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Deep Architectures and Ensembles for Semantic Video Classification

This paper addresses the problem of accurate semantic labeling of short videos. To this end, a multitude of three different deep nets, ranging from traditional recurrent neural 4 networks (LSTM, GRU), temporal agnostic networks (FV, VLAD, BoW), fully connected neural networks mid-stage AV fusion, and others were considered. Additionally, we also propose a residual architecture-based deep neural network (DNN) for video classification, with state-of-the-art classification performance at significantly reduced complexity. Furthermore, we propose four new approaches to diversity-driven multi-net ensembling, one based on fast correlation measure and three incorporating a DNN-based combiner. We show that significant performance gains can be achieved by ensembling diverse nets and we investigate factors contributing to high diversity. Based on the extensive YouTube8M dataset, we provide an in-depth evaluation and analysis of their behavior. We show that the performance of the ensemble is state-of-the-art achieving the highest accuracy on the YouTube8M Kaggle test data. The performance of the ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets, and show that the resulting method achieves comparable accuracy with the state-of-the-art methods using similar input features.

High voltage outdoor insulator surface condition evaluation using aerial insulator images

High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains. The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods. Two different types of classification approaches are compared: one method is based on neural networks (e.g. CNN, InceptionV3, MobileNet, VGG16, and ResNet50) and the other method is based on traditional machine learning classifiers (e.g. Bayes Net, Decision Tree, Lazy, Rules, and Meta classifiers). They are evaluated to discriminate the images of insulator surface exposed to freezing, wet, and snowing conditions. The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy. The classification of the insulator surfaces will assist in determining the insulator conditions, and take preventive measures for its protection.

Current advances in membrane technologies for produced water desalination

The limited clean water resource has urged the need to extract fresh water from non-conventional sources. Produced water (PW) is a potential source of fresh water, particularly for oil-producing countries experiencing water stress issue. The engagement of membrane-based desalination technology in the field of PW treatment brings about new possibility of water reclamation. This review highlights the state-of-the-art membrane-based technologies including both stand-alone and their hybrid system, for PW treatment and reclamation. While reverse osmosis continues as a reliable option for PW treatment, emerging technologies like forward osmosis and membrane distillation have attained increasing attentions from the desalination community. To pave a way for practical applications of membrane technologies for PW desalination, the recent advances in term of membrane properties improvement, operating condition optimization, and membrane cleaning protocols are reviewed. Finally, the challenges and future outlooks of current PW desalination technologies are also highlighted.

Impact of Definitive Surgery for Graves' Disease on Adolescent Disease-Specific Quality of Life and Psychosocial Functioning

Introduction: Pediatric Graves' disease (GD) is associated with hyperthyroid symptoms that impact psychosocial and physical functioning. Total thyroidectomy (TT) is a definitive treatment option that replaces antithyroid medication. While studies have examined health-related quality of life (QOL) in adults, there are no data describing impacts of TT in pediatrics. In this prospective longitudinal study, we explored the impact of TT on disease-specific QOL and satisfaction with TT and scar appearance in adolescent patients with GD undergoing TT. Methods: Patients 12-19 years old pursuing TT for GD and their parents were recruited to complete surveys before and at least 6 months after TT. Surveys assessed motivations for pursuing TT, QOL, perceived stigmatization, self-esteem, scar appearance, and surgery satisfaction. Paired scores were compared using Wilcoxon signed-rank tests, and subscore associations were assessed using Spearman association tests. Results: Thirty-seven patient-parent dyads completed baseline surveys, including 20 patient-parent dyads completing pre- and post-TT surveys. At baseline, patients reported physical and cognitive symptomology, including tiredness, anxiety, and emotional susceptibility through ThyPRO. Psychosocial functioning at school was low through PedsQL. Disease-specific QOL significantly improved after TT, with notable improvements associated with resolution of goiter (median change = -26.14, p = 0.003), hyperthyroid symptoms (median change = -43.75, p = 0.002), tiredness (median change = -26.79, p = 0.017), cognitive impairment (median change = -14.58, p = 0.035), anxiety (median change = -33.33, p = 0.010), and emotional susceptibility (median change = -28.99, p = 0.035). Physical (median change = 18.75, p = 0.005) and school-related functioning (median change = 30.00, p = 0.002) also significantly improved post-TT. Reported GD-associated eye symptomology (thyroid eye disease) was the second lowest scoring ThyPRO subscore at baseline and improved after surgery (median change = 14.06, p = 0.03). Families reported median recovery by two months, high satisfaction with the outcomes of TT, and minimal concerns over scar appearance. No permanent surgical complications (i.e., recurrent laryngeal nerve damage or hypoparathyroidism) were sustained. Conclusions: In the setting of a high-volume surgeon with low complication rates, TT for GD in pediatric populations may have substantial beneficial effects on disease-specific QOL and psychosocial functioning, with minimal adverse complaints about scar appearance.

Collocation for Diffeomorphic Deformations in Medical Image Registration

Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is invertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms. We present CDD (Collocation for Diffeomorphic Deformations), a numerical solution to diffeomorphic image registration, which solves for the Stationary Velocity Field (SVF) using an implicit A-stable collocation method. CDD guarantees the preservation of the diffeomorphic properties at all discrete points and is thereby consistent to machine precision. We compared CDD's collocation method with the following standard methods: Scaling and Squaring, Forward Euler, and Runge-Kutta 4, and found that CDD is up to 9 orders of magnitude more consistent. Finally, we evaluated CDD on a number of standard bench-mark data sets and compared the results with current state-of-the-art methods: SPM-DARTEL, Diffeomorphic Demons and SyN. We found that CDD outperforms state-of-the-art methods in consistency and delivers comparable or superior registration precision.

A generic framework for hierarchical de novo protein design

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold. Here, we built on the TopoBuilder (TB) de novo design method, to automatically assemble structural templates with native-like features starting from string descriptors that capture the overall topology of proteins. Our framework eliminates the dependency of hand-crafted and fold-specific rules through an iterative, data-driven approach that extracts geometrical parameters from structural tertiary motifs. We evaluated the TopoBuilder framework by designing sequences for a set of five protein folds and experimental characterization revealed that several sequences were folded and stable in solution. The TopoBuilder de novo design framework will be broadly useful to guide the generation of artificial proteins with customized geometries, enabling the exploration of the protein universe.

Advances in large-area Hg1-xCdxTe photovoltaic detectors for remote-sensing applications

State-of-the-art large-area photovoltaic (PV) detectors fabricated in HgCdTe grown by molecular beam epitaxy (MBE) have been demonstrated for the Crosstrack Infrared Sounder (CrIS) instrument. Large-area devices (1 mm in diameter) yielded excellent electrical and optical performance operating at 81 K for lambda(c) similar to 15 mum, at 98 K for lambda(c) similar to 9 mum, and lambda(c) similar to 5-mum spectral cutoffs. Fabricated detectors have near-theoretical electrical performance, and Anti Reflection coated quantum efficiency (QE) is greater than 0.70. Measured average R(0)A at 98 K is 2.0E7 Omegacm(2), and near-theoretical QEs greater than 0.90 were obtained on detectors with lambda(c) similar to 5-mum spectral cutoffs. These state-of-the-art large-area PV detector results reflect high-quality HgCdTe grown by MBE on CdZnTe substrates in all three spectral bands of interest.

BP-EVD: Forward Block-Output Propagation for Efficient Video Denoising

Denoising videos in real-time is critical in many applications, including robotics and medicine, where varying-light conditions, miniaturized sensors, and optics can substantially compromise image quality. This work proposes the first video denoising method based on a deep neural network that achieves state-of-the-art performance on dynamic scenes while running in real-time on VGA video resolution with no frame latency. The backbone of our method is a novel, remarkably simple, temporal network of cascaded blocks with forward block output propagation. We train our architecture with short, long, and global residual connections by minimizing the restoration loss of pairs of frames, leading to a more effective training across noise levels. It is robust to heavy noise following Poisson-Gaussian noise statistics. The algorithm is evaluated on RAW and RGB data. We propose a denoising algorithm that requires no future frames to denoise a current frame, reducing its latency considerably. The visual and quantitative results show that our algorithm achieves state-of-the-art performance among efficient algorithms, achieving from two-fold to two-orders-of-magnitude speed-ups on standard benchmarks for video denoising.

Ensemble of deep transfer learning models for real-time automatic detection of face mask

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask has become an adequate protection solution many governments adopt. Manual real-time monitoring of face mask wearing for many people is becoming a difficult task. This paper applies three heterogeneous deep transfer learning models, viz., ResNet50, Inception-v3, and VGG-16, to prepare an ensemble classification model for detecting whether a person is wearing a mask. The ensemble classification model is underlined by the concept of the weighted average technique. The proposed framework is based on two phases. An off-line phase that aims to prepare a classification model by following training-testing steps to detect and locate facemasks. Then in the second online phase, it is deployed to detect real-time faces from live videos, which are captured by a web-camera. The prepared model is compared with several state-of-the-art models. The proposed model has achieved the highest classification accuracy of 99.97%, precision of 0.997, recall of 0.997, F1-score of 0.997 and kappa coefficient 0.994. The superiority of the model over state-of-the-art compared methods is well evident from the experimental results.

Willingness to pay for policies to reduce health risks from COVID-19: Evidence from U.S. professional sports

Airborne transmission of the COVID-19 virus increased the need for health policies to reduce transmission in congregate settings associated with minimal risk before the pandemic. While a large literature estimates tradeoffs between policies designed to reduce negative health outcomes, no empirical research addresses consumer willingness to pay (WTP) for health policies designed to reduce airborne virus transmission. Using survey data from 1381 fans of professional sports, we estimate consumers' WTP for reduced likelihood of coronavirus transmission through mask and social distancing policies using a stated preference approach. The results indicate increased attendance likelihood if the venue requires masks and limits attendance, with significant heterogeneity in WTP across risk scenarios and sports. We characterize consumers as casual fans who prefer a mask requirement but are indifferent to capacity constraints, strong fans who are anti-maskers and prefer capacity constraints, and a second group of casual fans with positive WTP under both mask and limited capacity requirements. For example, casual fans' WTP for masking, $38 per National Basketball Association (NBA) game attended, is more than double their WTP for capacity constraints only. Strong fans' WTP for attending capacity constrained NBA games was $490, more than 400% higher than the pre-pandemic average WTP of $105.

Analyzing Dynamical Brain Functional Connectivity as Trajectories on Space of Covariance Matrices

Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain regions, as a trajectory on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs). We use a recently developed metric on the space of SPDMs for quantifying differences across FC observations, and for clustering and classification of FC trajectories. To facilitate large scale and high-dimensional data analysis, we propose a novel, metric-based dimensionality reduction technique to reduce data from large SPDMs to small SPDMs. We illustrate this comprehensive framework using data from the Human Connectome Project (HCP) database for multiple subjects and tasks, with task classification rates that match or outperform state-of-the-art techniques.

Divergent semantic integration (DSI): Extracting creativity from narratives with distributional semantic modeling

We developed a novel conceptualization of one component of creativity in narratives by integrating creativity theory and distributional semantics theory. We termed the new construct divergent semantic integration (DSI), defined as the extent to which a narrative connects divergent ideas. Across nine studies, 27 different narrative prompts, and over 3500 short narratives, we compared six models of DSI that varied in their computational architecture. The best-performing model employed Bidirectional Encoder Representations from Transformers (BERT), which generates context-dependent numerical representations of words (i.e., embeddings). BERT DSI scores demonstrated impressive predictive power, explaining up to 72% of the variance in human creativity ratings, even approaching human inter-rater reliability for some tasks. BERT DSI scores showed equivalently high predictive power for expert and nonexpert human ratings of creativity in narratives. Critically, DSI scores generalized across ethnicity and English language proficiency, including individuals identifying as Hispanic and L2 English speakers. The integration of creativity and distributional semantics theory has substantial potential to generate novel hypotheses about creativity and novel operationalizations of its underlying processes and components. To facilitate new discoveries across diverse disciplines, we provide a tutorial with code (osf.io/ath2s) on how to compute DSI and a web app ( osf.io/ath2s ) to freely retrieve DSI scores.

Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition

Local-feature-based face recognition (FR) methods, such as Gabor features encoded by local binary pattern, could achieve state-of-the-art FR results in large-scale face databases such as FERET and FRGC. However, the time and space complexity of Gabor transformation are too high for many practical FR applications. In this paper, we propose a new and efficient local feature extraction scheme, namely monogenic binary coding (MBC), for face representation and recognition. Monogenic signal representation decomposes an original signal into three complementary components: amplitude, orientation, and phase. We encode the monogenic variation in each local region and monogenic feature in each pixel, and then calculate the statistical features (e.g., histogram) of the extracted local features. The local statistical features extracted from the complementary monogenic components (i.e., amplitude, orientation, and phase) are then fused for effective FR. It is shown that the proposed MBC scheme has significantly lower time and space complexity than the Gabor-transformation-based local feature methods. The extensive FR experiments on four large-scale databases demonstrated the effectiveness of MBC, whose performance is competitive with and even better than state-of-the-art local-feature-based FR methods.

Acoustic Resonance Testing of Small Data on Sintered Cogwheels

Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.

Expansion and Neofunctionalization of Actinoporin-like Genes in Mediterranean Mussel (Mytilus galloprovincialis)

Pore-forming toxins are an important component of the venom of many animals. Actinoporins are potent cytolysins that were first detected in the venom of sea anemones; however, they are occasionally found in animals other than cnidarians and are expanded in a few predatory gastropods. Here, we report the presence of 27 unique actinoporin-like genes with monophyletic origin in Mytilus galloprovincialis, which we have termed mytiporins. These mytiporins exhibited a remarkable level of molecular diversity and gene presence-absence variation, which warranted further studies aimed at elucidating their functional role. We structurally and functionally characterized mytiporin-1 and found significant differences from the archetypal actinoporin fragaceatoxin C. Mytiporin-1 showed weaker permeabilization activity, no specificity towards sphingomyelin, and weak activity in model lipid systems with negatively charged lipids. In contrast to fragaceatoxin C, which forms octameric pores, functional mytiporin-1 pores on negatively charged lipid membranes were hexameric. Similar hexameric pores were observed for coluporin-26 from Cumia reticulata and a conoporin from Conus andremenezi. This indicates that also other molluscan actinoporin-like proteins differ from fragaceatoxin C. Although the functional role of mytiporins in the context of molluscan physiology remains to be elucidated, the lineage-specific gene family expansion event that characterizes mytiporins indicates that strong selective forces acted on their molecular diversification. Given the tissue distribution of mytiporins, this process may have broadened the taxonomic breadth of their biological targets, which would have important implications for digestive processes or mucosal immunity.

Bright versus dim ambient light affects subjective well-being but not serotonin-related biological factors

Light falling on the retina is converted into an electrical signal which stimulates serotonin synthesis. Previous studies described an increase of plasma and CNS serotonin levels after bright light exposure. Ghrelin and leptin are peptide hormones which are involved in the regulation of hunger/satiety and are related to serotonin. Neopterin and kynurenine are immunological markers which are also linked to serotonin biosynthesis. In this study, 29 healthy male volunteers were exposed to bright (5000lx) and dim (50lx) light conditions for 120min in a cross-over manner. Subjective well-being and hunger as well as various serotonin associated plasma factors were assessed before and after light exposure. Subjective well-being showed a small increase under bright light and a small decrease under dim light, resulting in a significant interaction between light condition and time. Ghrelin concentrations increased significantly under both light conditions, but there was no interaction between light and time. Correspondingly, leptin decreased significantly under both light conditions. Hunger increased significantly with no light-time interaction. We also found a significant decrease of neopterin, tryptophan and tyrosine levels, but no interaction between light and time. In conclusion, ambient light was affecting subjective well-being rather than serotonin associated biological factors.

A Survey on Adaptive Random Testing

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.

'Don't think that we die from AIDS': Invisibilised uncertainty and global transgender health

The invisibilisation of social groups in health research and survey data is a source of medical uncertainty, long seen as a hallmark of the medical field. However, scholarship has not thoroughly assessed how medical uncertainty is structured by state-level processes and global health agendas, especially for people beyond the Global North. This article introduces invisibilised uncertainty as a type of medical uncertainty structured by global organisational and state-level priorities, which can invisibilise social groups and health problems from research and data collection, exacerbating medical uncertainty and health disparities for people worldwide. Based on 14 months of fieldwork in Thailand and in-depth interviews with 62 participants, the article illuminates how state-level processes and global clinical research agendas have structured knowledge gaps and uncertainties for Thai transgender women. As omissions in health research and data collection become embodied on a world scale, the article expands our understandings of how gendered health disparities are structured nationally and globally. It advances a sociology of medical ignorance by analysing the uneven landscape of holistic transgender health research, parsing how institutional dynamics can prioritise or invisibilise people and health issues in research and data, and structure uncertainties.

Use of Telehealth During the COVID-19 Pandemic Among Practicing Maternal-Fetal Medicine Clinicians

Background: Limited knowledge exists about the drivers of telehealth use among obstetricians during COVID-19 in the United States. We investigated the use of live video visits by Maternal-Fetal Medicine (MFM) clinicians, the factors associated with use and interest in future use. Methods: We drew survey data from 373 clinicians on two outcomes: (1) use of any (vs. no) live video visits during COVID-19 and (2) among users, the extent of live video use. Bivariate and multivariate logistic regressions quantified the association between predisposing (demographic and practice setting characteristics) and enabling factors (prepandemic telehealth use, structural and perceived patient barriers) and each outcome. Results: During the pandemic, 88% reported any use, a jump from 29% prepandemic utilization. Users (vs. nonusers) were younger (p = 0.02); tended to provide comprehensive prenatal care (p = 0.01) and/or inpatient care (p = 0.02), practice in university settings (p = 0.01), engage in various telehealth modalities prepandemic (p ≤ 0.01), and to perceive challenges with technical (p < 0.01), reimbursement (p = 0.05), and patient barriers to internet or data plan access (p ≤ 0.001). After adjusting for covariates, only prepandemic communication through patient portal (adjusted odds ratio [aOR] = 3.85; 95% confidence interval [CI] = 1.33-11.12), perceived patient access barriers (aOR = 5.27; 95% CI = 1.95-14.23), and practice in multiple versus university settings (aOR = 0.18; 95% CI = 0.06-0.56) remained significantly associated with use. Approximately 44% were high users. Prepandemic ultrasound use (aOR = 1.92; 95% CI = 1.17-3.16), perceived patient access barriers (aOR = 1.85; 95% CI = 1.12-3.06) and Midwest versus North practice location (aOR = 0.46; 95% CI = 0.21-0.98) predicted high use. Among high users, 99% wanted to continue offering video visits. Conclusions: We found widespread use of live video obstetric care by MFM clinicians and continued interest in use postpandemic.

A Self-Play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario

Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing focus on truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, the integrated labels will be used to fine-tune machine learning models. Particularly, in this paper, we change the target of truth inference in crowdsourcing from discrete labels to multiple comments given by online participants, that is, the integration of the crowdsourced comments. For such a goal, we propose a Self-play and Sentiment-Emphasized Comment Integration Framework (SSECIF), based on deep Q-learning, with three unique features. First, our framework SSECIF can generate the comment integration in a totally self-play way, without relying on the ground truth generated by human effort. Second, the integrated comment generated by SSECIF can include salient content with low redundancy. Third, the proposed framework SSECIF has emphasized, with a higher intensity, the sentiment in the integrated comment, in order to reflect the attitude or opinion more obviously. Extensive evaluation on real-world datasets demonstrates that SSECIF has achieved the best overall performance in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.

Exploiting Lightweight Statistical Learning for Event-Based Vision Processing

This paper presents a lightweight statistical learning framework potentially suitable for low-cost event-based vision systems, where visual information is captured by a dynamic vision sensor (DVS) and represented as an asynchronous stream of pixel addresses (events) indicating a relative intensity change on those locations. A simple random ferns classifier based on randomly selected patch-based binary features is employed to categorize pixel event flows. Our experimental results demonstrate that compared to existing event-based processing algorithms, such as spiking convolutional neural networks (SCNNs) and the state-of-the-art bag-of-events (BoE)-based statistical algorithms, our framework excels in high processing speed (2x faster than the BoE statistical methods and >100x faster than previous SCNNs in training speed) with extremely simple online learning process, and achieves state-of-the-art classification accuracy on four popular address-event representation data sets: MNIST-DVS, Poker-DVS, Posture-DVS, and CIFAR10-DVS. Hardware estimation shows that our algorithm will be preferable for low-cost embedded system implementations.

Total generalized variation and shearlet transform based Poissonian image deconvolution

Integrating the advantages of total generalized variation and shearlet transform, this article introduces a hybrid regularizers scheme for deconvolving Poissonian image. Computationally, a highly efficient alternating minimization algorithm associated with variable splitting approach is described to obtain the optimal solution in detail. Illustrationally, in comparison with several current state-of-the-art numerical methods, numerical simulations consistently demonstrate the outstanding performance of our proposed approach to deblurring Poissonian image, in terms of both restoration accuracy and feature-preserving ability.

Sustainable chemical technologies in production of clean fuels from fossil fuels

Some aspects of the present and the possible future role of sustainable chemical technologies in the production of clean liquid and gaseous fuels from fossil fuels are discussed. The state-of-the-art and the vision of possible sources and alternative routes which may lead to clean fuels from fossil fuels due to the progress in crude oil, natural gas and coal processing are briefly presented. The possible future role of the Fischer-Tropsch synthesis, methanol synthesis, dimethylether synthesis, and a group of methanol transformation processes is also discussed.

Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling

We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891 +/- 0.016 (mean +/- std) and an average symmetric surface distance (ASD) of 0.994 +/- 0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 +/- 0.031 DC and 1.556 +/- 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.

A critical review on microbial ecology in the novel biological nitrogen removal process: Dynamic balance of complex functional microbes for nitrogen removal

The novel biological nitrogen removal process has been extensively studied for its high nitrogen removal efficiency, energy efficiency, and greenness. A successful novel biological nitrogen removal process has a stable microecological equilibrium and benign interactions between the various functional bacteria. However, changes in the external environment can easily disrupt the dynamic balance of the microecology and affect the activity of functional bacteria in the novel biological nitrogen removal process. Therefore, this review focuses on the microecology in existing the novel biological nitrogen removal process, including the growth characteristics of functional microorganisms and their interactions, together with the effects of different influencing factors on the evolution of microbial communities. This provides ideas for achieving a stable dynamic balance of the microecology in a novel biological nitrogen removal process. Furthermore, to investigate deeply the mechanisms of microbial interactions in novel biological nitrogen removal process, this review also focuses on the influence of quorum sensing (QS) systems on nitrogen removal microbes, regulated by which bacteria secrete acyl homoserine lactones (AHLs) as signaling molecules to regulate microbial ecology in the novel biological nitrogen removal process. However, the mechanisms of action of AHLs on the regulation of functional bacteria have not been fully determined and the composition of QS system circuits requires further investigation. Meanwhile, it is necessary to further apply molecular analysis techniques and the theory of systems ecology in the future to enhance the exploration of microbial species and ecological niches, providing a deeper scientific basis for the development of a novel biological nitrogen removal process.

Antitumor efficacy of extracellular complexes with gadolinium in Binary Radiotherapy

In this report the efficacy of extracellular pharmaceutical Gd-DTPA in Binary Radiotherapy was studied. The study was carried out in mice bearing transplantable adenocarcinoma Ca755 using X-ray based contrast enhanced radiotherapy as a practical implementation of Binary Radiotherapy. It was shown that intravenous administration of 0.3 ml of 0.5 M water solution of Gd-DTPA followed by X-irradiation at a dose of 10 Gy provides T/C%=10±3% and leads to complete tumor regression in 25% of mice.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

A comparison of radionuclide dispersion model performances for the Baltic Sea and Fukushima releases in the Pacific Ocean

State-of-the art dispersion models were applied to simulate Cs-137 dispersion from Chernobyl nuclear power plant disaster fallout in the Baltic Sea and from Fukushima Daiichi nuclear plant releases in the Pacific Ocean after the 2011 Tsunami. Models were of different nature, from box to full three-dimensional models, and included water/sediment interactions. Agreement between models was very good in the Baltic. In the case of Fukushima, results from models could be considered to be in acceptable agreement only after a model harmonization process consisting of using exactly the same forcing (water circulation and parameters) in all models.

Adaptive Pairing Reversible Watermarking

This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013. An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced. This adaptive approach provides an increased number of pixel pairs where both pixels are embedded and decreases the number of shifted pixels. The adaptive pairwise reversible watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.

KEY FACTORS IN SUCCESSFUL TEACHING OF SPACE ENVIRONMENT DESIGN UNDER INHERITANCE AND INNOVATION - TAKING THE CASE OF LACQUER ARTS TEACHING

The occurrence of Covid-19 pandemic had made it impossible for students to produce lacquer arts works from the school workshops. To address this issue and to respond to the appeal of Suspending Classes, but not Learning, Internet was made handy to set up situational teaching for spatial environment design. In this way, path for online teaching is explored, and creation passion of students are stimulated, challenges of courses exigency are resolved and great teaching effects had been accomplished. Taking the lacquer teaching forces in Beijing as the study subjects, this research get to know weights of each factor and its influences over teaching of space environment design. In a randomly manner, the subjects are handed out questionnaires. A total of 250 questionnaires are handed out and 183 ones are collected back, with a recovery rate of 83%. Through participation of space environment design teaching, it broke down the restrictions in time and space. Interests of learning on students were simulated, thinking approaches of creativity was widened, and the creation foundation was made solid, achieving the goal of enriching inheritance channels for lacquer arts inheritance.

A Novel Learning Based Non-Lambertian Photometric Stereo Method for Pixel-Level Normal Reconstruction of Polished Surfaces

High-quality reconstruction of polished surfaces is a promising yet challenging task in the industrial field. Due to its extreme reflective properties, state-of-the-art methods have not achieved a satisfying trade-off between retaining texture and removing the effects of specular outliers. In this paper, we propose a learning based pixel-level photometric stereo method to estimate the surface normal. A feature fusion convolutional neural network is used to extract the features from the normal map solved by the least square method and from the original images respectively, and combine them to regress the normal map. The proposed network outperforms the state-of-the-art methods on the DiLiGenT benchmark dataset. Meanwhile, we use the polished rail welding surface to verify the generalization of our method. To fit the complex geometry of the rails, we design a flexible photometric stereo information collection hardware with multi-angle lights and multi-view cameras, which can collect the light and shade information of the rail surface for photometric stereo. The experimental results indicate that the proposed method is able to reconstruct the normal of the polished surface at the pixel level with abundant texture information.

Knowledge distillation methods for efficient unsupervised adaptation across multiple domains

Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-world applications. For instance, in person re-identification, videos are captured over a distributed set of cameras with non-overlapping viewpoints. The shift between the source (e.g. lab setting) and target (e.g. cameras) domains may lead to a significant decline in recognition accuracy. Additionally, state-of-the-art CNNs may not be suitable for such real-time applications given their computational requirements. Although several techniques have recently been proposed to address domain shift problems through unsupervised domain adaptation (UDA), or to accelerate/compress CNNs through knowledge distillation (KD), we seek to simultaneously adapt and compress CNNs to generalize well across multiple target domains. In this paper, we propose a progressive KD approach for unsupervised single target DA (STDA) and multi-target DA (MTDA) of CNNs. Our method for KD-STDA adapts a CNN to a single target domain by distilling from a larger teacher CNN, trained on both target and source domain data in order to maintain its consistency with a common representation. This method is extended to address MTDA problems, where multiple teachers are used to distill multiple target domain knowledge to a common student CNN. A different target domain is assigned to each teacher model for UDA, and they alternatively distill their knowledge to the student model to preserve specificity of each target, instead of directly combining the knowledge from each teacher using fusion methods. Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets. It is also compared against stateof-the-art methods for MTDA on Digits, Office31, and OfficeHome. In both settings ? KD-STDA and KD-MTDA ? results indicate that our approach can achieve the highest level of accuracy across target domains, while requiring a comparable or lower CNN complexity. ? 2021 Published by Elsevier B.V.

LIGHT DESIGN IN RUSSIAN UNIVERSITIES: WHAT AND HOW TO TEACH?

Light design as a prominent and self-sufficient branch of design has appeared and developed abroad and is a rather new part of Russian design practice. Along with solving practical problems, it is time to think on the aspects of training of light design specialists in Russian design universities. Engineering and technical, and partially artistic aspects in light design education, have international nature to a great extent, but the new specialty has been acquiring its own distinctions in Russia, which are mainly related to aesthetical problems of the profession and specific understanding of the cultural and historical context. These national distinctions have first appeared in practice and theoretical studies and then they began being translated into the educational sphere, turning into authorial education programmes and techniques and predetermining selection of design problems and their implementation methods. Light design implies training of interdisciplinary specialists, which requires a comprehensive approach to development of the educational process including innovative-technological as well as humanitarian sections, forming an unconventional model of the educational process. The Environment Design sub-department of Moscow State Stroganov Academy of Design and Applied Arts, the oldest Russian art and industry school, has gained a rich experience in artistic design in the field of light design, which comprises all leading professional trends. The methods of conventional artistic design, computer modelling, and animation skills as well as the knowledge included in the new discipline "The Basics of Light Design" have allowed developing of term and graduation projects having consideration for specific practical requirements and aiming at their future implementation.

Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of- the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.

Deep Neural Networks for Chronological Age Estimation From OPG Images

Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are proposed. The first (DANet) consists of a sequential Convolutional Neural Network (CNN) path to predict the age, while the second (DASNet) adds a second CNN path to predict the sex and uses sex-specific features with the aim of improving the age prediction performance. Both methods were tested on a set of 2289 OPG images of subjects from 4.5 to 89.2 years old, where both bad radiological quality images and images showing conditioning dental characteristics were not discarded. The results showed that the DASNet outperforms the DANet in every aspect, reducing the median Error (E) and the median Absolute Error (AE) by about 4 months in the entire database. When evaluating the DASNet in the reduced datasets, the AE values decrease as the real age of the subjects decreases, until reaching a median of about 8 months in the subjects younger than 15. The DASNet method was also compared to the state-of-the-art manual age estimation methods, showing significantly less over- or under-estimation problems. Consequently, we conclude that the DASNet can be used to automatically predict the chronological age of a subject accurately, especially in young subjects with developing dentitions.

Spray-Dried Photonic Balls with a Disordered/Ordered Hybrid Structure for Shear-Stress Indication

Optical microscale shear-stress indicator particles are of interest for the in situ recording of localized forces, e.g., during 3D printing or smart skins in robotic applications. Recently developed particle systems are based on optical responses enabled by integrated organic dyes. They thus suffer from potential chemical instability and cross-sensitivities toward humidity or temperature. These drawbacks can be circumvented using photonic balls as shear-stress indicator particles, which employ structural color as the element to record forces. Here, such photonic balls are prepared from silica and iron oxide nanoparticles via the scalable and fast spray-drying technique. Process parameters to create photonic balls with a disordered core and an ordered particle structure toward the exterior of the supraparticles are reported. This hybrid disordered-ordered structure is responsible for a color loss of the indicator particles during shear-stress application because of irreversible structural destruction. By adjusting the primary silica particle sizes, nearly all colors of the visible spectrum can be achieved and the sensitivity of the response to shear stress can be adjusted.

Complete mitochondrial genome of Parastichopus californicus (Aspidochirotida: Stichopodidae)

In this study, we first determined and described the complete mitogenome sequence of Parastichopus californicus, which was 16 727 bp in length. The mitochondrial genome had the canonical mitochondrial gene content, including 13 protein-coding genes, 2 rRNA genes and 22 tRNA genes. The overall base composition of the heavy-strand was 31.5% A, 18 % G, 20.6% C and 29.9% T, with a high A + T content of 61.4%. ML phylogenetic tree indicated that P. californicus and P. nigripunctatus were clustered in one branch belonging to the genus Parastichopus. This conclusion was identical to the former result by the methods of morphological taxonomy.

An Entropic Optimal Transport loss for learning deep neural networks under label noise in remote sensing images

Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a challenging and tedious task in most scenarios of remote sensing image analysis, thus cheap surrogate procedures are employed to label the dataset. Training deep neural networks on such datasets with inaccurate labels easily overfits to the noisy training labels and degrades the performance of the classification tasks drastically. To mitigate this effect, we propose an original solution with entropic optimal transportation. It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples. We empirically demonstrate on several remote sensing datasets, where both scene and pixel-based hyperspectral images are considered for classification. Our method proves to be highly tolerant to significant amounts of label noise and achieves favorable results against state-of-the-art methods.

Characterisation of particulate matter in the Royal Museum of Fine Arts, Antwerp, Belgium

Aerosol samples were collected during two campaigns in February and July 1999 both inside and outside the Koninklijk Museum voor Schone Kunsten (KMSK, Royal Museum of Fine Arts) in Antwerp. Bulk aerosol concentrations, as well as the composition of the individual particles, were determined. The influence of the outdoor aerosol was clearly visible. In winter, restoration and construction works constituted an additional indoor source of Ca-rich and Ca-Si particles. Along with sea salt, these were the main particle types identified in this season. In summer, S-rich particles were most frequent. The summer abundances of Ca-rich particles remained low, even though the museum is situated in a limestone building. Moreover, dry deposition samples were collected in order to determine what amount of particles could actually be deposited onto the works of art. (C) 2002 Elsevier Science Ltd. All rights reserved.

Subunit composition, molecular environment, and activation of native TRPC channels encoded by their interactomes

In the mammalian brain TRPC channels, a family of Ca2+-permeable cation channels, are involved in a variety of processes from neuronal growth and synapse formation to transmitter release, synaptic transmission and plasticity. The molecular appearance and operation of native TRPC channels, however, remained poorly understood. Here, we used high-resolution proteomics to show that TRPC channels in the rodent brain are macro-molecular complexes of more than 1 MDa in size that result from the co-assembly of the tetrameric channel core with an ensemble of interacting proteins (interactome). The core(s) of TRPC1-, C4-, and C5-containing channels are mostly heteromers with defined stoichiometries for each subtype, whereas TRPC3, C6, and C7 preferentially form homomers. In addition, TRPC1/C4/C5 channels may co-assemble with the metabotropic glutamate receptor mGluR1, thus guaranteeing both specificity and reliability of channel activation via the phospholipase-Ca2+ pathway. Our results unveil the subunit composition of native TRPC channels and resolve the molecular details underlying their activation.

Integrative biohydrogen- and biomethane-producing bioprocesses for comprehensive production of biohythane

The production of biohythane, a combination of energy-dense hydrogen and methane, from the anaerobic digestion of low-cost organic wastes has attracted attention as a potential candidate for the transition to a sustainable circular economy. Substantial research has been initiated to upscale the process engineering to establish a hythane-based economy by addressing major challenges associated with the process and product upgrading. This review provides an overview of the feasibility of biohythane production in various anaerobic digestion systems (single-stage, dual-stage) and possible technologies to upgrade biohythane to hydrogen-enriched renewable natural gas. The main goal of this review is to promote research in biohythane production technology by outlining critical needs, including meta-omics and metabolic engineering approaches for the advancements in biohythane production technology.

An Energy-Efficient High CSNR XNOR and Accumulation Scheme For BNN

In this brief, we present an energy-efficient and high compute signal-to-noise ratio (CSNR) XNOR and accumulation (XAC) scheme for binary neural networks (BNNs). Transmission gates achieve a large compute signal margin (CSM) and high CSNR for accurate XAC operation. The 10T1C XNOR SRAM bit-cell performs the in-memory XAC operation without pre-charging the larger bitline capacitances and significantly reducing energy consumption per XAC operation. The validation of the proposed XAC scheme is done through the post-layout simulations in 65nm CMOS technology with V-DD of 1 V. The achieved 1 ns of latency and 2.36 fJ of energy consumption per XAC operation are (7.2 x , 7.2 x ) and (2 x , 1.31 x ) lower than state-of-the-art digital and analog compute in-memory (CIM) XAC schemes respectively. The proposed XAC design achieves 8.6 x improvement in figure-of-merit (FoM), over prior state-of-the-art. Moreover, (sigma/mu) average of 0.2% from Monte Carlo simulations show that proposed XAC scheme is robust against systematic mismatch and process variations.

Sparsity-Driven Despeckling for SAR Images

Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the result of various SAR image processing tasks such as edge detection and segmentation. Thus, speckle reduction is critical and is used as a preprocessing step for smoothing homogeneous regions while preserving features such as edges and point scatterers. Although state-of-the-art methods provide better despeckling compared with conventional methods, their resource consumption is higher. In this letter, a sparsitydriven total-variation (TV) approach employing l0-norm, fractional norm, or l(1)-norm to smooth homogeneous regions with minimal degradation in edges and point scatterers is proposed. Proposed method, sparsity-driven despeckling (SDD), is capable of using different norms controlled by a single parameter and provides better or similar despeckling compared with the state-of-the-art methods with shorter execution times. Despeckling performance and execution time of the SDD are shown using synthetic and real-world SAR images.

Circuit-Based Approaches to Understanding Corticostriatothalamic Dysfunction Across the Psychosis Continuum

Dopamine is known to play a role in the pathogenesis of psychotic symptoms, but the mechanisms driving dopaminergic dysfunction in psychosis remain unclear. Considerable attention has focused on the role of corticostriatothalamic (CST) circuits, given that they regulate and are modulated by the activity of dopaminergic cells in the midbrain. Preclinical studies have proposed multiple models of CST dysfunction in psychosis, each prioritizing different brain regions and pathophysiological mechanisms. A particular challenge is that CST circuits have undergone considerable evolutionary modification across mammals, complicating comparisons across species. Here, we consider preclinical models of CST dysfunction in psychosis and evaluate the degree to which they are supported by evidence from human resting-state functional magnetic resonance imaging studies conducted across the psychosis continuum, ranging from subclinical schizotypy to established schizophrenia. In partial support of some preclinical models, human studies indicate that dorsal CST and hippocampal-striatal functional dysconnectivity are apparent across the psychosis spectrum and may represent a vulnerability marker for psychosis. In contrast, midbrain dysfunction may emerge when symptoms warrant clinical assistance and may thus be a trigger for illness onset. The major difference between clinical and preclinical findings is the strong involvement of the dorsal CST in the former, consistent with an increasing prominence of this circuitry in the primate brain. We close by underscoring the need for high-resolution characterization of phenotypic heterogeneity in psychosis to develop a refined understanding of how the dysfunction of specific circuit elements gives rise to distinct symptom profiles.

The Effects of Adding Art Therapy to Ongoing Antidepressant Treatment in Moderate-to-Severe Major Depressive Disorder: A Randomized Controlled Study

This randomized controlled study aimed to investigate the effects of art psychotherapy on moderate-to-severe major depressive disorder (MDD). Forty-two MDD patients were recruited from a psychiatric outpatient clinic in Seoul, the Republic of Korea. Participants were allocated on a randomized, open-label basis to either an experimental group, wherein they were treated with art psychotherapy added to pharmacotherapy, or a control group, wherein they were treated with pharmacotherapy alone. Pre- and post-test measures of the Hamilton Depression Rating Scale, Beck Depression Inventory-II, and remission rates were measured. The results indicate that patients treated with art psychotherapy and ongoing pharmacotherapy showed slightly greater improvement when compared with pharmacotherapy alone in moderate-to-severe MDD. These results suggest that art psychotherapy could be an effective add-on strategy for the treatment of moderate-to-severe MDD. However, a rigorous test would facilitate a better understanding of art psychotherapy as an add-on strategy for MDD treatment.

Fast 3-D Image Reconstruction on Nonregular UWB Sparse MIMO Planar Array Using Scaling Techniques

This article proposes a novel multistatic scaling algorithm for fast 3-D imaging with an ultrawideband (UWB) multiple-input-multiple-output (MIMO) planar array architecture. The proposed imaging method well addresses the weaknesses of the state-of-the-art imaging techniques, such as heavy computation burden and requirement of regular MIMO topologies. The key range cell migration in MIMO data set is corrected by a multistatic scaling operation. Only the fast Fourier transform and multiplications are employed during the entire imaging procedure that is easy to implement. For an arbitrary MIMO planar array, the computation complexity of the algorithm is O(N(5)log(2)N), which is lower than the optimum backpropagation and omega-K techniques. We have designed a 16 x 16 UWB sparse MIMO planar array with nonregular topology in our experiments. Results with both synthetic and practical data demonstrate the accuracy and efficiency of the algorithm, i.e., it obtains images of the same quality with much less time compared with the state-of-the-art other imaging algorithms.

Cyberchondria, Anxiety Sensitivity, Hypochondria, and Internet Addiction: Implications for Mental Health Professionals

Repetitive online searches for health information increase anxieties and result in Internet addiction. Internet addiction, cyberchondria, anxiety sensitivity, and hypochondria have been studied separately, but how these concepts are reciprocally linked has not been investigated. This study aimed to determine the levels, correlations, and predictors of Internet addiction, cyberchondria, anxiety sensitivity, and hypochondria among students based on the sample's characteristics. A sample of 143 university students participated in this cross-sectional online survey. A self-reported questionnaire was employed to collect data from students. The studied concepts had moderate to high correlations with each other and with the students' characteristics. Not getting infected with the coronavirus was among the demographic factors inserted into the regression model that only predicted cyberchondria. The model of cyberchondria was significant and explained 11.5% of the variance in the score of concepts. The results of the standard regression analysis indicated that the model predicting Internet addiction accounted for 41.2% of the variability. Our unique findings indicate that cyberchondria can contribute to developing Internet addiction compared to earlier studies. The findings suggest the importance of empowering students to overcome their anxieties by managing cyberchondria and Internet addiction. Mental health professionals, namely psychiatric nurses, are at the forefront of taking preventive mental health measures on campus, such as screening and referring students who exhibit these problems to psychological support and counseling to cope with their anxieties.

MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D

Multiple object tracking has been a challenging field, mainly due to noisy detection sets an identity switch caused by occlusion and similar appearance among nearby targets. Previous works rely on appearance models that are built on an individual or several selected frames for the comparison of features, but they cannot encode the long-term appearance changes caused by pose, viewing angle, and lighting conditions. In this paper, we propose an adaptive model that learns online a relatively long-term appearance change of each target. The proposed model is compatible with any feature of fixed dimension or their combination, whose learning rates are dynamically controlled by the adaptive update and spatial weighting schemes. To handle occlusion and nearby objects that are sharing a similar appearance, we also design the cross-matching and re-identification schemes based on the application of the proposed adaptive appearance models. In addition, the 3D geometry information is effectively incorporated in our formulation for data association. The proposed method outperforms all the state of the art on the MOTChallenge 3D benchmark and achieves real-time computation with only a standard desktop CPU. It has also shown superior performance over the state of the art on the 2D benchmark of MOTChallenge.

Spatially Consistent Supervoxel Correspondences of Cone-Beam Computed Tomography Images

Establishingdense correspondencesof conebeam computed tomography (CBCT) images is a crucial step for the attribute transfer and morphological variation assessment in clinical orthodontics. In this paper, a novel method, unsupervised spatially consistent clustering forest, is proposed to tackle the challenges for automatic supervoxel-wise correspondences of CBCT images. A complexity analysis of the proposed method with respect to the clustering hypotheses is provided with a data-dependent learning guarantee. The learning bound considers both the sequential tree traversals determined by questions stored in branch nodes and the clustering compactness of leaf nodes. A novel tree- pruning algorithm, guided by the learning bound, is also proposed to remove locally inconsistent leaf nodes. The resulting forest yields spatially consistent affinity estimations, thanks to the pruning penalizing trees with inconsistent leaf assignments and the combinational contextual feature channels used to learn the forest. A forest- based metric is utilized to derive the pairwise affinities and dense correspondences of CBCT images. The proposedmethod has been applied to the label propagation of clinically captured CBCT images. In the experiments, the method outperforms variants of both supervised and unsupervised forest- based methods and state-of-the- art label-propagation methods, achieving the mean dice similarity coefficients of 0.92, 0.89, 0.94, and 0.93 for the mandible, the maxilla, the zygoma arch, and the teeth data, respectively.

Simple production of resilin-like protein hydrogels using the Brevibacillus secretory expression system and column-free purification

Resilin, an insect structural protein, has excellent flexibility, photocrosslinking properties, and temperature responsiveness. Recombinant resilin-like proteins (RLPs) can be fabricated into three-dimensional (3D) structures for use as cell culture substrates and highly elastic materials. A simplified, high-yielding production process for RLPs is required for their widespread application. This study proposes a simple production process combining extracellular expression using Brevibacillus choshinensis (B. choshinensis) and rapid column-free purification. Extracellular production was tested using four representative signal peptides; B. choshinensis was found to efficiently secrete Rec1, an RLP derived from Drosophila melanogaster, regardless of the type of signal peptide. However, it was suggested that Rec1 is altered by an increase in the pH of the culture medium associated with prolonged incubation. Production in a jar fermentor with controllable pH yielded 530 mg Rec1 per liter of culture medium, which is superior to productivity using other hosts. The secreted Rec1 was purified from the culture supernatant via (NH4 )2 SO4 and ethanol precipitations, and the purified Rec1 was applied to ring-shaped 3D hydrogels. These results indicate that the combination of secretory production using B. choshinensis and column-free purification can accelerate the further application of RLPs.

Development and validation of a potential biomarker to improve the assessment of liver fibrosis progression in patients with chronic hepatitis B

We aimed to develop and validate a novel combined score to improve the assessment of liver fibrosis progression in patients with chronic hepatitis B (CHB). In this study, a total of 331 CHB patients from three cohorts who underwent liver biopsy were enrolled, and the Scheuer system was used for liver fibrosis classification. The combined score was derived by principal component analysis of key differentially expressed genes. For significant liver fibrosis (≥S2), the areas under the receiver operating characteristics curves (AUROCs) of the combined score were 0.838, 0.842, and 0.881 in the three cohorts, respectively. And for advanced liver fibrosis (≥S3), the AUROCs were 0.794, 0.801, and 0.901, respectively. Compared with the results of AUROCs for aspartate aminotransferase≥to≥platelet ratio (APRI) and fibrosis index based on four factors (FIB-4) in the validation cohorts, better clinical diagnostic value for assessing the progression of liver fibrosis was found in the combined score. Additionally, univariate ordered logistic regression analysis indicated that the combined score could serve as a more superior and stable risk factor than APRI and FIB-4 in the assessment of liver fibrosis. For CHB patients with normal alanine aminotransferase (ALT), our results further emphasized the diagnostic value of the combined score for significant fibrosis (≥S2) and advanced fibrosis (≥S3). Moreover, it was found that patients with the high combined score, who were associated with the advanced fibrosis stage, had higher levels of drug sensitivity and immune checkpoint expression. In conclusion, the novel combined score could serve as a potential biomarker and contribute to improving the assessment of fibrosis stage in CHB patients.

A large-scale study of the Trichinella genus in the golden jackal (Canis aureus) population in Serbia

Over the last decades the golden jackal (Canis aureus) has significantly expanded its range throughout Southeast and Central Europe, and the Balkan Peninsula is considered to be a core area of the species distribution in this part of the range. Due to its increasing number, ability of long distance movement through a wide range of landscapes and opportunistic feeding habits, the golden jackal may represent an important reservoir and transmitter of a variety of zoonotic agents, including parasites. The Balkans, Serbia included, remain an endemic area for various zoonotic parasites including Trichinella spp. Trichinella has recently been recorded in jackals in Serbia, which prompted us to carry out a large-scale survey of its prevalence, distribution and species identification in this host. In cooperation with local hunters, carcasses of a total of 738 legally hunted golden jackals were collected at 24 localities over an 11-year period (2003-2013). Analysis of tongue base tissue revealed Trichinella larvae in 122, indicating a prevalence of infection of 16.5%. No difference in the prevalence of infection was found between genders [16.2% in males and 16.9% in females (χ(2)=0.05, p=0.821)], or among the study years (G=7.22, p=0.705). Trichinella larvae were found in 13 out of the 24 examined localities. Molecular identification was performed for 90 isolates, and 64 (71.1%) larvae were identified as Trichinella spiralis and 25 (27.9%) as Trichinella britovi. Mixed infection (T. spiralis and T. britovi) was recorded in a single case. Although T. spiralis was more prevalent, T. britovi had a wider distribution, and was the only recorded species in jackal populations from the mountainous region of eastern Serbia. On the other hand, T. spiralis was dominant in jackals in the lowlands of central and northern Serbia, where domestic pigs are mostly reared. These results show that the golden jackal is involved in both the domestic and sylvatic cycle, and that it has emerged as a major host species in the sylvatic cycle of the Trichinella genus. Therefore, continued monitoring of Trichinella infection in golden jackals in Serbia and the whole of the Balkans is recommended in order to control transmission of this parasite to humans and domestic animals.

The Expected Job Satisfaction Affecting Entrepreneurial Intention as Career Choice in the Cultural and Artistic Industry

Artists have chosen a career between employment and self-employment. We studied the factors that influence career choices. We examined the effects of work conditions and employability on job satisfaction, and examined the effect of job satisfaction, outcome expectations and self-efficacy on entrepreneurial intentions. In addition, this study examined whether heuristic factors influence entrepreneurial intentions. Our findings suggest that perceived employability positively affected job satisfaction, while expectation gaps in working conditions negatively affected job satisfaction. Secondly, job satisfaction had a negative effect on entrepreneurial intention. In addition, self-efficacy and outcome expectations mediated between job satisfaction and entrepreneurial intention. Finally, overconfidence was positively influenced by job satisfaction and positively affected self-efficacy. This study contributed the study of the entrepreneurial intent to the field of culture and arts. It confirmed the effect of career choice and heuristic factors on entrepreneurial intention.

Morphological instability and roughening of growing 3D bacterial colonies

How do growing bacterial colonies get their shapes? While colony morphogenesis is well studied in two dimensions, many bacteria grow as large colonies in three-dimensional (3D) environments, such as gels and tissues in the body or subsurface soils and sediments. Here, we describe the morphodynamics of large colonies of bacteria growing in three dimensions. Using experiments in transparent 3D granular hydrogel matrices, we show that dense colonies of four different species of bacteria generically become morphologically unstable and roughen as they consume nutrients and grow beyond a critical size-eventually adopting a characteristic branched, broccoli-like morphology independent of variations in the cell type and environmental conditions. This behavior reflects a key difference between two-dimensional (2D) and 3D colonies; while a 2D colony may access the nutrients needed for growth from the third dimension, a 3D colony inevitably becomes nutrient limited in its interior, driving a transition to unstable growth at its surface. We elucidate the onset of the instability using linear stability analysis and numerical simulations of a continuum model that treats the colony as an "active fluid" whose dynamics are driven by nutrient-dependent cellular growth. We find that when all dimensions of the colony substantially exceed the nutrient penetration length, nutrient-limited growth drives a 3D morphological instability that recapitulates essential features of the experimental observations. Our work thus provides a framework to predict and control the organization of growing colonies-as well as other forms of growing active matter, such as tumors and engineered living materials-in 3D environments.

A blast fungus zinc-finger fold effector binds to a hydrophobic pocket in host Exo70 proteins to modulate immune recognition in rice

Exocytosis plays an important role in plant-microbe interactions, in both pathogenesis and symbiosis. Exo70 proteins are integral components of the exocyst, an octameric complex that mediates tethering of vesicles to membranes in eukaryotes. Although plant Exo70s are known to be targeted by pathogen effectors, the underpinning molecular mechanisms and the impact of this interaction on infection are poorly understood. Here, we show the molecular basis of the association between the effector AVR-Pii of the blast fungus Maganaporthe oryzae and rice Exo70 alleles OsExo70F2 and OsExo70F3, which is sensed by the immune receptor pair Pii via an integrated RIN4/NOI domain. The crystal structure of AVR-Pii in complex with OsExo70F2 reveals that the effector binds to a conserved hydrophobic pocket in Exo70, defining an effector/target binding interface. Structure-guided and random mutagenesis validates the importance of AVR-Pii residues at the Exo70 binding interface to sustain protein association and disease resistance in rice when challenged with fungal strains expressing effector mutants. Furthermore, the structure of AVR-Pii defines a zinc-finger effector fold (ZiF) distinct from the MAX (Magnaporthe Avrs and ToxB-like) fold previously described for a majority of characterized M. oryzae effectors. Our data suggest that blast fungus ZiF effectors bind a conserved Exo70 interface to manipulate plant exocytosis and that these effectors are also baited by plant immune receptors, pointing to new opportunities for engineering disease resistance.

Accelerated 3D bSSFP Using a Modified Wave-CAIPI Technique With Truncated Wave Gradients

The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady- state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3DWave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bSSFP obtains up to 12x retrospective acceleration at 0.8 mm isotropic resolution in in vivo 3D brain experiments and is superior to the state-of-the-art Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration ( CAIPIRINHA) technique, according to both visual validation and quantitative analysis. Moreover, in vivo 3D spine and abdomen imaging demonstrate the potential clinical applications of Wave-bSSFP with fast acquisition speed, improved isotropic resolution and fine image quality.

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37 degrees C. The device features a limit of detection of 3.8 mu M, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.

Heat Management in Power Converters: From State of the Art to Future Ultrahigh Efficiency Systems

Thermal management is a key design aspect of power converters since it determines their reliability as well as their final performance and power density. Cooling technologies have been a research area in electronics since the 1940s and, in the last 15 years, the number of articles related to this field has grown significantly. At present, thermal management is essential in new disciplines and it is a critical enabling technology in the development of power electronic systems. This paper aims at presenting a review of the state-of-the-art technology and provides future design guidelines for high efficiency power electronic converters. The main design trends are focused on the need to develop cooling systems able to manage high local density heat fluxes due to two converging trends: higher power dissipation and smaller module size. Considering the latest advances in thermal management, as well as the huge improvement in power electronics in the last decades, a review and classification of the main thermal management techniques is presented. Besides, they are compared considering important parameters such as peak power dissipation, efficiency, cost/complexity, power density or technical maturity, and a design example for an ultrahigh efficiency converter is presented.

STATE-OF-ART, CHALLENGES, AND OUTLOOK ON MANUFACTURING OF COOLING HOLES FOR TURBINE BLADES

A cooling hole is important structure of turbine blades for high-performance aircraft engines. It is very challenging to manufacture cooling holes in superalloys including nickel-based and titanium alloys. This article aims to provide a critical assessment on the major types of machining processes for manufacturing cooling holes. The process mechanism, efficiency, form accuracy, and surface integrity of the state-of-art of four machining processes, i.e., mechanical drilling (MD), electrical discharge drilling (EDD), laser drilling (LD), and electrochemical drilling (ECD) have been thoroughly analyzed and compared in details. The future challenges and future potential research directions for the machining processes are also discussed.

SAKARYA SCIENCE AND ART CENTER NATURE EDUCATION PROGRAM

The aim of this study is to increase the awareness of the gifted and talented students about science and nature education according to their own learning properties. The study was carried out in July 2011, within the project 'Sakarya Arts and Sciences Centre Nature Education Program' with the code of 111B063 supported by the Scientific and Technological Research Counsil of Turkey (TUBITAK). A hundred and twenty gifted and talented students, each session composed of 30 students, have attended the research. In the study, pre-test, post-test, quasi-experimental design without control group was used. Also, nature education knowledge tests scrutinised by assessment and evaluation experts and instructors are used. Acquired data was analysed through statistical program for social sciences. It was observed that for all the students, the post-test scores are higher than the pre-test scores and a significant difference has been found as a result of the analyses. It is also determined that nature education program has a substantial impact on gifted and talented students awareness about nature education and science. The program prepared by considering the study results is believed to have enhanced the awareness to science and nature.

Assessment of the breadth of binding promiscuity of heme towards human proteins

Heme regulates important biological processes by transient interactions with many human proteins. The goal of the present study was to assess extends of protein binding promiscuity of heme. To this end we evaluated interaction of heme with &gt;9000 human proteins. Heme manifested high binding promiscuity by binding to most of the proteins in the array. Nevertheless, some proteins have outstanding heme binding capacity. Bioinformatics analyses revealed that apart from typical haemoproteins, these proteins are frequently involved in metal binding or have the potential to recognize DNA. This study can contribute for understanding the regulatory functions of labile heme.

Diel Periodicity in Males of the Navel Orangeworm (Lepidoptera: Pyralidae) as Revealed by Automated Camera Traps

Navel orangeworm, Amyelois transitella (Walker), is a key pest of walnuts, pistachio, and almonds in California. Pheromone mating disruption using timed aerosol dispensers is an increasingly common management technique. Dispenser efficiency may be increased by timing releases with the active mating period of navel orangeworm. Past work found that the peak time of sexual activity for navel orangeworm females is 2 h before sunrise when temperatures are above 18°C. Inference of male responsiveness from data collected in that study was limited by the necessity of using laboratory-reared females as a source of sex pheromone emission to attract males and the inherent limitations of human observers for nocturnal events. Here we used camera traps baited with artificial pheromone to observe male navel orangeworm mating response in the field over two field seasons. Male response to synthetic pheromone exhibited diel patterns broadly similar to females, i.e., they were active for a brief period of 2-3 h before dawn under summer conditions and began responding to pheromone earlier and over a longer period of time during spring and fall. But contrary to the previous findings with females, some males were captured at all hours of the day and night, and there was no evidence of short-term change of pheromone responsiveness in response to temperature. Environmental effects on the response of navel orangeworm males to an artificial pheromone source differ in important ways from the environmental effects on female release of sex pheromone.

A modular CNN-based building detector for remote sensing images

Convolutional neural networks (CNNs) have resurged lately due to their state-of-the-art performance in various disciplines, such as computer vision, audio and text processing. However, CNNs have not been widely employed for remote sensing applications. In this paper, we propose a CNN architecture, named Modular-CNN, to improve the performance of building detectors that employ Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) in a remote sensing dataset. Additionally, we propose two improvements to increase the classification accuracy of Modular-CNN. The first improvement combines the power of raw and normalised features, while the second one concerns the Euler transformation of feature vectors. We demonstrate the effectiveness of our proposed Modular-CNN and the novel improvements in remote sensing and other datasets in a comparative study with other state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.

Search for Compatible Source Code

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9-60.9% in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4-11% in Precision.

Use of Water-In-Salt Concentrated Liquid Electrolytes in Electrochemical Energy Storage: State of the Art and Perspectives

Batteries based on organic electrolytes have been raising safety concerns due to some associated fire/explosion accidents caused by the unusual combination of highly flammable organic electrolytes and high energy electrodes. Nonflammable aqueous batteries are a good alternative to the current energy storage systems. However, what makes aqueous batteries safe and viable turns out to be their main weakness, since water molecules are prone to decomposition because of a narrow electrochemical stability window (ESW). In this perspective we introduce aqueous batteries and then discuss the state-of-the-art of water-in-salt (WIS) electrolytes for aqueous energy storage systems. The main strategies to improve ESW are reviewed, including: (i) the use of fluorinated salts to make a solid electrolyte interphase (SEI); (ii) the use of cost-effective and highly soluble salts to reduce water activity through super concentration; and (iii) the use of hybrid electrolytes combining the advantages of both aqueous and non-aqueous phases. Then, we discuss different battery chemistries operated with different WIS electrolytes. Finally, we highlight the challenges and future technological perspectives for practical aqueous energy storage systems, including applications in stationary storage/grid, power backup, portable electronics, and automotive sectors.

Working memory is updated by reallocation of resources from obsolete to new items

Visual working memory (VWM) resources are limited, placing constraints on how much visual information can be simultaneously retained. During visually guided activity, stored information can quickly become outdated, so updating mechanisms are needed to ensure the contents of memory remain relevant to current task goals. In particular, successful deallocation of resources from items that become obsolete is likely to be critical for maintaining the precision of those representations still in memory. The experiments in this study involved presenting two memory arrays of coloured disks in sequence. The appearance of the second array was a cue to replace, rehearse, or add a new colour to the colours in memory. We predicted that successful resource reallocation should result in comparable recall precision when an item was replaced or rehearsed, owing to the removal of pre-replacement features. In contrast, a failure to update WM should lead to comparable precision with a condition in which a new colour was added to memory. We identified a very small proportion (∼5%) of trials in which participants incorrectly reported a feature from the first array in place of its replacement in the second, which we interpreted as a failure to incorporate the information from the second display into memory. Once these trials were discounted, precision estimates were consistent with complete redistribution of resources in the case of updating a single item. We conclude that working memory can be efficiently updated when previous information becomes obsolete, but that this is a demanding active process that occasionally fails.

Image compressive sensing via Truncated Schatten-p Norm regularization

Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in Compressive Sensing (CS) image recovery. How to solve the resulting rank regularization problem which is known as an NP-hard problem is critical to the recovery results. NLR takes use of logdet as a smooth nonconvex surrogate function for the rank instead of the convex nuclear norm. However, logdet function cannot well approximate the rank because there exists an irreparable gap between the fixed logdet function and the real rank. In this paper, Truncated Schatten-p Norm regularization, which is used as a surrogate function for the rank to exploit the benefits of both schatten-p norm and truncated nuclear norm, has been proposed toward better exploiting low rank property in CS image recovery. In addition, we have developed an efficient iterative scheme to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform the existing state-of-the-art image CS methods. (C) 2016 Elsevier B.V. All rights reserved.

EIDM: deep learning model for IoT intrusion detection systems

Internet of Things (IoT) is a disruptive technology for the future decades. Due to its pervasive growth, it is susceptible to cyber-attacks, and hence the significance of Intrusion Detection Systems (IDSs) for IoT is pertinent. The viability of machine learning has encouraged analysts to apply learning techniques to intelligently discover and recognize cyber attacks and unusual behavior among the IoTs. This paper proposes an enhanced anomaly-based Intrusion Detection Deep learning Multi-class classification model (EIDM) that can classify 15 traffic behaviors including 14 attack types with the accuracy of 95% contained in the CICIDS2017 dataset. Four state-of-the-art deep learning models are also customized to classify six classes of network traffic behavior. An extensive comparative study in terms of classification accuracy and efficiency metrics is conducted between EIDM and several state-of-the-art deep learning-based IDSs showing that EIDM has achieved accurate detection results.

Social Model Hospice Homes: Bridging the Gap in End-of-Life Care Delivery

Background: People prefer to die at home, if given a choice. However, data show that less than half of hospice patients get to do so, as many lack the social resources needed for end-of-life (EOL) care to be supported at home. The Social Model Hospice Home (SMHh) is an emerging model of care and offers an option for individuals whose EOL care cannot be fully supported by their available social network. An SMHh is a community-supported home staffed with round the clock caregivers dedicated to closing this social gap in EOL care delivery. Objective: This study aimed to enhance and clarify the reasons for successful outcomes of the SMHh concept. Methods: This study used a qualitative descriptive approach. Twenty-three semistructured interviews were conducted with the participants. Located throughout North America, participants consisted of staff, volunteers, guests, family members of previous guests, board members, and administrators of SMHh programs. Results: Over the course of the study, five major themes were developed: (1) physical attributes of the home, (2) volunteer base, (3) attention to detail, (4) structure of accountability, and (5) practice of compassionate honesty. These themes reveal the beginning of an explanatory theory of why the SMHh concept can support the delivery of effective, high-quality EOL care. Conclusions: The result of this study substantiates SMHh as a viable alternative to traditional in-home or institution-based EOL care. As the SMHh movement develops, more research is needed to explore and understand how this emerging model of care can be implemented on a larger scale.

A two-stage robust iterative learning model predictive control for batch processes

Iterative learning model predictive control (ILMPC) has been considered as potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along batches and ensure system stability within batches. However, as a model-based control method, the control performance of the ILMPC algorithm deteriorates when exists model parameter uncertainty. Therefore, guaranteeing system tracking performance in the case of model parameter uncertainty is a challenging task in the framework designing of ILMPC method. To this end, we develop a two-stage robust ILMPC strategy for batch processes, which integrates the robust iterative learning control (ILC) in the domain of batch-axis and robust model predictive control (MPC) in the domain of time-axis into one comprehensive control scheme. The integrated control law of the developed two-stage robust ILMPC algorithm is obtained by solving two convex optimization problems. As a result, the developed control method obtains faster convergence speed and better tracking performance in the case of model parameter uncertainty. Moreover, the convergence analysis of the system is presented. Finally, comparative simulations are provided to verify the superiority of the developed control algorithm.

Life cycle impacts and costs of photovoltaic systems: Current state of the art and future outlooks

The photovoltaic energy sector is rapidly expanding and technological specification for PV has improved dramatically in the last two decades. This paper sketches the current state of the art and drafts three alternative scenarios for the future, in terms of costs, market penetration and environmental performance. According to these scenarios, if economic incentives are supported long enough into the next ten to twenty years, PV looks set for a rosy future, and is likely to play a significant role in the future energy mix, while at the same time contributing to reduce the environmental impact of electricity supply. (C) 2009 Elsevier Ltd. All rights reserved.

Camera-Vision-Based Water Level Estimation

This letter presents a camera-vision-based water level estimation method that uses a single image. Existing state-of-the-art multiple-image and airborne ultrasonic sensor-based water level estimation methods rely on a large number of image frames to stabilize the results given the heavy background jitter. In real environments, our proposed two-step histogram-based method is superior to performance of previous image and sensor-based methods in terms of efficiency, robustness, and accuracy.

Advanced and recurrent endometrial cancer: State of the art and future perspectives

Patients with primary metastatic/recurrent endometrial cancer have poor prognosis and available therapeutic options are limited. Current treatment is mainly based on platinum-based chemotherapy. Recently, the Food and Drug Administration (FDA) granted approval for the combination of pembrolizumab and lenvatinib for endometrial cancer patients without microsatellite instability (MSS) progressing on a previous line of therapy while European Medicines Agency (EMA) approved the combination for all comers patients failing previous platinum treatment. Anti programmed cell death protein-1 (PD-1) dostarlimab (TSR-042) was approved as monotherapy in patients with advanced, microsatellite instable (MSI) endometrial cancer progressing to platinum treatment. Phase II-III clinical trials in metastatic endometrial cancer are mainly focused on target therapies and immunotherapy as single agents or in combination. Unfortunately, most of these trials are lacking of predictive biomarkers of response to select patients most or at least likely to benefit from those treatments.

Post-processing approaches for improving people detection performance

People detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. We address one of the main problems of people detection, in video sequences: every people detector from the state of the art must maintain a balance between the number of false detections and the number of missing pedestrians. This compromise limits the global detection results. In order to reduce or relax this limitation and improve the detection results, we evaluate two different post-processing subtasks. Firstly, we propose the use of people-background segmentation as a filtering stage in people detection. Then, we evaluate the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results. And, finally, we evaluate the successive application of both post-processing approaches. Experiments have been performed on two extensive datasets and using different people detectors from the state of the art: the results show the benefits achieved using the proposed post-processing techniques. (C) 2014 Elsevier Inc. All rights reserved.

Sinus of Valsalva aneurysm: Defining the optimal approach

Sinus of Valsalva aneurysm (SVA) is relatively rare, especially in Western countries, and reports on long-term results after surgical SVA repair in a sizable patient cohort are scarce. In this issue of the Journal of Cardiac Surgery, Chaganti and colleagues publish their surgical experience over the past 30 years in 216 patients with SVA. SVAs were closed via a dual approach, with (1) patch closure (80%) or direct closure (20%) of the base of the fistula through aortotomy and (2) direct closure of the ruptured tip through the chamber of rupture. Aortic valve replacement (9.7%) or repair (6.5%) was performed for moderate to severe aortic regurgitation (AR). There was no hospital mortality. During a mean follow-up of 10 years, no patient had residual/recurrent shunting. The actual survival at 10 years was 99%, with only two deaths. Freedom from moderate or severe AR was 98.5% at 10 years. Early and long-term results after surgical repair of SVA were excellent in their 216 patients with a mean follow-up of 10 years. Their dual approach for SVA was effective in preventing residual/recurrent shunting. The need for AVR in 10% of the patients speaks to the importance of follow-up. The current report provides strong support for surgical repair being the preferred management for SVA.

State-of-the-art cerium nanoparticles as promising agents against human viral infections

The world is faces a significant global health challenge in the form of viral infections, particularly the emergence of viral strains that are resistant to effective antiviral therapies. This underscores the urgent need for the development of effective and safe antiviral agents. Nanoscale materials are now being used as novel antiviral agents. Cerium nanoparticles have unique chemical and physical properties that make them particularly promising for viral infections. These particles reduce inflammation and the autoimmune response. Cerium nanoparticles, in addition to their antiviral properties, have many other advantages that are highly sought after for various aspects of biomedical applications. This review focuses on the various properties of cerium nanoparticles as a novel agent against viral infections.

Graph Attention for Automated Audio Captioning

State-of-the-art audio captioning methods typically use the encoder-decoder structure with pretrained audio neural networks (PANNs) as encoders for feature extraction. However, the convolution operation used in PANNs is limited in capturing the long-time dependencies within an audio signal, thereby leading to potential performance degradation in audio captioning. This letter presents a novel method using graph attention (GraphAC) for encoder-decoder based audio captioning. In the encoder, a graph attention module is introduced after the PANNs to learn contextual association (i.e. the dependency among the audio features over different time frames) through an adjacency graph, and a top-k mask is used to mitigate the interference from noisy nodes. The learnt contextual association leads to a more effective feature representation with feature node aggregation. As a result, the decoder can predict important semantic information about the acoustic scene and events based on the contextual associations learned from the audio signal. Experimental results show that GraphAC outperforms the state-of-the-art methods with PANNs as the encoders, thanks to the incorporation of the graph attention module into the encoder for capturing the long-time dependencies within the audio signal.

Heating temperature and water activity of alfalfa seeds affect thermal inactivation of Salmonella and maintaining seed viability

Sprouts have been involved in many outbreaks of salmonellosis where seeds were identified as the likely source of contamination. This study aimed to develop an effective heat treatment that could achieve a >5-log reduction of Salmonella inoculated on alfalfa seeds while maintaining seed viability and vigor. Effects of seeds' water activity (aw) and heat treatment temperature on Salmonella inactivation and seed viability were determined. Alfalfa seeds were dip-inoculated with a four-strain Salmonella cocktail and dried to aw of 0.05-0.20. The inoculated seeds were then placed in sealed glass tubes and heated at 65.9, 71.0, and 76.6 °C for up to 180 h. Increasing aw of seeds greatly improved thermal inactivation of Salmonella. For example, to achieve a >5-log reduction of Salmonella on seeds, treatment times of 140 and 60 h at 71.0 °C were required for aw of 0.1 and 0.2, respectively. Treatment temperature also greatly affected inactivation of Salmonella on alfalfa seeds. For example, to achieve a >5-log reduction of Salmonella on seeds with aw of 0.2, treatment times of 180 and 60 h were required for temperatures of 65.9 and 71.0 °C, respectively. Seeds' aw was critical for preserving seed viability. When seeds were treated at 71.0 °C for 60 h, increasing aw from 0.1 to 0.2 decreased the sprout yield ratio from 103.9 % to 73.7 %. Treatment of seeds with aw of 0.1 at 71.0 °C was found to be optimum for achieving a desirable Salmonella inactivation level while maintaining seed viability, resulting in 4.2 and 6.0 log reductions of Salmonella and yield ratios of 100.7 % and 96.1 % after 100- and 140-h treatments, respectively. This optimum heat treatment was compared with the traditional 20,000-ppm chlorine wash in terms of Salmonella inactivation and preservation of seed viability and found to be a far superior disinfection method. The chlorine treatment achieved 1.8 and 2.0 log reductions of Salmonella and yield ratios of 70.9 % and 65.1 % after 15- and 20-min treatments, respectively.

On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey

This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning. It is showing interesting results in the computer vision field and, recently, it has been applied to the visual tracking problem yielding to the rapid development of novel tracking strategies. After providing an introduction to reinforcement learning, this paper compares recent visual tracking approaches based on deep reinforcement learning. Analysis of the state-of-the-art suggests that reinforcement learning allows modeling varying parts of the tracking system including target bounding box regression, appearance model selection, and tracking hyper-parameter optimization. The DRL framework is elegant and intriguing, and most of the DRL-based trackers achieve state-of-the-art results.

Improving Face-Based Age Estimation With Attention-Based Dynamic Patch Fusion

With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.

Developmental exposure of aflatoxin B1 reversibly affects hippocampal neurogenesis targeting late-stage neural progenitor cells through suppression of cholinergic signaling in rats

To elucidate the maternal exposure effects of aflatoxin B1 (AFB1) and its metabolite aflatoxin M1, which is transferred into milk, on postnatal hippocampal neurogenesis, pregnant Sprague-Dawley rats were provided a diet containing AFB1 at 0, 0.1, 0.3, or 1.0 ppm from gestational day 6 to day 21 after delivery on weaning. Offspring were maintained through postnatal day (PND) 77 without AFB1 exposure. Following exposure to 1.0 ppm AFB1, offspring showed no apparent systemic toxicity at weaning, whereas dams showed increased liver weight and DNA repair gene upregulation in the liver. In the hippocampal dentate gyrus of male PND 21 offspring, the number of doublecortin(+) progenitor cells were decreased, which was associated with decreased proliferative cell population in the subgranular zone at ≥ 0.3 ppm, although T-box brain 2(+) cells, tubulin beta III(+) cells, gamma-H2A histone family, member X(+) cells, and cyclin-dependent kinase inhibitor 1A(+) cells did not fluctuate in number. AFB1 exposure examined at 1.0 ppm also resulted in transcript downregulation of the cholinergic receptor subunit Chrna7 and dopaminergic receptor Drd2 in the dentate gyrus, although there was no change in transcript levels of DNA repair genes. In the hippocampal dentate hilus, interneurons expressing CHRNA7 or phosphorylated tropomyosin receptor kinase B (TRKB) decreased at ≥ 0.3 ppm. On PND 77, there were no changes in neurogenesis-related parameters. These results suggested that maternal AFB1 exposure reversibly affects hippocampal neurogenesis targeting type-3 progenitor cells. This mechanism likely involves suppression of cholinergic signals on hilar GABAergic interneurons and brain-derived neurotrophic factor-TRKB signaling from granule cells. The no-observed-adverse-effect level for offspring neurogenesis was determined to be 0.1 ppm (7.1-13.6 mg/kg body weight/day).

Combination of Kullback-Leibler divergence and Manhattan distance measures to detect salient objects

Salient object detection is a computer vision technique that filters out redundant visual information and considers potentially relevant parts of our visual field. In this paper, we modify the Liu et al. model for salient object detection, which combines multi-scale contrast, center-surround histogram and color spatial distribution with conditional random fields. A combination of Symmetric Kullback-Leibler divergence and Manhattan distance instead of chi-square measure is employed to determine center-surround histogram difference. The modified Liu et al. model also uses a less computational intensive color spatial distribution map. To check the efficacy of the modified Liu et al. model, the performance is evaluated in terms of precision, recall, F-measure, area under curve and computation time. Experiment is carried out on a publicly available image datasets, and performance is compared with Liu et al. model and six other popular state-of-the-art models. Experimental results demonstrate that the modified Liu et al. model outperforms Liu et al. model and other existing state-of-the-art methods in terms of precision, F-measure, area under curve and has comparable performance in terms of recall with Liu et al. model.

Injectable Immunotherapeutic Hydrogel Containing RNA-Loaded Lipid Nanoparticles Reshapes Tumor Microenvironment for Pancreatic Cancer Therapy

Pancreatic cancer immunotherapy is becoming a promising strategy for improving the survival rate of postsurgical patients. However, the low response rate to immunotherapy suggests a low number of antigen-specific T cells and a high number of immunosuppressive tumor-associated macrophages in the pancreatic tumor microenvironment. Herein, we developed an in situ injectable thermosensitive chitosan hydrogel loaded with lipid-immune regulatory factor 5 (IRF5) mRNA/C-C chemokine ligand 5 (CCL5) siRNA (LPR) nanoparticle complexes (LPR@CHG) that reprogram the antitumoral immune niche. The LPR@CHG hydrogel upregulates IRF5 and downregulates CCL5 secretion, which contribute to a significant increase in M1 phenotype macrophages. Tumor growth is controlled by effective M1 phenotype macrophage that initiate T cell-mediated immune responses. Overall, the LPR@CHG hydrogel is expected to be a meaningful immunotherapy platform that can reshape the immunosuppressive tumor microenvironment and improve the efficacy of current pancreatic immunotherapies while minimizing systemic toxicity.

Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0

Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes.

Ambiguity-aware robust teacher (ART): Enhance d self-knowle dge distillation framework with pruned teacher network

Self-knowledge distillation (self-KD) methods, which use a student model itself as the teacher model instead of a large and complex teacher model, are currently a subject of active study. Since most previ-ous self-KD approaches relied on the knowledge of a single teacher model, if the teacher model incor-rectly predicted confusing samples, poor-quality knowledge was transferred to the student model. Unfor-tunately, natural images are often ambiguous for teacher models due to multiple objects, mislabeling, or low quality. In this paper, we propose a novel knowledge distillation framework named ambiguity-aware robust teacher knowledge distillation (ART-KD) that provides refined knowledge, that reflects the ambigu-ity of the samples with network pruning. Since the pruned teacher model is simply obtained by copying and pruning the teacher model, re-training process is unnecessary in ART-KD. The key insight of ART-KD lies in the predictions of a teacher model and pruned teacher model for ambiguous samples providing different distributions with low similarity. From these two distributions, we obtain a joint distribution considering the ambiguity of the samples as teacher's knowledge for distillation. We comprehensively evaluate our method on public classification benchmarks, as well as more challenging benchmarks for fine-grained visual recognition (FGVR), achieving much superior performance to state-of-the-art counter-parts.(c) 2023 Elsevier Ltd. All rights reserved.

SAS: Painting Detection and Recognition via Smart Art System With Mobile Devices

Artwork recognition is an important research direction in the field of image processing. However, most of the current proposed methods are not designed for the demand of real-time analysis with mobile devices. Moreover, existing methods usually rely on high quality images and require large amounts of computing consumption. Based on the deep learning technology, in this paper, we propose a Smart Art System (SAS) with mobile devices. Our SAS mainly consists of two parts, i.e., painting detection unit and recognition unit. The detection module adopts a new painting detection algorithm called Single Shot Detection with Painting Landmark Location (SSD-PLL). SSD-PLL can effectively eliminate the influence of complex background factors on recognition. Considering the limited computing capacity of the mobile devices, our recognition module adopts a new ultra-light painting classifier. The classifier adopts MobileNet as the backbone and owns extra operation for Local Features Fusion (LFF). With our SAS, users can use mobile phone to take a photo of any paintings, then SAS would analyze the paintings and report the relevant information in real time. In order to validate the effectiveness of the proposed method, we have established two large scale image databases. The databases include 7,500 Traditional Chinese paintings (TCPs) and 8,800 Oil paintings (OPs), respectively. We evaluate our method and compare with the relevant algorithms, and our method achieves the highest performance and better real-time performance. Extensive experimental results on these databases show the effectiveness of the proposed algorithm.

Viewpoints of Residency Program Directors Regarding Depressive Symptoms in Pharmacy Residents

Objective: Several publications have highlighted residency-specific factors being associated with depressive symptoms in pharmacy residents, but no studies have investigated the viewpoint of residency program directors (RPDs) regarding this issue. This study's primary objectives were to identify potential contributing factors, determine current resources, and outline possible solutions to decrease depressive symptoms among pharmacy residents from the point of view of RPDs. Methods: RPDs were asked to participate in a 45-60-minute semi-structured interview conducted via phone by the primary investigator, recorded, and transcribed using NVivo. Interviews were manually analyzed by study investigators to identify emerging themes. The investigators discussed findings and discrepancies to agree upon thematic interpretations of the transcripts. Results: Ten interviews were conducted between May 2019 and February 2020. RPD experience ranged from 2-15 years. Pharmacy practice PGY1 programs comprised 60% of interviews, 20% for community practice, and 10% each for managed care and ambulatory care. All RPDs indicated increased workload as a contributing factor to depressive symptoms in residents. The inability to accept and utilize constructive feedback and difficulties transitioning from student to resident were identified as contributing factors by 50% of the RPDs. Nine RPDs reported having employee assistance programs, stating the resource was underutilized, and identified the need for additional education regarding identification and triage, not necessarily management, to help residents. Conclusion: This study highlights consistency among RPDs regarding potential contributors to depressive symptoms in pharmacy residents and emphasizes the need for additional RPD and preceptor training to identify and help residents more effectively with these issues.

DDX60 selectively reduces translation off viral type II internal ribosome entry sites

Co-opting host cell protein synthesis is a hallmark of many virus infections. In response, certain host defense proteins limit mRNA translation globally, albeit at the cost of the host cell's own protein synthesis. Here, we describe an interferon-stimulated helicase, DDX60, that decreases translation from viral internal ribosome entry sites (IRESs). DDX60 acts selectively on type II IRESs of encephalomyocarditis virus (EMCV) and foot and mouth disease virus (FMDV), but not by other IRES types or by 5' cap. Correspondingly, DDX60 reduces EMCV and FMDV (type II IRES) replication, but not that of poliovirus or bovine enterovirus 1 (BEV-1; type I IRES). Furthermore, replacing the IRES of poliovirus with a type II IRES is sufficient for DDX60 to inhibit viral replication. Finally, DDX60 selectively modulates the amount of translating ribosomes on viral and in vitro transcribed type II IRES mRNAs, but not 5' capped mRNA. Our study identifies a novel facet in the repertoire of interferon-stimulated effector genes, the selective downregulation of translation from viral type II IRES elements.

Slum tourism in the context of the tourism and poverty (relief) debate

The paper examines the role of slum tourism in poverty relief. To do so, it surveys the state-of-the-art literature on tourism and poverty and investigates the ways in which slum tourism research relates to this literature. Slum tourism research has addressed the question of how the poor may benefit from this practice; however, these efforts have not systematically considered the general debate on tourism and poverty relief. The survey of slum tourism research also contributes to the conceptual development of the tourism-poverty nexus. The predominant choice of approaches in this field relies on quantitative indicators of poverty relief, but these do not sufficiently account for the multi-dimensional character of poverty. The study of slum tourism research points to the multi-dimensional valorisation of poverty in tourism which is an aspect often overlooked in the state-of-the-art research on tourism and poverty.

Body Mass Index in Adolescence and Long-Term Risk of Early Incident Atrial Fibrillation and Subsequent Mortality, Heart Failure, and Ischemic Stroke

Background We sought to determine the role of obesity in adolescent men on development of atrial fibrillation (AF) and subsequent associated clinical outcomes in subjects diagnosed with AF. Methods and Results We conducted a nationwide, register-based, cohort study of 1 704 467 men (mean age, 18.3±0.75 years) enrolled in compulsory military service in Sweden from 1969 through 2005. Height and weight, blood pressure, fitness, muscle strength, intelligence quotient, and medical disorders were recorded at baseline. Records obtained from the National Inpatient Registry and the Cause of Death Register were used to determine incidence and clinical outcomes of AF. During a median follow-up of 32 years (interquartile range, 24-41 years), 36 693 cases (mean age at diagnosis, 52.4±10.6 years) of AF were recorded. The multivariable-adjusted hazard ratio (HR) for AF increased from 1.06 (95% CI, 1.03-1.10) in individuals with body mass index (BMI) of 20.0 to <22.5 kg/m2 to 3.72 (95% CI, 2.44-5.66) among men with BMI of 40.0 to 50.0 kg/m2, compared with those with BMI of 18.5 to <20.0 kg/m2. During a median follow-up of ≈6 years in patients diagnosed with AF, we identified 3767 deaths, 3251 cases of incident heart failure, and 921 cases of ischemic stroke. The multivariable-adjusted HRs for all-cause mortality, incident heart failure, and ischemic stroke in AF-diagnosed men with baseline BMI >30 kg/m2 compared with those with BMI <20 kg/m2 were 2.86 (95% CI, 2.30-3.56), 3.42 (95% CI, 2.50-4.68), and 2.34 (95% CI, 1.52-3.61), respectively. Conclusions Increasing BMI in adolescent men is strongly associated with early AF, and with subsequent worse clinical outcomes in those diagnosed with AF with respect to all-cause mortality, incident heart failure, and ischemic stroke.

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

High-Speed Low-Complexity Guided Image Filtering-Based Disparity Estimation

Stereo vision is a methodology to obtain depth in a scene based on the stereo image pair. In this paper, we introduce a discrete wavelet transform (DWT)-based methodology for a state-of-the-art disparity estimation algorithm that resulted in significant performance improvement in terms of speed and computational complexity. In the initial stage of the proposed algorithm, we apply DWT to the input images, reducing the number of samples to be processed in subsequent stages by 50%, thereby decreasing computational complexity and improving processing speed. Subsequently, the architecture has been designed based on this proposed methodology and prototyped on a Xilinx Virtex-7 FPGA. The performance of the proposed methodology has been evaluated against four standard Middlebury Benchmark image pairs viz. Tsukuba, Venus, Teddy, and Cones. The proposed methodology results in the improvement of about 44.4% cycles per frame, 52% frames/s, and 61.5% and 59.6% LUT and register utilization, respectively, compared with state-of-the-art designs.

Renewable energy technology innovation and inclusive low-carbon development from the perspective of spatiotemporal consistency

As an emerging driving factor, the positive impact of renewable energy technology innovation (RETI) on inclusive low-carbon development (ILCD) may be undervalued or even neglected. This paper develops an evaluation system to measure China's ILCD by using provincial panel data from 2006 to 2020. Based on the combined perspective of spatial spillover effect and threshold effect, this paper examines the spatial spillover effects and the regional boundary of RETI on ILCD in different periods and further analyzes five heterogeneities. The results show that (1) RETI and ILCD are increasing steadily, presenting a spatial pattern of "high in the east and low in the west." (2) Overall, RETI significantly promotes ILCD in local and neighboring areas. RETI in the growth period inhibits local ILCD, which in the mature period promotes local and neighboring ILCD. (3) The spatial spillover boundary of the whole RETI is 1400 km, that of RETI in the growth period is 1000 km, and that of RETI in the mature period is 1600 km. (4) The promotion effect of RETI on ILCD enhances over time and shows a spatial pattern of "eastern > central > south > north > western." It is further found that RETI strongly promotes ILCD in non-resource-based, high marketization, and strong environmental regulation areas. Therefore, it is necessary to break down administrative and market barriers, strengthen inter-regional cooperation and interconnection of resource elements, and establish a dynamic management mechanism of "one province, one policy" according to the regional heterogeneity for providing decision-making reference in promoting global energy transition and climate governance.

Lecanemab, Aducanumab, and Gantenerumab - Binding Profiles to Different Forms of Amyloid-Beta Might Explain Efficacy and Side Effects in Clinical Trials for Alzheimer's Disease

Immunotherapy against amyloid-beta (Aβ) is a promising option for the treatment of Alzheimer's disease (AD). Aβ exists as various species, including monomers, oligomers, protofibrils, and insoluble fibrils in plaques. Oligomers and protofibrils have been shown to be toxic, and removal of these aggregates might represent an effective treatment for AD. We have characterized the binding properties of lecanemab, aducanumab, and gantenerumab to different Aβ species with inhibition ELISA, immunodepletion, and surface plasmon resonance. All three antibodies bound monomers with low affinity. However, lecanemab and aducanumab had very weak binding to monomers, and gantenerumab somewhat stronger binding. Lecanemab was distinctive as it had tenfold stronger binding to protofibrils compared to fibrils. Aducanumab and gantenerumab preferred binding to fibrils over protofibrils. Our results show different binding profiles of lecanemab, aducanumab, and gantenerumab that may explain clinical results observed for these antibodies regarding both efficacy and side effects.

The association of frailty with chronic kidney disease in older adults using the ASPirin in reducing events in the elderly cohort

Frailty and chronic kidney disease (CKD) both increase with age and are prevalent in older adults. However, studies in older adults examining the relationship between frailty and milder impairments of kidney function are relatively sparse. We examined the cross-sectional association of baseline estimated glomerular filtration rate (eGFR), albuminuria and CKD ([eGFR <60 ml/min/1.73 m2 ] and/or albuminuria [>3.0 mg/mmol]) with prefrailty and frailty in the ASPirin in Reducing Events in the Elderly (ASPREE) trial cohort of healthy older participants. Univariate logistic regression models measured the unadjusted odds ratios (OR) and 95% confidence intervals (CI) for prevalent combined prefrailty and frailty (respectively defined as presence of 1-2 or 3+ of 5 modified fried criteria) for the association between CKD, eGFR, albuminuria and other potential risk factors. Multivariable models calculated OR for prefrailty-frailty adjusted for potential confounders and either CKD, (i) eGFR and albuminuria measured as either continuous variables; (ii) or categorical variables; (iii). Of 17 759 eligible participants, 6934 were classified as prefrail, 389 were frail. CKD, eGFR and albuminuria were all associated with combined prefrailty-frailty on univariate analysis. In the multivariable modelling, neither CKD (reduced eGFR and/or albuminuria), nor eGFR (either continuous or categorical variables) were associated with prefrailty-frailty. However, albuminuria, either as a continuous variable (OR [95% CI] 1.07 [1.04-1.10]; p < .001), or categorical variable (OR 1.21 [1.08-1.36]; p = .001) was consistently associated with prefrailty-frailty. The complex relationship between albuminuria (which may be a biomarker for vascular inflammation), ageing, progressive CKD and frailty requires further investigation.

NP-completeness of chromatic orthogonal art gallery problem

The chromatic orthogonal art gallery problem is a well-known problem in the computational geometry. Two points in an orthogonal polygon P see each other if there is an axis-aligned rectangle inside P contains them. An orthogonal guarding of P is k-colorable, if there is an assignment between k colors and the guards such that the visibility regions of every two guards in the same color have no intersection. The purposes of this paper are discussing the time complexity of k-colorability of orthogonal guarding and providing algorithms for the chromatic orthogonal art gallery problem. The correctness of presented solutions is proved, mathematically. Herein, the heuristic method is used that leads us to an innovative reduction, some optimal and one approximation algorithms. The paper shows that deciding k-colorability of orthogonal guarding for P is NP-complete. First, we prove that deciding 2-colorability of P is NP-complete. It is proved by a reduction from planar monotone rectilinear 3-SAT problem. After that, a reduction from graph coloring implies this is true for every fixed integer k >= 2. In the third step, we present a 6-approximation algorithm for every orthogonal simple polygon. Also, an exact algorithm is provided for histogram polygons that finds the minimum chromatic number.

Confidence Interval Constraint-Based Regularization Framework for PET Quantization

In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that rely on the direct estimation of confidence intervals for each reconstructed value. Then, the second step uses confidence intervals as a constraint to choose the most appropriate candidate according to a regularization criterion. Two different constraints are proposed in this paper. The first one has the main advantage of strictly ensuring that the regularized solution will respect the interval-valued data-fitting constraint, thus preventing over-smoothing of the solution while offering interesting properties in terms of spatial and statistical bias/variance trade-off. Another regularization proposition based on the design of a smoother constraint also with appealing properties is proposed as an alternative. The competitiveness of the proposed framework is illustrated in comparison to other regularization schemes using analytical and GATE-based simulation and real PET acquisition.

Preparation of polyvinyl chloride (PVC) membrane blended with acrylamide grafted bentonite for oily water treatment

The current work aims to advance the hydrophilicity, morphology, and antifouling characteristics of polyvinyl chloride (PVC) membranes for oily wastewater separation by incorporating modified bentonite. The surface of bentonite nanoparticles is altered by adopting the "grafting from" method using the surface-initiated atom transfer radical polymerization (SI-ATRP) approach. The PVC-based membrane is first prepared by blending acrylamide grafted bentonite (AAm-g-bentonite). AAm is grafted on bentonite in the presence of 2,2'-Bipyridyl and copper (I) bromide as a catalyst. The modified bentonite nanoparticles are studied using multiple techniques, such as fourier transform infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA), sedimentation tests, field emission scanning electron microscope (FE-SEM), etc. Flat-sheet PVC-based membrane is prepared by blending AAm-g-bentonite using the nonsolvent induced phase separation (NIPS) technique. Different methods, including FE-SEM, FTIR, sedimentation test, contact angle, porosity, antifouling property, and filtration studies of pure and oily water, are used to characterize and determine the performance of mixed-matrix membranes. Membrane performance is improved in the presence of modified bentonite (i.e., AAm-g-bentonite), with the best result achieved at PVC/AAm-g-ben-8 (i.e., 8 wt % of AAm-g-bentonite). Enhanced pure water flux (293.14 Lm-2h-1), permeate flux (123.96 Lm-2h-1), and oil rejection >93.2% are obtained by the reduced contact angle (49.1°) and improved porosity (71.22%).

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors

In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in the state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network-based image segmentation.

Sensors for Robotic Hands: A Survey of State of the Art

Recent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors. We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands.