poly(amino acid) block, are reported. These AIE-PS polymersomes dispersed in aqueous solution have a high AIE-PS load efficiency (up to 46% as a mass fraction), a hydrodynamic diameter of 86 nm that is suitable for in vivo applications, and an excellent colloidal stability for at least 1 month. They exhibit a red/near-infrared photoluminescence and ability to generate reactive oxygen species (ROS) under visible light. They are non-cytotoxic in the dark as tested on Hela cells up to concentration of 100 µm. Benefiting from colloidal stability, AIE property and ROS generation capability, such a family of polymersomes can be great candidates for image-guided PDT.

A Review on Risk Factors of Postpartum Depression in India and Its Management

Postpartum depression is the term used for depression that predominates in the postpartum period, which is increasingly seen in research and clinical practice up to 1 year after delivery. Other symptoms commonly seen in women with postpartum depression include mood swings or lability and excessive worry about the baby. In addition, postpartum depression is often associated with anxiety disorders or significant anxiety symptoms. Women with a history of psychiatric illness are prone to postpartum depression. Postpartum depression is a crucial psychological health ailment that confers a vast degree of disability in females and is often associated with significant emotional, behavioral, and cognitive dangers in children. It is a disorder that is often unrecognized and undertreated. Postpartum depression is a critical issue to be addressed because it interferes with a woman's self-care and parenting. It also affects a child's mental growth and development. For these reasons, evaluation of risk factors is required to consider every facet of postpartum depression in women. This article reviews the associated risk factors and management of postpartum depression in India. Traditional studies for risk factors in postpartum depression have typically categorized women according to a particular stage of pregnancy that follows them into postpartum depression. Pregnancy-associated risk factors are estimated during pregnancy and are looked up for their predictive association with postpartum depression defined by clinical diagnostic methods or self-report assessment. Treatment options include psychotherapy and antidepressant medication. The risk of postpartum depression in fathers also follows maternal postpartum depression. Paternal depressive disorder is associated with adverse effects on child development. Early intervention for postpartum depression and anxiety may decrease the severity and recurrence of symptoms as well as the negative effects on the baby's health and development.

Safety of laronidase delivered into the spinal canal for treatment of cervical stenosis in mucopolysaccharidosis I

Enzyme replacement therapy with laronidase (recombinant human alpha-l-iduronidase) is successfully used to treat patients with mucopolysaccharidosis type I (MPS I). However, the intravenously-administered enzyme is not expected to treat or prevent neurological deterioration. As MPS I patients suffer from spinal cord compression due in part to thickened spinal meninges, we undertook a phase I clinical trial of lumbar intrathecal laronidase in MPS I subjects age 8 years and older with symptomatic (primarily cervical) spinal cord compression. The study faced significant challenges, including a heterogeneous patient population, difficulty recruiting subjects despite an international collaborative effort, and an inability to include a placebo-controlled design due to ethical concerns. Nine serious adverse events occurred in the subjects. All subjects reported improvement in symptomatology and showed improved neurological examinations, but objective outcome measures did not demonstrate change. Despite limitations, we demonstrated the safety of this approach to treating neurological disease due to MPS I.

Chemistry and Function of Glycosaminoglycans in the Nervous System

Proteoglycans, and especially their GAG components, participate in numerous biologically significant interactions with growth factors, chemokines, morphogens, guidance molecules, survival factors, and other extracellular and cell-surface components. These interactions are often critical to the basic developmental processes of cellular proliferation and differentiation, as well as to both the onset of disease sequelae and prevention of disease progression. In many tissues, proteoglycans and especially their glycosaminoglycan (GAG) components are mediators of these processes. The GAG family is characterized by covalently linked repeating disaccharides forming long unbranched polysaccharide chains. Thus far in higher eukaryotes, the family consists of chondroitin sulfate (CS), heparin/heparan sulfate (HS), dermatan sulfate (DS), keratan sulfate (KS) and hyaluronan (HA). All GAG chains (except HA) are characteristically modified by varying amounts of esterified sulfate. One or more GAG chains are usually found in nature bound to polypeptide backbones in the form of proteoglycans; HA is the exception. In the nervous system, GAG/proteoglycan-mediated interactions participate in proliferation and synaptogenesis, neural plasticity, and regeneration. This review focuses on the structure, chemistry and function of GAGs in nervous system development, disease, function and injury response.

Interaction and toxicity of ingested nanoparticles on the intestinal barrier

The gastrointestinal tract represents one of primary routes of entry for many nanomaterials. Their size in the nanometer range and their high surface area confer them very interesting properties as food additives. They are used as texturizing, opacifying or anticaking agents. Food packaging contains nanomaterials with antimicrobial properties. Humans are also orally exposed to nanoparticles (NPs) present in the air or drinking water. Ingested NPs can then reach the intestinal lumen and interact with the gastrointestinal fluids, microbiota, mucus layers and the epithelial barrier, allowing a potential translocation. The toxicological profile of ingested NPs is still unclear due to their variety in terms of composition and physicochemical properties as well as the limited number of investigations. Their unique properties related to their small size could however affect the intestinal ecosystem but also the physical and functional properties of the intestinal barrier. This review focuses on the fate of ingested organic and inorganic NPs in the intestinal lumen and their toxicity on the microbiota and epithelial cells.

Comparative pathogenicity of drug-resistant and drug-sensitive Trypanosoma brucei and Trypanosoma congolense infections in Nigerian local dogs

Animal trypanosomosis is an important endemic and wasting disease in sub-Saharan Africa. Its control relies on chemotherapy, and resistance to trypanocides has been widely reported. The pathogenicity of drug-resistant canine trypanosomes is not clear with scanty information available. Thus, this study assessed the comparative pathogenicity of drug-resistant and drug-sensitive Trypanosoma brucei and Trypanosoma congolense infections in dogs. Twenty Nigerian local dogs were used and were randomly assigned into five groups (A-E) of four dogs each. Group A served as the uninfected-control group, while groups B and C were infected with 106 drug-sensitive T. congolense and T. brucei. Groups D and E were infected with 106 multidrug-resistant T. congolense and T. brucei, respectively. The pre-patent period (PPP), clinical signs, level of parasitaemia (LOP), rectal temperature, body weight, packed cell volume (PCV), red blood cell count (RBC), haemoglobin concentration (HbC), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), total leucocyte count (TLC) and survivability were assessed. Groups D and E had longer (p < 0.05) mean PPP than groups B and C. Also, group E dogs had lower (p < 0.05) mean LOP, longer (p < 0.05) mean survivability, and higher (p < 0.05) mean body weight, PCV, HbC and RBC than group C dogs. The clinical signs were very severe in group C dogs, compared to group E dogs. However, these parameters did not differ statistically between groups B and D. Thus, multidrug-resistant T. brucei was of lower pathogenicity than drug-sensitive T. brucei, while multidrug-resistant and drug-sensitive T. congolense had comparable pathogenicity following infection in dogs.

A Novel Approach for Blind Estimation of Reverberation Time using Gamma Distribution Model

In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamnia distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that-the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.

From Compliance to Transformation: Notes on the MSU Strategic Plan to Address RVSM

This response to Campbell et al. makes three points. First, the commitment to "know more" must examine the full ecology of relationship violence and sexual misconduct (RVSM); that knowledge is essential for creating multilevel prevention strategies. Second, a full realization of an intersectional perspective requires attention to a broader range of power-based harms, forging institutional links between RVSM prevention and work on diversity, equity, and inclusion. Third, while support for survivors is certainly vital, most people who experience harm do not report it, and so an ambitious approach to prevention is vital to building communities in which everyone can thrive.

Toward Improving the Performance of Epoch Extraction from Telephonic Speech

Epoch is an abrupt closure event within a glottal cycle at which significant excitation to the vocal-tract system happens during the production of voiced speech. The state-of-the-art zero frequency filtering technique is a simple and efficient method that shows robustness in extracting the epochs from clean speech. However, this method has shown poor performance for telephonic quality speech, due to the presence of spurious zero crossings in epoch evidence, which leads to a high false alarm rate. Recently,zero-phase zero frequency resonator(ZP-ZFR) an alternative tozero frequency filteris proposed for stable implementation of zero frequency filtering technique. In this study, higher-order ZP-ZFR is investigated to improve the performance of zero frequency filtering for epoch extraction from telephonic speech. The performance of the proposed ZP-ZFR method is quantitatively evaluated on telephonic speech simulated using six standard databases having simultaneous electroglottograph recordings as ground truth. Experimental results suggest that the performance of the proposed method is significantly better than the state-of-the-art methods in terms of identification rate and false alarm rate.

Vimentin as a potential target for diverse nervous system diseases

Vimentin is a major type III intermediate filament protein that plays important roles in several basic cellular functions including cell migration, proliferation, and division. Although vimentin is a cytoplasmic protein, it also exists in the extracellular matrix and at the cell surface. Previous studies have shown that vimentin may exert multiple physiological effects in different nervous system injuries and diseases. For example, the studies of vimentin in spinal cord injury and stroke mainly focus on the formation of reactive astrocytes. Reduced glial scar, increased axonal regeneration, and improved motor function have been noted after spinal cord injury in vimentin and glial fibrillary acidic protein knockout (GFAP-/-VIM-/-) mice. However, attenuated glial scar formation in post-stroke in GFAP-/- VIM-/- mice resulted in abnormal neuronal network restoration and worse neurological recovery. These opposite results have been attributed to the multiple roles of glial scar in different temporal and spatial conditions. In addition, extracellular vimentin may be a neurotrophic factor that promotes axonal extension by interaction with the insulin-like growth factor 1 receptor. In the pathogenesis of bacterial meningitis, cell surface vimentin is a meningitis facilitator, acting as a receptor of multiple pathogenic bacteria, including E. coli K1, Listeria monocytogenes, and group B streptococcus. Compared with wild type mice, VIM-/- mice are less susceptible to bacterial infection and exhibit a reduced inflammatory response, suggesting that vimentin is necessary to induce the pathogenesis of meningitis. Recently published literature showed that vimentin serves as a double-edged sword in the nervous system, regulating axonal regrowth, myelination, apoptosis, and neuroinflammation. This review aims to provide an overview of vimentin in spinal cord injury, stroke, bacterial meningitis, gliomas, and peripheral nerve injury and to discuss the potential therapeutic methods involving vimentin manipulation in improving axonal regeneration, alleviating infection, inhibiting brain tumor progression, and enhancing nerve myelination.

Renal tubular response to titanium dioxide nanoparticles exposure

Titatinum dioxide nanoparticles (TiO2-NPs) are frequently used in several areas. Titanium alloys are employed in orthopedic and odontological surgery (such as hip, knee, and teeth implants). To evaluate the potential acute toxic effects of titanium pieces implantations and in other sources that allow the systemic delivery of titanium, parenteral routes of TiO2-NPs administration should be taken into account. The present study evaluated the impact of subcutaneous administration of TiO2-NPs on renal function and structure in rats. Animals were exposed to a dose of 50 mg/kg b.w., s.c. and sacrificed after 48 h. Titanium levels were detected in urine (135 ± 6 ηg/mL) and in renal tissue (502 ± 40 ηg/g) employing inductively coupled plasma mass spectrometry. An increase in alkaline phosphatase activity, total protein levels, and glucose concentrations was observed in urine from treated rats suggesting injury in proximal tubule cells. In parallel, histopathological studies showed tubular dilatation and cellular desquamation in these nephron segments. In summary, this study demonstrates that subcutaneous administration of TiO2-NPs causes acute nephrotoxicity evidenced by functional and histological alterations in proximal tubule cells. This fact deserves to be mainly considered when humans are exposed directly or indirectly to TiO2-NPs sources that cause the systemic delivery of titanium.

A case of nephrogenic diabetes insipidus likely caused by anti-neutrophil cytoplastic antibody-associated vasculitis

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a relatively rare form of autoimmune disease. Diabetes insipidus (DI) is characterized by diluted polyuria and thirstiness, and is clinically categorized into central and nephrogenic DI depending on damaged organs. In most previously reported cases, ANCA-related disorders have been implicated in central DI, which is attributed to impaired secretion of arginine vasopressin (AVP) from the posterior pituitary. However, no previous case of AAV-related nephrogenic DI has been reported in the English literature. Herein, we report a case of nephrogenic DI likely caused by AAV. A 76-year-old man was admitted to our hospital for acute kidney injury. He showed dehydration, polyuria, and polydipsia. Laboratory tests demonstrated elevated levels of serum urea and creatinine and a high myeloperoxidase ANCA titer. In the present case, both plasma AVP concentration and response of AVP secretion to 5% saline load test were normal. In addition, 1-desmino-8-arginine vasopressin administration could not increase urinary osmolarity. Kidney biopsy specimen revealed tubulointerstitial nephritis with findings that appeared to indicate peritubular capillaritis. Therefore, the patient was diagnosed with nephrogenic DI likely owing to ANCA-associated tubulointerstitial nephritis. Immediately after prednisolone administration, urinary volume decreased, urinary osmolarity increased, and kidney function was improved. This case demonstrates that AAV that extensively affects the tubulointerstitial area can result in nephrogenic DI.

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research

We argue that critical areas of memory research rely on problematic measurement practices and provide concrete suggestions to improve the situation. In particular, we highlight the prevalence of memory studies that use tasks (like the "old/new" task: "have you seen this item before? yes/no") where quantifying performance is deeply dependent on counterfactual reasoning that depends on the (unknowable) distribution of underlying memory signals. As a result of this difficulty, different literatures in memory research (e.g., visual working memory, eyewitness identification, picture memory, etc.) have settled on a variety of fundamentally different metrics to get performance measures from such tasks (e.g., A', corrected hit rate, percent correct, d', diagnosticity ratios, K values, etc.), even though these metrics make different, contradictory assumptions about the distribution of latent memory signals, and even though all of their assumptions are frequently incorrect. We suggest that in order for the psychology and neuroscience of memory to become a more cumulative, theory-driven science, more attention must be given to measurement issues. We make a concrete suggestion: The default memory task for those simply interested in performance should change from old/new ("did you see this item'?") to two-alternative forced-choice ("which of these two items did you see?"). In situations where old/new variants are preferred (e.g., eyewitness identification; theoretical investigations of the nature of memory signals), receiver operating characteristic (ROC) analysis should be performed rather than a binary old/new task.

Drug-Carrier Miscibility in Solid Dispersions of Glibenclamide and a Novel Approach to Enhance Its Solubility Using an Effervescent Agent

The present research aims to investigate the miscibility, physical stability, solubility, and dissolution rate of a poorly water-soluble glibenclamide (GLB) in solid dispersions (SDs) with hydrophilic carriers like PEG-1500 and PEG-50 hydrogenated palm glycerides (Acconon). Mathematical theories such as Hansen solubility parameters, Flory Huggins theory, Gibbs free energy, and the in silico molecular dynamics simulation study approaches were used to predict the drug-carrier miscibility. To increase the solubility further, the effervescence technique was introduced to the conventional solid dispersions to prepare effervescent solid dispersions (ESD). Solid dispersions (SDs) were prepared by microwave, solvent evaporation, lyophilization, and hot melt extrusion (HME) techniques and tested for different characterization parameters. The theoretical and in silico parameters suggested that GLB would show good miscibility with the selected carriers under certain conditions. Intermolecular hydrogen bonding between the drug and carrier(s) was confirmed by Fourier transform infrared spectroscopy and proton nuclear magnetic resonance spectroscopy. Solid-state characterizations like powder X-ray diffraction, differential scanning calorimetry, and microscopy confirm the amorphous nature of SDs. The addition of the effervescent agent improved the amorphous nature, due to which the solubility and drug release rate was increased. In vitro and ex vivo intestinal absorption studies showed improved flux and permeability than the pure drug, suggesting an enhanced drug delivery. The GLB solubility, dissolution, and stability were greatly enhanced by the SD and ESD technology.

Right Ventricular Limitation: A Tale of Two Elastances

Right ventricular (RV) dysfunction is a commonly considered cause of low cardiac output in critically ill patients. Its management can be difficult and requires an understanding of how the RV limits cardiac output. We explain that RV stroke output is caught between the passive elastance of the RV walls during diastolic filling and the active elastance produced by the RV in systole. These two elastances limit RV filling and stroke volume and consequently limit left ventricular stroke volume. We emphasize the use of the term "RV limitation" and argue that limitation of RV filling is the primary pathophysiological process by which the RV causes hemodynamic instability. Importantly, RV limitation can be present even when RV function is normal. We use the term "RV dysfunction" to indicate that RV end-systolic elastance is depressed or diastolic elastance is increased. When RV dysfunction is present, RV limitation occurs at lowerpulmonary valve opening pressures and lower stroke volume, but stroke volume and cardiac output still can be maintained until RV filling is limited. We use the term "RV failure" to indicate the condition in which RV output is insufficient for tissue needs. We discuss the physiological underpinnings of these terms and implications for clinical management.

An unusually long intra-atrial course of the right coronary artery detected on CT angiography

We hereby present an unusually long intra-atrial course of the right coronary artery incidentally detected on computed tomography angiography. Although usually asymptomatic, an intra-atrial right coronary artery may be injured during iatrogenic procedures which require right heart catheterisation.

MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.

TECHNICAL DEVELOPMENTS IN MAMMOGRAPHY

The art, science, and technology of mammography have developed steadily over the past 35 y. Mammography is a central tool for diagnosis of symptoms of breast cancer. In addition, periodic screening of asymptomatic women in certain age groups has been clearly demonstrated to contribute to reduction of mortality from breast cancer. Technical improvements have allowed the examination to be carried out at substantially lower radiation dose than was necessary to obtain a good image in the 1970's, while at the same time providing greatly improved contrast, spatial resolution, dynamic range and tissue coverage. Digital mammography overcomes many of the technical limitations inherent in screen-film mammography and has been shown to offer increased accuracy for women under 50 and those with dense breasts. The radiation risk associated with mammography cannot be ignored, however, modern analysis suggests that it is very low, especially compared to the benefits from the exam. Nevertheless, imaging should be conducted with careful attention to efficient use of the radiation. New techniques, currently under development and evaluation, promise to add further to the value of mammography.

The Mediating Effect of Social Anxiety on the Relationship Between Internet Addiction and Aggression in Teenagers

The purpose of this study was to investigate the effect of "internet addiction level" on "aggression level" among teenagers and to examine the mediating role of "social anxiety level" on this effect. The study participants consisted of 958 students attending private teaching institutions for university preparation in Turkey. Data was collected from voluntary participants through data collection forms delivered to the management of these institutions. "Sociodemographic Characteristics Questionnaire Form", "Young's Internet Addiction Test", "Buss-Perry Aggression Questionnaire" and "Social Anxiety Scale" were used as data collection tools. A regression analysis based on the bootstrap method was implemented to test whether "social anxiety level" had a mediating role on the influence of students' "internet addiction level" on "aggression level". According to the findings, it was determined that the indirect effect of "internet addiction" on "aggression" was significant, and therefore, "social anxiety level" mediated the relationship between "internet addiction" and "aggression".

Characterness: An Indicator of Text in the Wild

Text in an image provides vital information for interpreting its contents, and text in a scene can aid a variety of tasks from navigation to obstacle avoidance and odometry. Despite its value, however, detecting general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem, which reflects the characterness of an image region. In this sense, our approach mirrors the move from saliency detection methods to measures of objectness. In order to measure the characterness, we develop three novel cues that are tailored for character detection and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field model so as to exploit the inherent dependencies between characters. We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multicue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection data sets. We also show that our measurement of characterness is superior than state-of-the-art saliency detection models when applied to the same task.

Polynomial matrix decompositions and semi-blind channel estimation for MIMO frequency-selective channels

The authors propose a semi-blind channel estimation (semi-BCE) and precoding/decoding technique for frequency selective (FS) multiple-input multiple-output (MIMO) channels. A FS MIMO channel can be represented using a matrix whose elements are polynomials; hence their method is based on polynomial matrix decomposition. Polynomial eigenvalue decomposition (PEVD) and polynomial QR decomposition (PQRD) are the generalisation of eigenvalue decomposition and QR decomposition; they are suitable for decoupling and precoding of FS MIMO channels. As the coding of communication channels requires reliable estimation of the channel, a semi-BCE scheme, coupled with PQRD/PEVD-based MIMO-channel decomposition, is attractive since this reduces training overhead (pilot transmission) considerably, resulting in higher spectral efficiency. The proposed semi-BCE algorithm is a generalisation of a recently developed single-input single output BCE method to MIMO systems. A new class of PQRD algorithms is introduced, which is based on the recently-developed sequential matrix diagonalisation (SMD). The decoders produced by the proposed SMD-based PQRD algorithm are shown to be more suitable (efficient) for MIMO-channel equalisation than those generated by the prior art. Computer simulations show that the proposed MIMO-channel coding strategy compares favourably to state-of-the-art MIMO systems, in terms of bit error rate performance, while reducing the overhead.

TREATMENT WITH THERESIENöL OF SURGICAL DISEASES OF THE SKIN

Theresienöl is a 100 % natural product representing a mixture of animal and vegetable raw materials from Tyrol. Its exact recipe has been preserved untouched and in deep secret for more than six centuries yet, and has been passed down from generation to generation. Six patients were included in this case series one patient with malignant melanoma of the skin after re-excision with subsequent non-free skin surgical plastic, two patients with III degree skin burning and three patients with infected wound successfully treated with Theresienöl. All of them - before the application of Theresienöl - were treated with different operative methods. The treatment of scars from operative interventions with Theresienöl is very effective. That is why it must start directly after the operative intervention. The therapeutic effect of Theresienöl for postoperative scars is commensurable with and even better than the one of all applied until now local medicines, which makes it an agent of choice in those cases. Theresienöl represents a good alternative to the free skin surgical plastic for small burns of III degree. The local treatment of infected wounds with Theresienoil is more effective and economically sound than the treatment with all the rest types of dressings. The effects from the treatment of different surgical diseases with Theresienöl occur very rapidly, while there is a very good response to local hematomas, pain, and itchiness by the medicine, and there are no side effects from its administration.

Identification and Validation of Yak ( Bos grunniens) Frozen-Thawed Sperm Proteins Associated with Capacitation and the Acrosome Reaction

To achieve fertilization, mammalian spermatozoa must undergo capacitation and the acrosome reaction (AR) within the female reproductive tract. However, the effects of cryopreservation on sperm maturation and fertilizing potential have yet to be established. To gain insight into changes in protein levels within sperm cells prepared for use in the context of fertilization, a comprehensive quantitative proteomic profiling approach was used to analyze frozen-thawed Ashidan yak spermatozoa under three sequential conditions: density gradient centrifugation-based purification, incubation in a capacitation medium, and treatment with the calcium ionophore A23187 to facilitate AR induction. In total, 3280 proteins were detected in these yak sperm samples, of which 3074 were quantified, with 68 and 32 being significantly altered following sperm capacitation and AR induction. Differentially abundant capacitation-related proteins were enriched in the metabolism and PPAR signaling pathways, while differentially abundant AR-related proteins were enriched in the AMPK signaling pathway. These data confirmed a role for superoxide dismutase 1 (SOD1) as a regulator of sperm capacitation while also offering indirect evidence that heat shock protein 90 alpha (HSP90AA1) regulates the AR. Together, these findings offer a means whereby sperm fertility-related marker proteins can be effectively identified. Data are available via Proteome Xchange with identifier PXD035038.

Aspergilloma in a hydatid cavity

Fungal colonization by aspergillus usually occurs in pre-existing lung cavities mostly due to post-tubercular sequelae. Colonization of a hydatid cavity is very rare. We hereby report this unusual co-infection in a 55 years old diabetic male patient who was diagnosed pre-operatively and was managed with surgery, anti-fungal agents, and anthelminthics. The possibility of this co-infection should make clinicians more vigilant in managing hydatid cysts in diabetics and immunocompromised, as they may have concomitant fungal infestation of the hydatid cavity.

Sulfuretin induces osteoblast differentiation through activation of TGF-β signaling

The identification and examination of potential determinants controlling the progression of cell fate toward osteoblasts can be intriguing subjects. In this study, the effects of sulfuretin, a major compound isolated from Rhus verniciflua Stokes, on osteoblast differentiation were investigated. Treatments of sulfuretin induced alkaline phosphatase (ALP) activity in mesenchymal C3H10T1/2 cells and mineralization in preosteoblast MC3T3-E1 cells. Pro-osteogenic effects of sulfuretin were consistently observed in freshly isolated primary bone marrow cells. In mechanical studies, sulfuretin specifically induced expression of TGF-β target genes, such as SMAD7 and PAI-1, but not other signaling pathway-related genes. Similar to the results of gene expression analysis, reporter assays further demonstrated TGF-β-specific induction by sulfuretin. Furthermore, disruption of TGF-β signaling using treatment with TGF-β-specific inhibitor, SB-431542, and introduction of SMAD2/3 small interfering RNA impaired the effects of sulfuretin in inducing ALP activity and expression of ALP mRNA. Together, these data indicate that the pro-osteogenic effects of sulfuretin are mediated through activation of TGF-β signaling, further supporting the potential of sulfuretin in the prevention of bone-related diseases such as bone fracture and osteoporosis.

Aligned visual semantic scene graph for image captioning

Image captioning is a multi-modal task to describe an image into natural language. Many state-of-the-art methods generally take the encoder-decoder architecture, encode an image by the convolution neural networks, or by the structured semantic scene graph that contains the object, relationship and the attribute information. The image scene graph constructed by the existing scene graph generation models are generally too noisy. To alleviate the phenomenon, we propose a multi-level cross-modal alignment (MCA) module to align the image scene graph with the sentence scene graph at different level. MCA can distill the redundant information of the image scene graph according to the sentence scene graph, and providing the commonsense knowledge for the decoder. Except for the semantic relationships, we take advantage of the bounding boxes with the visual objects to compute the implicit spatial relationships for the detected objects. With the aligned scene graph features and the implicit spatial relationship information, our decoder fused them via the dynamic mixtured attention to translate these features into descriptions. Extensive experiments on the MSCOCO dataset got the promising result compared with the state-of-the-art methods, which verified the effectiveness of our method.

Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification

The current literature of remote sensing (RS) scene classification shows that state-of-the-art results are achieved using feature extraction methods, where convolutional neural networks (CNNs) (mostly VGG16 with 138.36 M parameters) are used as feature extractors and then simple to complex handcrafted modules are added for additional feature learning and classification, thus coming back to feature engineering. In this paper, we revisit the fine-tuning approach for deeper networks (GoogLeNet and Beyond) and show that it has not been well exploited due to the negative effect of the vanishing gradient problem encountered when transferring knowledge to small datasets. The aim of this work is two-fold. Firstly, we provide best practices for fine-tuning pre-trained CNNs using the root-mean-square propagation (RMSprop) method. Secondly, we propose a simple yet effective solution for tackling the vanishing gradient problem by injecting gradients at an earlier layer of the network using an auxiliary classification loss function. Then, we fine-tune the resulting regularized network by optimizing both the primary and auxiliary losses. As for pre-trained CNNs, we consider in this work inception-based networks and EfficientNets with small weights: GoogLeNet (7 M) and EfficientNet-B0 (5.3 M) and their deeper versions Inception-v3 (23.83 M) and EfficientNet-B3 (12 M), respectively. The former networks have been used previously in the context of RS and yielded low accuracies compared to VGG16, while the latter are new state-of-the-art models. Extensive experimental results on several benchmark datasets reveal clearly that if fine-tuning is done in an appropriate way, it can settle new state-of-the-art results with low computational cost.

Proximity Detection During Epidemics: Direct UWB TOA Versus Machine Learning Based RSSI

In this paper, we compare the direct TOA-based UWB technology with the RSSI-based BLE technology using machine learning algorithms for proximity detection during epidemics in terms of complexity of implementation, availability in existing smart phones, and precision of the results. We establish the theoretical limits on the precision and confidence of proximity estimation for both technologies using the Cramer Rao Lower Bound (CRLB) and validate the theoretical foundations using empirical data gathered in diverse practical operating scenarios. We perform our empirical experiments at eight distances in three flat environments and one non-flat environment encompassing both Line of Sight (LOS) and Obstructed-LOS (OLOS) situations. We also analyze the effects of various postures (eight angles) of the person carrying the sensor, and four on-body locations of the sensor. To estimate the range with BLE RSSI, we use 14 features for training the Gradient Boosted Machines (GBM) learning algorithm and we compare the precision of results with those obtained from memoryless UWB TOA ranging algorithm. We show that the memoryless UWB TOA algorithm achieves 93.60% confidence, slightly outperforming the 92.85% confidence of the BLE RSSI with more complex GBM machine learning (ML) algorithm and the need for substantial training. The training process for the RSSI-based BLE social distance measurements involved 3000 measurements to create a training dataset for each scenario and post-processing of data to extract 14 features of RSSI, and the ML classification algorithm consumed 200 s of computational time. The memoryless UWB ranging algorithm achieves more robust results without any need for training in less than 0.5 s of computation time.

Mansonic neuroschistosomiasis in the childhood: an undiagnosed pathology?

Schistosomiasis is an endemic parasitic disease in several tropical countries. In Brazil, the only prevalent species of parasite responsible for schistosomiasis is Schistosoma mansoni. Neuroschistosomiasis is the second most frequent form of infection and the primary ectopic manifestation, with predominant involvement of the lower thoracic spinal cord and lumbar and lumbosacral regions. The frequent contact of children with contaminated ponds and the immaturity of their immune systems make this age group especially susceptible to infection by this parasite. Therefore, neuroschistosomiasis mansoni should always be considered in cases of transverse myelitis in children from endemic regions. The treatment for this condition is quite simple and effective, resulting in total recovery of neurological deficits if the diagnosis is made early.

Knowledge-Based Representation Learning for Nucleus Instance Classification From Histopathological Images

The classification of nuclei in H&E-stained histopathological images is a fundamental step in the quantitative analysis of digital pathology. Most existing methods employ multi-class classification on the detected nucleus instances, while the annotation scale greatly limits their performance. Moreover, they often downplay the contextual information surrounding nucleus instances that is critical for classification. To explicitly provide contextual information to the classification model, we design a new structured input consisting of a content-rich image patch and a target instance mask. The image patch provides rich contextual information, while the target instance mask indicates the location of the instance to be classified and emphasizes its shape. Benefiting from our structured input format, we propose Structured Triplet for representation learning, a triplet learning framework on unlabelled nucleus instances with customized positive and negative sampling strategies. We pre-train a feature extraction model based on this framework with a large-scale unlabeled dataset, making it possible to train an effective classificationmodel with limited annotated data. We also add two auxiliary branches, namely the attribute learning branch and the conventional self-supervised learning branch, to further improve its performance. As part of this work, we will release a new dataset of H&E-stained pathology images with nucleus instance masks, containing 20,187 patches of size 1024 x 1024, where each patch comes from a different whole- slide image. The model pre-trained on this dataset with our framework significantly reduces the burden of extensive labeling. We show a substantial improvement in nucleus classification accuracy compared with the state-ofthe-art methods.

Latent-Class Hough Forests for 6 DoF Object Pose Estimation

In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the art template matching feature into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. We train with positive samples only and we treat class distributions at the leaf nodes as latent variables. During testing we infer by iteratively updating these distributions, providing accurate estimation of background clutter and foreground occlusions and, thus, better detection rate. Furthermore, as a by-product, our Latent-Class Hough Forests can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected two, more challenging, datasets for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We provide extensive experiments on the various parameters of the framework such as patch size, number of trees and number of iterations to infer class distributions at test time. We also evaluate the Latent-Class Hough Forests on all datasets where we outperform state of the art methods.

The growth-regulating factor PdbGRF1 positively regulates the salt stress response in Populus davidiana × P. bolleana

Growth-regulating factor (GRF) is a transcription factor unique to plants that plays a crucial role in the growth, development and stress adaptation of plants. However, information on the GRFs related to salt stress in Populus davidiana × P. bolleana is lacking. In this study, we characterized the activity of PdbGRF1 in transgenic Populus davidiana × P. bolleana under salt stress. qRTPCR analyses showed that PdbGRF1 was highly expressed in young leaves and that the pattern of PdbGRF1 expression was significantly changed at most time points under salt stress, which suggests that PdbGRF1 expression may be related to the salt stress response. Moreover, PdbGRF1 overexpression enhanced tolerance to salt stress. A physiological parameter analysis showed that the overexpression of PdbGRF1 significantly decreased the contents of hydrogen peroxide (H2O2) and malondialdehyde (MDA) and increased the activities of antioxidant enzymes (SOD and POD) and the proline content. A molecular analysis showed that PdbGRF1 regulated the expression of PdbPOD17 and PdbAKT1 by binding to the DRE ('A/GCCGAC') in their respective promoters. Together, our results demonstrate that the binding of PdbGRF1 to DRE regulates genes related to stress tolerance and activates the associated physiological pathways, and these effects increase the ROS scavenging ability, reduce the degree of damage to the plasma membrane and ultimately enhance the salt stress response in Populus davidiana × P. bolleana.

Challenges in the diagnosis and management of immune-mediated necrotising myopathy (IMNM) in a patient on long-term statins

Immune-mediated necrotising myopathy (IMNM) is a severe and poorly understood complication of statin use. Prompt management with immunosuppressive treatment is often needed to control the condition, which differs from the management of the more commonly recognised statin-induced myopathy. We present a case report and brief review of the literature regarding the pathogenesis, diagnosis, and management of anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) positive IMNM (HMGCR IMNM). There are no randomised clinical trials, but several smaller studies and cases suggest a triple therapy of corticosteroids, IVIG, and a corticosteroid-sparing immunosuppressant appears efficacious in patients with IMNM and proximal weakness. The mechanism of statin-induced IMNM is uncertain, and this is further complicated by the reports of HMGCR IMNM in statin-naïve patients, including children. We present a case of biopsy-confirmed HMGCR IMNM in a woman taking daily statins for treatment of hypercholesterolaemia for 4 years. She presented with symptoms consistent with a urinary tract infection (UTI), including muscle weakness. She was treated as an isolated case of UTI. One month later, she presented again with worsening weakness in her shoulders and hips. Creatine kinase was elevated, and MRI showed increased signal with STIR sequences in both thighs. Anti-HMGCR was positive and leg biopsy-confirmed necrotising changes. Stopping her statin prescription and a short course of prednisolone did not improve her muscle weakness. Adding methotrexate resulted in eventual resolution of her symptoms. IMNM should be considered as a differential in any patient taking statins presenting with muscle weakness, and this case suggests that immunosuppressant therapy in addition to cessation of statins is effective at treating IMNM. Clinical trials are needed to further investigate the efficacy of different combinations of immunosuppressants.

The intestinal microbiota in colorectal cancer metastasis - Passive observer or key player?

The association between colorectal cancer (CRC) and alterations in intestinal microbiota has been demonstrated by several studies, and there is increasing evidence that bacteria are an important component of the tumour microenvironment. Bacteria may contribute to the development of CRC metastasis by signalling through metabolites, promoting epithelial-mesenchymal transition, creating an immunosuppressive microenvironment and through the impairment of the gut-vascular barrier. Host immunity and intestinal microbiome symbiosis play a key role in determining innate and adaptive immune responses at the local and systemic level. How this gut-systemic axis might contribute to the development of CRC metastasis is however unclear. Several clinical trials are investigating the impact of microbiome-targeted interventions on the systemic inflammatory response, treatment-related complications, and side effects. This review examines pre-clinical and clinical studies which have examined the role of microbes in relation to CRC metastasis, the mechanisms which may contribute to tumour dissemination, and directions for future work.

Constrained feature selection for semisupervised color-texture image segmentation using spectral clustering

Color-texture image segmentation remains a challenging problem due to extensive color-texture variability. Thus, the limited prior knowledge that is expressed by pairwise constraints can be exploited to guide the segmentation process. We propose a new semisupervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. The pairwise constraints are used by the constraint feature selection to choose the most relevant features among an available set of color and texture features. For this purpose, an innovative constraint score is developed to evaluate a subset of features at one time. A specific constrained SC algorithm involving the pairwise constraints is then applied to regroup the pixels into clusters. Experimental results on four benchmark datasets show that the proposed constraint score outperforms the main state-of-the-art constraint scores and that our semisupervised segmentation method is competitive compared with supervised, semisupervised, and unsupervised state-of-the-art segmentation methods. ? 2021 SPIE and IS&T [DOI: 10 .1117/1.JEI.30.1.013014]

Deep Scattering Spectra with Deep Neural Networks for Acoustic Scene Classification Tasks

As one of the most commonly used features, Mel-frequency cepstral coefficients (MFCCs) are less discriminative at high frequency. A novel technique, known as Deep scattering spectrum (DSS), addresses this issue and looks to preserve greater details. DSS feature has shown promise both on classification and recognition tasks. In this paper, we extend the use of DSS feature for acoustic scene classification task. Results on Detection and classification of acoustic scenes and events (DCASE) 2016 and 2017 show that DSS provided 4:8% and 17:4% relative improvements in accuracy over MFCC features, within a state-of-the-art time delay neural network framework.

Evaluating the effect of LPS from periodontal pathogenic bacteria on the expression of senescence-related genes in human dental pulp stem cells

The human dental pulp stem cells (hDPSCs) are one of the readily available sources of multipotent mesenchymal stem cells (MSCs) and can be considered as a type of tool cells for cell-based therapies. However, the main limitation in the clinical use of these cells is DPSC senescence, which can be induced by lipopolysaccharide (LPS) of oral pathogenic bacteria. Up to now, far little attention has been paid to exploring the molecular mechanisms of senescence in DPSCs. So, the current study aimed to investigate the underlying molecular mechanism of senescence in hDPSCs stimulated with Porphyromonas gingivalis (P. gingivalis) and Escherichia coli (E. coli)-derived LPSs, by evaluating both mRNA and protein expression of four important senescence-related genes, including TP53, CDKN1A, CDKN2A and SIRT1. To this purpose, hDPSCs were stimulated with different LPSs for 6, 24 and 48 h and then the gene expression was evaluated using quantitative real-time polymerase chain reaction (qPCR) and western blotting. Following stimulation with P. gingivalis and E. coli-derived LPSs, the relative mRNA and protein expression of all genes were significantly up-regulated in a time-dependent manner, as compared with unstimulated hDPSCs. Moreover, the hDPSCs stimulated with P. gingivalis LPS for 6 and 24 h had the highest mRNA expression of CDKN1A and SIRT1, respectively (p &lt; 0.0001), whereas the highest mRNA expression of CDKN2A and TP53 was seen in hDPSCs stimulated with E. coli LPS for 48 h (p &lt; 0.0001). In summary, because DPSCs have been reported to have therapeutic potential for several cell-based therapies, targeting molecular mechanisms aiming at preventing DPSC senescence could be considered a valuable strategy.

'Depending on where I am…' Hair, travelling and the performance of identity among Black and mixed-race women

A growing interdisciplinary literature examines the role of hair textures and styles in Black and mixed-race women's identity performances. Through an analysis of travel narratives, this paper extends and complements research on the context-dependency of racialized identity performances. This paper presents an analysis of 24 qualitative interviews with Black and mixed-race women in England and Germany. The question it seeks to answer is: 'How do changes in context alter Black and mixed-race women's hairstyling practices as a performance of identity?' Navigating a novel context could lead the women to (1) conform to local standards of beauty and femininity, (2) resist external expectations, (3) try out novel performances and (4) negotiate the complex performance of belonging. All in all, this paper shows that Black and mixed-race women dialogically re/negotiated and performatively re/created how they identify and how they are identified by others as they moved from one context to another.

Regulatory aspects on nanomedicines

Nanomedicines have been in the forefront of pharmaceutical research in the last decades, creating new challenges for research community, industry, and regulators. There is a strong demand for the fast development of scientific and technological tools to address unmet medical needs, thus improving human health care and life quality. Tremendous advances in the biomaterials and nanotechnology fields have prompted their use as promising tools to overcome important drawbacks, mostly associated to the non-specific effects of conventional therapeutic approaches. However, the wide range of application of nanomedicines demands a profound knowledge and characterization of these complex products. Their properties need to be extensively understood to avoid unpredicted effects on patients, such as potential immune reactivity. Research policy and alliances have been bringing together scientists, regulators, industry, and, more frequently in recent years, patient representatives and patient advocacy institutions. In order to successfully enhance the development of new technologies, improved strategies for research-based corporate organizations, more integrated research tools dealing with appropriate translational requirements aiming at clinical development, and proactive regulatory policies are essential in the near future. This review focuses on the most important aspects currently recognized as key factors for the regulation of nanomedicines, discussing the efforts under development by industry and regulatory agencies to promote their translation into the market. Regulatory Science aspects driving a faster and safer development of nanomedicines will be a central issue for the next years.

The state of the art of environmental valuation with discrete choice experiments

This paper provides with a review of the state of the art of environmental valuation with discrete choice experiments (DCEs). The growing body of literature on this field serves to emphasize the increasing role that DCEs are playing in environmental decision making in the last decade. The paper attempts to cover the full process of undertaking a choice experiment, including survey and experimental design, econometric analysis of choice data and welfare analysis. The research on this field is found to be intense, although many challenges are put forward (e.g. choice-task complexity and cognitive effort, experimental design, preference and scale heterogeneity, endogeneity or model uncertainty). Reviewing the state of the art of DCEs serves to draw attention to the main challenges that this methodological approach will need to overcome in the coming years and to identify the frontiers in discrete choice analysis. (C) 2010 Elsevier B.V. All rights reserved.

A Computational Approach to Packet Classification

Multi-field packet classification is a crucial component in modern software-defined data center networks. To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches; however, they do not scale well with the number of rules. We present a novel approach, NuevoMatch, which improves the memory scaling of existing methods. A new data structure, Range Query Recursive Model Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of the accesses to main memory with model inference computations. We describe an efficient training algorithm that guarantees the correctness of the RQ-RMI-based classification. The use of RQ-RMI allows the rules to be compressed into neural networks that fit into the hardware cache. Further, it takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector instructions, achieving a latency of tens of nanoseconds per lookup. Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geometric mean compression factor of 4.9x, 8x, and 82x, and average performance improvement of 2.4x, 2.6x, and 1.6x in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.

State of the art in nail dosimetry: free radicals identification and reaction mechanisms

Until very recently, analysis of bone biopsies by means of the method of electron paramagnetic resonance (EPR) collected after surgery or amputation has been considered as the sole reliable method for radiation dose assessment in hands and feet. EPR measurements in finger- and toenail have been considered for accident dosimetry for a long time. Human nails are very attractive biophysical materials because they are easy to collect and pertinent to whole body irradiation. Information on the existence of a radiation-induced signal in human nails has been reported almost 25 years ago. However, no practical application of EPR dosimetry on nails is known to date because, from an EPR perspective, nails represent a very complex material. In addition to the radiation-induced signal (RIS), parasitic and intense signals are induced by the mechanical stress caused when collecting nail samples (mechanically induced signals-MIS). Moreover, it has been demonstrated that the RIS stability is strongly influenced not only by temperature but also by humidity. Most studies of human nails were carried out using conventional X-band microwave band (9 GHz). Higher frequency Q-band (37 GHz) provides higher spectral resolution which allows obtaining more detailed information on the nature of different radicals in human nails. Here, we present for the first time a complete description of the different EPR signals identified in nails including parasitic, intrinsic and RIS. EPR in both X- and Q-bands was used. Four different MIS signals and five different signals specific to irradiation with ionizing radiation have been identified. The most important outcome of this work is the identification of a stable RIS component. In contrast with other identified (unstable) RIS components, this component is thermally and time stable and not affected by the physical contact of fingernails with water. A detailed description of this signal is provided here. The discovery of stable radiation-induced radical(s) associated with the RIS component mentioned opens a way for broad application of EPR dosimetry in human nails. Consequently, several recent dosimetry assessments of real accident cases have been performed based on the described measurements and analyses of this component.

What have we learnt from palaeoclimate simulations?

There has been a gradual evolution in the way that palaeoclimate modelling and palaeoenvironmental data are used together to understand how the Earth System works, from an initial and largely descriptive phase through explicit hypothesis testing to diagnosis of underlying mechanisms. Analyses of past climate states are now regarded as integral to the evaluation of climate models, and have become part of the toolkit used to assess the likely realism of future projections. Palaeoclimate assessment has demonstrated that changes in large-scale features of climate that are governed by the energy and water balance show consistent responses to changes in forcing in different climate states, and these consistent responses are reproduced by climate models. However, state-of-the-art models are still largely unable to reproduce observed changes in climate at a regional scale reliably. While palaeoclimate analyses of state-of-the-art climate models suggest an urgent need for model improvement, much work is also needed on extending and improving palaeoclimate reconstructions and quantifying and reducing both numerical and interpretative uncertainties. Copyright (C) 2016 The Authors. Journal of Quaternary Science Published by John Wiley & Sons Ltd.

Programming Multistable Metamaterials to Discover Latent Functionalities

Using multistable mechanical metamaterials to develop deployable structures, electrical devices, and mechanical memories raises two unanswered questions. First, can mechanical instability be programmed to design sensors and memory devices? Second, how can mechanical properties be tuned at the post-fabrication stage via external stimuli? Answering these questions requires a thorough understanding of the snapping sequences and variations of the elastic energy in multistable metamaterials. The mechanics of deformation sequences and continuous force/energy-displacement curves are comprehensively unveiled here. A 1D array, that is chain, of bistable cells is studied to explore instability-induced energy release and snapping sequences under one external mechanical stimulus. This method offers an insight into the programmability of multistable chains, which is exploited to fabricate a mechanical sensor/memory with sampling (analog to digital-A/D) and data reconstruction (digital to analog-D/A) functionalities operating based on the correlation between the deformation sequence and the mechanical input. The findings offer a new paradigm for developing programmable high-capacity read-write mechanical memories regardless of thei size scale. Furthermore, exotic mechanical properties can be tuned by harnessing the attained programmability of multistable chains. In this respect, a transversely multistable mechanical metamaterial with tensegrity-like bistable cells is designed to showcase the tunability of chirality.

The Disparities in Mental Health Between Gay and Bisexual Men Following Positive HIV Diagnosis in China: A One-Year Follow-Up Study

This study aimed to determine the change in mental health (depression and anxiety) among HIV-positive gay and bisexual men (GBM) one year after diagnosis and the disparities in trajectories of mental health between them. The potential factors contributing to the disparities were also investigated. This was a one-year follow-up study focusing on the mental health of newly diagnosed HIV-positive individuals. Participants rated their depression, anxiety, stress, and social support levels at baseline and one year later. Information on the utilization of mental healthcare and the initiation of antiretroviral therapy (ART) after diagnosis was collected at one-year follow-up. A total of 171 and 87 HIV-positive gay and bisexual men, respectively, completed two-time points surveys in this study. The depressive and anxiety symptoms experienced by HIV-positive GBM improvement one year after diagnosis. These improvements tended to be smaller in gay participants. Other factors including mental health care utilization and ART status during the one-year follow-up period, changes in social stress scores and objective social support scores were also associated with the changes in depression and anxiety, and all these factors, except for change in objective support, were found to be statistically different between HIV-positive GBM. Special attention should be given to the mental health of HIV-positive gay men. Promoting HIV-positive gay men to assess to mental health services and ART may be important for these populations to improve mental health. Enhancing social support and reducing stress levels may also be necessary for the vulnerable HIV-positive sexual minority groups.

Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules

In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life. Of all defects, cell-level anomalies can lead to serious failures and may affect surrounding PV modules in the long run. These fine defects are usually captured with high spatial resolution electroluminescence (EL) imaging. The difficulty of acquiring such images has limited the availability of data. For this work, multiple data resources and augmentation techniques have been used to surpass this limitation. Current state-of-the-art detection methods extract barely low-level information from individual PV cell images, and their performance is conditioned by the available training data. In this article, we propose an end-to-end deep learning pipeline that detects, locates and segments cell-level anomalies from entire photovoltaic modules via EL images. The proposed modular pipeline combines three deep learning techniques: 1. object detection (modified Faster-RNN), 2. image classification (EfficientNet) and 3. weakly supervised segmentation (autoencoder). The modular nature of the pipeline allows to upgrade the deep learning models to the further improvements in the state-of-the-art and also extend the pipeline towards new functionalities.

An ART-based fuzzy controller for the adaptive navigation of a quadruped robot

An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and the navigation mechanism are first described for the quadruped robot. The ART-based fuzzy controller, including an ART-based environment recognizer, a comparer, combined rule bases, and a fuzzy inferring mechanism, is next introduced for the purpose of the adaptive navigation of the quadruped robot. Unlike classical/conventional adaptive-fuzzy controllers, the present adaptive-control scheme is implemented by the adaptive selection of fuzzy-rule base in response to changes of the robot environment, which can be categorized and recognized by the proposed environment recognizer. The results of simulation and experiment show that the adaptive-fuzzy controller is effective.

A Multimode CMOS Vision Sensor With On-Chip Motion Direction Detection and Simultaneous Energy Harvesting Capabilities

This work presents a 128 x 128 multi-function CMOS vision sensor with motion direction detection (MDD) and simultaneous energy harvesting (EH) capability, featuring: 1) light weight template-based optical flow (OF) algorithm for on-chip MDD with high energy efficiency and small area overhead; and 2) independent imaging and energy harvesting (EH) operations using vertically stacked N+/PW and PW/DNW+DNW/PSUB junctions without photodiode reconfiguration. Fabricated in 0.18 mu m standard CMOS, this work demonstrates the first CMOS vision sensor featuring both MDD and EH modes within a compact pixel pitch of 4.75 mu m. With a power consumption of 10.5 mu W at 15fps in MDD mode, the chip prototype achieves a motion sensing figure-of-merit (MS-FoM) of 171 pJ/pixel.frame, which is 30% better than the state-of-the-art MDD sensors. The proposed EH configuration achieves continuous power generation at 267pW/Ix/mm(2) irrespective of the imaging/MDD operation, demonstrating a 1.56x improvement over prior arts.

Exploring Segment-Level Semantics for Online Phase Recognition From Surgical Videos

Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing methods ignored a pivotal problem that surgical phases should be classified by learning segment-level semantics instead of solely relying on frame-wise information. This paper presents a segment-attentive hierarchical consistency network (SAHC) for surgical phase recognition from videos. The key idea is to extract hierarchical high-level semantic-consistent segments and use them to refine the erroneous predictions caused by ambiguous frames. To achieve it, we design a temporal hierarchical network to generate hierarchical high-level segments. Then, we introduce a hierarchical segment-frame attention module to capture relations between the low-level frames and high-level segments. By regularizing the predictions of frames and their corresponding segments via a consistency loss, the network can generate semantic-consistent segments and then rectify the misclassified predictions caused by ambiguous low-level frames. We validate SAHC on two public surgical video datasets, i.e., the M2CAI16 challenge dataset and the Cholec80 dataset. Experimental results show that our method outperforms previous state-of-the-arts and ablation studies prove the effectiveness of our proposed modules. Our code has been released at: https://github.com/xmed-lab/SAHC.

Edge-preserving colour-to-greyscale conversion

This study introduces a colour-to-greyscale conversion method using colour clustering and multidimensional scaling that preserves region integrity, that is, it preserves edge structure without low-frequency distortion. Edges can be extracted from the resulting greyscale images and the results will be close to those obtainable from the original colour image. We compare with prior-art colour-to-greyscale conversion and a recent colour-edge extractor and show superior performance in each case.

Combining metadata and co-citations for recommending related papers

Identification of relevant documents is performed to keep track of the state-of-the-art methods and relies on research paper recommender systems. The proposed approaches for these systems can be classified into categories like content-based, collaborative filtering-based, and bibliographic information-based approaches. The content-based approaches exploit the full text of articles and provide more promising results than other approaches. However, most content is not freely available because of subscription requirements. Therefore, the scope of content-based approaches is limited. In such scenarios, the best possible alternative could be the exploitation of other openly available resources. Therefore, this research explores the possible use of metadata and bibliographic information to find related articles. The approach incorporates metadata with co-citations to find and rank related articles against a query paper. The similarity score of metadata fields is calculated and combined with co-citations. The proposed approach is evaluated on a newly constructed dataset of 5116 articles. The benchmark ranking against each co-cited document set is established by applying Jensen-Shannon divergence (JSD) and results are evaluated with the state-of-the-art content-based approach in terms of normalized discounted cumulative gain (NDCG). The state-of-the-art content-based approach achieved an NDCG score of 0.86 while the traditional co-citation-based approach scored 0.72. The presented method achieved NDCG scores of 0.73, 0.77, and 0.78 by incorporating the title, co-citation and title, and abstract, respectively, whereas the highest NDCG score of 0.77 was achieved by combining co-citations with metadata. However, better results are achieved by incorporating the title and abstract with NDCG score of 0.81. Therefore, it can be concluded that the proposed approach could be a better alternative in cases where content is unavailable.

A Multi-Hop Graph Neural Network for Event Detection via a Stacked Module and a Feedback Network

Event detection is an important subtask of information extraction, aiming to identify triggers and recognize event types in text. Previous state-of-the-art studies using graph neural networks (GNNs) are mainly applied to obtain long distance features of text and have achieved impressive performance. However, these methods face the issues of over-smoothing and semantic feature destruction, when containing multiple GNN layers. For the reasons, this paper proposes an improved GNN model for event detection. The model first proposes a stacked module to enrich node representation to alleviate the over-smoothing. The module aggregates multi-hop neighbors with different weights by stacking different GNNs in each hidden layer, so that the representation of nodes no longer tends to be similar. Then, a feedback network is designed with a gating mechanism to retain effective semantic information in the propagation process of the model. Finally, experimental results demonstrate that our model achieves competitive results in many indicators compared with state-of-the-art methods.

A generic reverse osmosis model for full-scale operation

Mathematical models can be a powerful tool in the operation of reverse osmosis (RO) facilities which is often challenged by a varying feed water quality. Most models, however, do not consider both full-scale and good modelling practice, which makes them less suited in practice. In this paper, a generic steady state model for RO was set-up and applied to a unique three-year data set from a full-scale RO process according to state-of-the-art good modelling practice. It was found that the model outputs are most sensitive towards the water and the solute permeability, and the feed spacer channel height, and therefore, only these parameters were calibrated. Furthermore, manufacturer's tests do not always reflect the full-scale situation, which highlights the importance of calibration. The model was validated with online conductivity data as input taking into account the uncertainty originating from online sensors, and compared to the commercial software Winflows. Despite the lack of long-term predictive power since fouling was not included, the model with online conductivity data as input showed satisfactory results, i.e. an average deviation from the data of 2.7%, 12.7%, 34.1% and 18.7% for respectively the recovery, the concentrate pressure, the permeate and concentrate solute concentration.

Cofired laminated ceramic package antenna for single-chip wireless transceivers

A novel concept for implementing art antenna ova art integrated circuit package is proposed for the single-chip solution of a wireless transceiver with the use of deep submicron CMOS technology. The prototype antenna printed on a 40-p-i-n dual-in-line cofired laminated ceramic package is experimentally studied at 3.45 GHz. The results show that the antenna has achieved a bandwidth of 25.1% and a gain of -2.0 dBi. (C) 2002 Wiley Periodicals, Inc.

Effects of Particulate Matter on the Risk of Gestational Hypertensive Disorders and Their Progression

Associations between particulate matter (PM) and gestational hypertensive disorders (GHDs) are well documented, but there is no evidence on the associations between PM and GHD progression, especially among those with assisted reproductive technology (ART) conceptions. To explore the effects of PM on the risk of GHDs and their progression among pregnant women with natural or ART conception, we enrolled 185,140 pregnant women during 2014-2020 in Shanghai and estimated the associations during different periods using multivariate logistic regression. During the 3 months of preconception, 10 mu g/m3 increases in PM concentrations were associated with increased risks of gestational hypertension (GH) (PM2.5: aOR = 1.076, 95% CI: 1.034-1.120; PM10: aOR = 1.042, 95% CI: 1.006-1.079) and preeclampsia (PM2.5: aOR = 1.064, 95% CI: 1.008-1.122; PM10: aOR = 1.048, 95% CI: 1.006-1.092 ) among women with natural conception. Furthermore, for women with ART conceptions who suffered current GHD, 10 mu g/m3 increases in PM concentrations in the third trimester elevated the risk of progression (PM2.5: aOR = 1.156, 95% CI: 1.022-1.306 ; PM10: aOR = 1.134, 95% CI: 1.013-1.270). In summary, women with natural conception should avoid preconceptional PM exposure to protect themselves from GH and preeclampsia. For women with ART conceptions suffering from GHD, it is necessary to avoid PM exposure in late pregnancy to prevent the disease from progressing.

ILLUMINATION IN RESTORATION WORKSHOPS: PRESSING CHALLENGES AND THEIR SOLUTIONS

Restoration workshops often stay in the periphery of museum life standing in the background of headline exhibitions. At the same time, restoration is an essential part of cultural heritage conservation. In this report, we would like to review the matters of formation of an optimal luminous environment using the workshops of the Grabar Art Conservation Centre as an example. The nature of light in a workshop is of primary importance for high quality of restoration work. We consider this case important since the Grabar Art Conservation Centre cooperates with many regional museums of Russia, and its experience may be interesting for museum practitioners.