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Creative, embodied practices, and the potentialities for sustainability transformations

This paper argues for an integrative approach to sustainability transformations, one that reconnects body and mind, that fuses art and science and that integrates diverse forms of knowledge in an open, collaborative and creative way. It responds to scholarship emphasizing the importance of connecting disparate ways of knowing, including scientific, artistic, embodied and local knowledges to better understand environmental change and to foster community resilience and engagement. This paper draws on the experience of an arts-based project in Lisbon, Portugal, and explores embodied and performative practices and their potential for climate change transformations. It puts forward and enlivens an example, where such forms of engaging communities can provide new insight into how equitable, just and sustainable transformations can come about. The process involved a series of interactive workshops with diverse arts-based methods and embodied practices to create performative material. From this process, a space emerged for the creation of meaning about climate change. Three key elements stood out in this process as being potentially important for the emergence of meaning-making and for understanding the impact of the project: the use of metaphors, embedding the project locally, and the use of creative, embodied practices. This furthers research, suggesting that the arts can play a critical role in engaging people with new perspectives on climate change and sustainability issues by offering opportunities for critical reflection and providing spaces for creative imagination and experimentation. Such processes may be important for contributing to the changes needed to realize transformations to sustainability.

Optimisation of low voltage power MOSFET components for high current applications

Achievable current density of low voltage power MOSFET components has increased significantly over the past years. Those devices are mainly used in converters for automotive auxiliary drives or renewable energy. The paper outlines state of the art technology and gives an outlook on further development, taking into account the particular requirements of the aforementioned demanding applications.

Fragile Bits in Palmprint Recognition

Recent years have witnessed a growing interest in developing automatic palmprint recognition methods. Among them, coding-based ones, representing the texture of a palmprint using a binary code, are most prevalent and successful. We find that not all bits in a code map generated by a specific coding scheme are equally consistent. A bit is deemed fragile if its value changes across code maps created from different images of the same palmprint. In this paper, we first analyze the fragile bits phenomenon in a state-of-the-art palmprint coding scheme, namely, binary orientation co-occurrence vector (BOCV). Then, based on our analysis, we extend BOCV to E-BOCV by incorporating fragile bits information in appropriate ways. Experiments conducted on the benchmark dataset demonstrate that E-BOCV can achieve the highest verification accuracy among all the state-of-the-art palmprint verification methods evaluated. To our knowledge, this is the first work investigating the fragile bits of coding-based palmprint recognition approaches.

Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging

Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov's smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.

Identification, taste characterization, and molecular docking study of a novel microbiota-derived umami peptide

Umami peptides are an important taste substance in fermented foods. However, in the absence of known microbiota-derived umami peptides, the understanding of the umami mechanism remains unclear. Tetragenococcus halophilus, a dominant fermentation bacteria, may be an important source of umami peptides. Accordingly, T. halophilus fermentation broth was fractioned by ethanol precipitation, gel chromatography, and reverse phase-high performance liquid chromatography. The isolated peptide fraction with the most intense umami taste was screened by amino acid composition and sensory analyses. Finally, three novel microbiota-derived peptides (DFE, LAGE, and QLQ) were identified, synthesized, and characterized for taste. Among them, only DFE had umami and umami-enhancing abilities improving multiple tastes. Molecular docking studies indicated that DEF binds to T1R1/T1R3 receptors through hydrogen bonding and electrostatic interactions involving receptor residues Ser332, Ser256, ASN41, His125, etc. This study highlights the critical role of microbiota-derived peptides in the umami taste of fermented foods.

Digital Airborne Photogrammetry-A New Tool for Quantitative Remote Sensing?-A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images. The analysis is based on a literature research and a questionnaire submitted to various interest groups related to the photogrammetric process. An important contribution to this paper is a characterization of the photogrammetric image acquisition and image product generation systems. The questionnaire revealed many weaknesses in current processes, but the future prospects of radiometrically quantitative photogrammetry are promising.

Safety evaluation of the food enzyme β-galactosidase from the genetically modified Kluyveromyces lactis strain KLA

The food enzyme β-galactosidase (β-d-galactoside galactohydrolase; EC 3.2.1.23) is produced with the genetically modified Kluyveromyces lactis strain KLA by DSM Food Specialties B.V. The genetic modifications did not give rise to safety concerns. The food enzyme was considered free from viable cells of the production organism and its DNA. The food enzyme is intended to be used for the lactose hydrolysis in milk processing, production of fermented milk products and whey processing. It is also intended for lactose hydrolysis in milk products at home. Dietary exposure to the food enzyme-total organic solids (TOS) was estimated to be up to 11.876 mg TOS/kg body weight per day in European populations. The production strain of the food enzyme fulfils the requirements for the Qualified Presumption of Safety (QPS) approach to safety assessment. As no concerns arising from its genetic modification or from the manufacturing process have been identified, the Panel considered that toxicological tests are not needed for the assessment of this food enzyme. A search for similarity of the amino acid sequence of the food enzyme to known allergens was made and no match was found. The Panel considered that, under the intended conditions of use, the risk of allergic reactions by dietary exposure cannot be excluded, but the likelihood for this to occur is low. The Panel concluded that this food enzyme does not give rise to safety concerns under the intended conditions of use.

Pemphigus vegetans misdiagnosed as condylomata acuminata: A case report

Pemphigus vegetans is a rare variant of pemphigus vulgaris, characterized by vegetating lesions primarily in the flexures. A 41-year-old male patient presented with pemphigus vegetans highly mimicking condylomata acuminata, which led to mistreatment. Careful analysis of clinical and laboratory findings enabled us to reach a correct diagnosis and successful treatment.

Robust Feature Matching in Long-Running Poor-Quality Videos

We describe a methodology that is designed to match key point and region-based features in real-world images, acquired from long-running security cameras with no control over the environment. We detect frame duplication and images from static scenes that have no activity to prevent processing saliently identical images, and describe a novel blur-sensitive feature detection method, a combinatorial feature descriptor, and a distance calculation that efficiently unites texture and color attributes to discriminate feature correspondence in low-quality images. Our methods are tested by performing key point matching on real-world security images such as outdoor closed-circuit television videos that are low quality and acquired in uncontrolled conditions with visual distortions caused by weather, crowded scenes, emergency lighting, or the high angle of the camera mounting. We demonstrate an improvement in accuracy of matching key points between images compared with state-of-the-art feature descriptors. We use key point features from a Harris corner detector, scale-invariant feature transform, speeded-up robust features, binary robust invariant scalable keypoints, and features from accelerated segment test as well as MSER and MSCR region detectors to provide a comprehensive analysis of our generic method. We demonstrate feature matching using a 138D descriptor that improves the matching performance of a state-of-the-art 384D color descriptor with just 36% of the storage requirements.

Evaluation of the antimicrobial attribute of bioactive peptides derived from colostrum whey fermented by Lactobacillus against diarrheagenic E. coli strains

Colostrum known as "liquid gold" contains approximately 60-80% of whey proteins that can be a great source of bioactive peptide production. Therefore, this study aimed to perform a comparative antimicrobial evaluation of the bioactive peptide generated from L. rhamnosus C25, L. rhamnosus C6, and L. casei NCDC17 fermented colostrum whey. Peptide fractions 10 kDa, 5 kDa, and 3 kDa were isolated using their respective molecular weight cut-off membranes and antimicrobial activity was evaluated against diarrheagenic E. coli strains. The higher inhibition was shown by < 10 kDa peptide fractions from L. rhamnosus C25 fermented colostrum whey and the zone of inhibition was 15 ± 0.06 (E. coli MTCC 723), 17 ± 0.04 (E. coli MTCC 724), 18 ± 0.05 (E. coli MTCC 725), and 17 ± 0.02 (E. coli ATCC 25922). In addition, ST-1 and LT-1 genes of E. coli strains were also confirmed using PCR which is responsible for the diarrheagenic property. Further, the interaction of potent peptides against E. coli strains was also observed by scanning electron microscope. Hence, the significance of the present study emphasized that these bioactive peptides generated from fermented colostrum whey can be used as ingredients in functional food against diarrhoea.

Orientia tsutsugamushi in Chiggers and Small Mammals in Laos

Background: Scrub typhus is a leading cause of febrile illness in Laos and accounts for a high burden of disease. There have been no previous studies on the causative agent, Orientia tsutsugamushi, in vector mites ("chiggers") or their small mammal hosts in Laos. Materials and Methods: Small mammals and free-living chiggers were trapped in districts of Vientiane Province and Capital. Tissues were tested for O. tsutsugamushi by PCR and serum for IgG to O. tsutsugamushi by immunofluorescence assays (IFAs). Chiggers removed from small mammals and collected in their free-living stage using black plates were identified and tested for O. tsutsugamushi by PCR. Results: Over an 18-month period, 131 small mammals of 14 species were collected in 5 districts. Seventy-eight of 131 small mammals were infested with chiggers, but all tissues were O. tsutsugamushi PCR negative. Eighteen species of chigger were identified and 1,609 were tested by PCR. A single pool of chiggers tested O. tsutsugamushi positive. Sera from 52 small mammals were tested by IFA, with 16 testing positive. Conclusions: These are the first molecular and serological data on O. tsutsugamushi in chiggers and small mammals in Laos. Further studies are needed to better understand the key vector species and ecology of scrub typhus in areas with high disease incidence in Laos.

Multimodal Emotion Recognition With Transformer-Based Self Supervised Feature Fusion

Emotion Recognition is a challenging research area given its complex nature, and humans express emotional cues across various modalities such as language, facial expressions, and speech. Representation and fusion of features are the most crucial tasks in multimodal emotion recognition research. Self Supervised Learning (SSL) has become a prominent and influential research direction in representation learning, where researchers have access to pre-trained SSL models that represent different data modalities. For the first time in the literature, we represent three input modalities of text, audio (speech), and vision with features extracted from independently pre-trained SSL models in this paper. Given the high dimensional nature of SSL features, we introduce a novel Transformers and Attention-based fusion mechanism that can combine multimodal SSL features and achieve state-of-the-art results for the task of multimodal emotion recognition. We benchmark and evaluate our work to show that our model is robust and outperforms the state-of-the-art models on four datasets.

A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models

In this paper, we propose a simple signal estimation algorithm based on multiple wavelet representations and Gaussian observation models. The proposed algorithm has two major steps: a joint-optimum estimation of the wavelet coefficients and an averaging of the denoised images. Experimental results show that the denoising performance of proposed algorithm is comparable to that of the state of the art. (c) 2006 Elsevier B.V. All rights reserved.

Core-Shell Structured Porous Calcium Phosphate Bioceramic Spheres for Enhanced Bone Regeneration

Adequate new bone regeneration in bone defects has always been a challenge as it requires excellent and efficient osteogenesis. Calcium phosphate (CaP) bioceramics, including hydroxyapatite (HA) and biphasic calcium phosphates (BCPs), have been extensively used in clinical bone defect filling due to their good osteoinductivity and biodegradability. Here, for the first time, we designed and fabricated two porous CaP bioceramic granules with core-shell structures, named in accordance with their composition as BCP@HA and HA@BCP (core@shell). The spherical shape and the porous structure of these granules were achieved by the calcium alginate gel molding technology combined with a H2O2 foaming process. These granules could be stacked to build a porous structure with a porosity of 65-70% and a micropore size distribution between 150 and 450 μm, which is reported to be good for new bone ingrowth. In vitro experiments confirmed that HA@BCP bioceramic granules could promote the proliferation and osteogenic ability when cocultured with bone marrow mesenchymal stem cells, while inhibiting the differentiation of RAW264.7 cells into osteoclasts. In vivo, 12 weeks of implantation in a critical-sized femoral bone defect animal model showed a higher bone volume fraction and bone mineral density in the HA@BCP group than in the BCP@HA or pure HA or BCP groups. From histological analysis, we discovered that the new bone tissue in the HA@BCP group was invading from the surface to the inside of the granules, and most of the bioceramic phase was replaced by the new bone. A higher degree of vascularization at the defect region repaired by HA@BCP was revealed by 3D microvascular perfusion angiography in terms of a higher vessel volume fraction. The current study demonstrated that the core-shell structured HA@BCP bioceramic granules could be a promising candidate for bone defect repair.

Overview of Battery Impedance Modeling Including Detailed State-of-the-Art Cylindrical 18650 Lithium-Ion Battery Cell Comparisons

Electrical models of battery cells are used in simulations to represent batteries' behavior in various fields of research and development involving battery cells and systems. Electrical equivalent circuit models, either linear or nonlinear, are commonly used for this purpose and are presented in this article. Various commercially available cylindrical, state-of-the-art lithium-ion battery cells, both protected and unprotected, are considered. Their impedance properties, according to four different equivalent circuit models, are measured using electrochemical impedance spectroscopies. Furthermore, the pricing, impedance, specific energy, and C-rate of the chosen battery cells are compared. For example, it is shown that the energy density of modern 18650 cells can vary from a typical value of 200 to about 260 Wh kg(-1), whereas the cell price can deviate by a factor of about 3 to 5. Therefore, as a result, this study presents a concise but comprehensive battery parameter library that should aid battery system designers or power electronic engineers in conducting battery simulations and in selecting appropriate battery cells based on application-specific requirements. In addition, the accuracies and computational efforts of the four equivalent circuit models are compared.

Novel Multi-IMU Tight Coupling Pedestrian Localization Exploiting Biomechanical Motion Constraints

In this article, we present a novel tight coupling inertial localization system which simultaneously processes the measurements of two inertial measurement units (IMUs) mounted on the leg, namely the upper thigh and the front part of the foot. Moreover, the proposed system exploits motion constraints of each leg link; that is, the thigh and the foot. To derive these constraints, we carry out a motion tracking experiment to collect both ground truth data and inertial measurements from IMUs mounted on the leg. The performance of the tight coupling system is assessed with a data set of approximately 10 h. The evaluation shows that the average 2D-position error of the proposed tight coupling system is at least 50% better than the average 2D-position error of two state-of-the-art systems, whereas the average height error of the tight coupling system is at least 75% better than the average height error of the two state-of-the-art systems. In this work, we improve the accuracy of the position estimation by introducing biomechanical constraints in an inertial localization system. This article allows to observe, for the first time, heading errors of an inertial localization system by using only inertial measurements and without the need for using maps or repeating totally or partially the walked trajectory.

A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal & Multi-view & Interactive ((MI)-I-2) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).

Graph-Based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

The increasing availability of digital pathology images has motivated the design of tools to foster multi-disciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilitates the transmission and access to pathology images by multiple users. However, the huge file size associated with pathology images usually prevents full exploitation of the collaborative telepathology system potential. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. In this paper, we propose a novel graph-based RC algorithm with lossless region of interest (RoI) coding for pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R-lambda) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the High-Efficiency Video Coding standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms the other state-of-the-art-methods designed for single images by very accurately attaining the target bit rate of the non-RoI.

Triggered Self-Sorting of Peptides to Form Higher-Order Assemblies in a Living System

Biological components (protein, DNA, lipid rafts, etc.) self-sort to form higher-order structures with elegant modulation by endogenous stimuli for maintaining cellular functions in living cells. However, the challenge of producing self-sorted higher-order assemblies of peptides in living systems (cells and tissues) spatiotemporally has yet to be achieved. This work reports the using of a biocompatible strategy to construct self-sorted assemblies of peptides in living cells and tumor-bearing mice. The results show that the designed peptides self-sort to form distinct nanostructures in living cancer cells using an endogenous trigger, as evidenced by confocal laser scanning microscopy and Bio-EM. Wound-healing experiments indicate that the in situ generation of self-sorted nanostructures exhibits a synergistic effect that significantly decreases the migration of cancer cells. In vivo experiments demonstrate that the designed peptides could self-sort in tumor-bearing mice and improve the tumor penetrating ability of the impenetrable component in tumor tissue. We can further program the formation of self-sorted materials through orthogonal triggers by introducing an exogenous trigger (light) and an endogenous trigger independently. Thus, this work provides a strategy to control multiple self-assembling processes in the context of the living system and provides a general strategy to construct self-sorted structures for the emergent properties of materials science.

CHANCE: Capacitor Charging Management Scheme in Energy Harvesting Systems

The energy efficiency of emerging nonvolatile processors equipped with FRAM-SRAM memory makes them a promising solution for energy harvesting systems. To enable correct functionality and forward progress with an unreliable power supply, the system must accumulate sufficient energy in the capacitor to execute tasks atomically, even in the worst case scenario. Due to the large gap between the average and worst case energy consumption of tasks, state-of-the-art approaches like eM-map require a large capacitor to execute tasks on the SRAM. However, the size, cost, and charging time of the capacitor are major concerns in the energy harvesting systems. In this article, we proposed CHANCE, a capacitor charging management scheme that improves the capacitor size and average response time of an energy harvesting system. CHANCE analyses the energy consumption of tasks to set an appropriate capacitor size to make a balance between capacitor charging time and failure rate for each task. The results show that CHANCE improves the response time of state-of-the-art approaches up to 68% with a five times smaller capacitor.

Divergent responses of inflammatory mediators within the amygdala and medial prefrontal cortex to acute psychological stress

There is now a growing body of literature that indicates that stress can initiate inflammatory processes, both in the periphery and brain; however, the spatiotemporal nature of this response is not well characterized. The aim of this study was to examine the effects of an acute psychological stress on changes in mRNA and protein levels of a wide range of inflammatory mediators across a broad temporal range, in key corticolimbic brain regions involved in the regulation of the stress response (amygdala, hippocampus, hypothalamus, medial prefrontal cortex). mRNA levels of inflammatory mediators were analyzed immediately following 30min or 120min of acute restraint stress and protein levels were examined 0h through 24h post-termination of 120min of acute restraint stress using both multiplex and ELISA methods. Our data demonstrate, for the first time, that exposure to acute psychological stress results in an increase in the protein level of several inflammatory mediators in the amygdala while concomitantly producing a decrease in the protein level of multiple inflammatory mediators within the medial prefrontal cortex. This pattern of changes seemed largely restricted to the amygdala and medial prefrontal cortex, with stress producing few changes in the mRNA or protein levels of inflammatory mediators within the hippocampus or hypothalamus. Consistent with previous research, stress resulted in a general elevation in multiple inflammatory mediators within the circulation. These data indicate that neuroinflammatory responses to stress do not appear to be generalized across brain structures and exhibit a high degree of spatiotemporal specificity. Given the impact of inflammatory signaling on neural excitability and emotional behavior, these data may provide a platform with which to explore the importance of inflammatory signaling within the prefrontocortical-amygdala circuit in the regulation of the neurobehavioral responses to stress.

Extraordinary Creatures: The Role of Birds in Early Iron Age Slovenia

Depictions of birds are overrepresented in the Dolenjska Hallstatt culture, and appear on over a quarter of artefacts depicting animals. A wide variety of artefacts with birds have been found primarily in graves, and crosscut gender, status, and age. However, poor preservation of zooarchaeological remains has made reconstructions of lived human-bird interactions difficult. This study uses ecological and ethological data, combined with local imagery, to provide insight into prehistoric human-bird interfaces in this area, and the cultural conceptions surrounding these interactions. Birds would have been a constant presence in the lives of Dolenjska Hallstatt people; however, human relationships with them were based more on observation than direct interaction. Birds were ubiquitous in imagery, and it is proposed that this stemmed from Dolenjska Hallstatt conceptions of birds as important observers of human actions, ritual mediators, and possibly guides or guardians. Their differences from humans and other animals distinguished them - they were set apart, and depictions highlighted non-normative behaviours. Birds in the Dolenjska Hallstatt worldview were more than animals, ascribed extraordinary capabilities that made them ritually potent and richly symbolic creatures.

Unsupervised cross-domain person re-identification with self-attention and joint-flexible optimization

Unsupervised domain adaptation (UDA) for person re-identication (ReID) remains a challenging task, as the trained ReID system often fails to adapting to a new dataset. Due to the lack of supervision of real labels, the performance of the UDA models suffers from inefficient feature learning and inevitable pseudo label noise. In this work, we tackle the problems by designing an effective dual-path mutual-learning framework which can capture effective information for better feature learning and mitigate the impact of label noise. Firstly, to reduce the impact of occlusion and viewpoints, we introduce the self-attention mechanism in a two-stage strategy making the models focus on the key areas of identifying people. Secondly, considering that UDA is an open-set task, we leverage density-based spatial clustering of applications with noise (DBSCAN) to avoid manually setting the number of classes of the target domain. Thirdly, for realizing joint and flexible optimization under the supervision of soft pseudo labels and hard pseudo labels, a joint and flexible loss (JFL) is proposed to train the network. Experiments on three large-scale datasets show that our model outperforms the state-of-the-art UDA methods in both mAP and top-1 evaluation protocols by large margins. Especially on task of Duke-to-Market, our method outperforms the state-of-the-art by 6.9% mAP. (c) 2021 Elsevier B.V. All rights reserved.

EAD-GAN: A Generative Adversarial Network for Disentangling Affine Transforms in Images

This article proposes a generative adversarial network called explicit affine disentangled generative adversarial network (EAD-GAN), which explicitly disentangles affine transform in a self-supervised manner. We propose an affine transform regularizer to force the InfoGAN to have explicit properties of affine transform. To facilitate training an affine transform encoder, we decompose the affine matrix into two separate matrices and infer the explicit transform parameters by the least-squares method. Unlike the existing approaches, representations learned by the proposed EAD-GAN have clear physical meaning, where transforms, such as rotation, horizontal and vertical zooms, skews, and translations, are explicitly learned from training data. Thus, we set different values of each transform parameter individually to generate specifically affine transformed data by the learned network. We show that the proposed EAD-GAN successfully disentangles these attributes on the MNIST, CelebA, and dSprites datasets. EAD-GAN achieves higher disentanglement scores with a large margin compared to the state-of-the-art methods on the dSprites dataset. For example, on the dSprites dataset, EAD-GAN achieves the MIG and DCI score of 0.59 and 0.96 respectively, compared to 0.37 and 0.71, respectively, for the state-of-the-art methods.

Practical Implementation and Challenges of Artificial Intelligence-Driven Electronic Health Record Evaluation: Protected Health Information

Detecting protected health information in electronic health record systems is often an early step in health care analytics, and it is a nontrivial problem. Specific challenges include finding clinician names and diseases, which lack a fixed format and are often context-dependent. The general problem of finding entities, termed named-entity recognition, has received a substantial amount of attention in the natural language processing and deep learning communities. This paper begins by outlining recent methods for finding protected health information, and it then introduces a hybrid system which combines regular expressions with a natural language processing framework called FLAIR. FLAIR is open-source, it includes state-of-the-art deep learning models, and it supports straightforward development of new models for language tasks including named-entity recognition. Finally, there is a discussion of how to apply the system to structured text in a database table as well as unstructured text in clinical notes.

Robust visual tracking by embedding combination and weighted-gradient optimization

Existing tracking-by-detection approaches build trackers on binary classifiers. Despite achieving state-of-the-art performance on tracking benchmarks, these trackers pay limited attention to data imbalance issue, e.g, positive and negative, easy and hard. In this paper, we demonstrate that separately learning feature embeddings corresponding to negative samples with different semantic characteristics is effective in reducing the background diversity to handle the imbalance between positive and negative samples, which facilitates background awareness of classifiers. Specifically, we propose a negative sample embedding combination network, which helps to learn several sub-embeddings and combine them to build a robust classifier. In addition, we propose a weighted-gradient loss to handle the imbalance between easy and hard samples. The gradient contribution of each sample to model training is dynamically weighted according to the gradient distribution, which prevents easy samples from overwhelming model training. Extensive experiments on benchmarks demonstrate that our tracker performs favorably against state-of-the-art algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

Image recognition of martial arts movements based on FPGA and image processing

The work is to perceive the intricate karate activity groupings consequently. It is to give a proportion of the nature of the activity performed. This is because there is a problem that requires essentially model. This work can be removed from the two motion capture hardware and consumer derived from the input sequence. The solution for the skeleton's movement proposed a depth-sensing system. The speed of the body's action because there is a karate sport classification. On the other hand, because the practice with even skilled martial arts for years, behavior patterns are repetitive. Similarly, there is no interest in the commercial market for solutions used in today's computer entertainment and tutoring system. The future of collecting analysis of elements present in the correct posture, accelerated by a punch and kick, the different aspects of performance, such as synchronizing the limb's movement and evaluation. This work aims to provide a survey to evaluate the quality of the whole body movement technology. It proposes a data set and its plan to implement the first reference file of the work's future. Generally speaking, FPGA is programmable with a group of programmable interconnect resources through digital circuits or programmable logic blocks surrounded by input/output blocks. Any system silicon chip is put together. The processor and the FPGA are essentially indeed parallel.

Spin-Coating-Based Facile Annular Photodynamic Intraocular Lens Fabrication for Efficient and Safer Posterior Capsular Opacification Prevention

Posterior capsular opacification (PCO) is the most common complication after cataract surgery, which is primarily caused by the proliferation of the residual lens epithelial cells (LECs) in the lens capsule. Previous studies have demonstrated that a drug-eluting intraocular lens (IOL), aimed to in situ eliminate LECs, are an effective and promising way to prevent PCO. However, because of the potential toxicities of the antiproliferative drugs to the adjacent tissues, the safety of such drug-eluting IOLs is still a highly important issue to be solved. In this investigation, a facile photodynamic coating-modified IOL was developed for effective and safer PCO prevention. An annular poly(lactide-co-glycolic acid) (PLGA) coating loaded with photosensitizer chlorin e6 (Ce6) was prepared by a spin-coating technique. The optical property investigations showed that the Ce6@PLGA coating was particularly suitable for the IOL surface modification. The in vitro cell culture investigation showed that Ce6@PLGA coating-modified IOLs effectively eliminated LECs when treated with light illumination, whereas it appeared to have good cytocompatibility without irradiation. The investigation of the cell elimination mechanism showed that the apoptosis of HLECs may be associated with the cytomembrane disruption induced by ROS, which is generated by the photodynamic coating during light illumination. The in vivo implantation experiments confirmed the desired PCO prevention effect, as well as the safety to and biocompatibility with the surrounding tissues. Thus, the facile Ce6@PLGA coating will provide an effective yet safe alternative of IOL surface modification for PCO prevention.

Knowledge Adaptation with Partially Shared Features for Event Detection Using Few Exemplars

Multimedia event detection (MED) is an emerging area of research. Previous work mainly focuses on simple event detection in sports and news videos, or abnormality detection in surveillance videos. In contrast, we focus on detecting more complicated and generic events that gain more users' interest, and we explore an effective solution for MED. Moreover, our solution only uses few positive examples since precisely labeled multimedia content is scarce in the real world. As the information from these few positive examples is limited, we propose using knowledge adaptation to facilitate event detection. Different from the state of the art, our algorithm is able to adapt knowledge from another source for MED even if the features of the source and the target are partially different, but overlapping. Avoiding the requirement that the two domains are consistent in feature types is desirable as data collection platforms change or augment their capabilities and we should be able to respond to this with little or no effort. We perform extensive experiments on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other state-of-the-art detection algorithms.

How cynicism and exhaustion influence the turnover intention of medical social workers: moderation effect of social work educational background and organizational type

Although exhaustion and cynicism are two dimensions of burnout, due to professionalism, they have different influence on the turnover intentions of medical social workers. Using a sample of 405 medical social workers in China, this study found that the influence mechanisms of exhaustion and cynicism on turnover intention are different. Social work educational background has a significant moderation effect on the relationships between exhaustion, cynicism, and turnover intention. A moderation effect of organizational type was also observed, although it was not significant.

Hard Sample Aware Noise Robust Learning for Histopathology Image Classification

Deep learning-based histopathology image classification is a key technique to help physicians in improving the accuracy and promptness of cancer diagnosis. However, the noisy labels are often inevitable in the complex manual annotation process, and thus mislead the training of the classification model. In this work, we introduce a novel hard sample aware noise robust learning method for histopathology image classification. To distinguish the informative hard samples from the harmful noisy ones, we build an easy/hard/noisy (EHN) detection model by using the sample training history. Then we integrate the EHN into a self-training architecture to lower the noise rate through gradually label correction. With the obtained almost clean dataset, we further propose a noise suppressing and hard enhancing (NSHE) scheme to train the noise robust model. Compared with the previous works, our method can save more clean samples and can be directly applied to the real-world noisy dataset scenario without using a clean subset. Experimental results demonstrate that the proposed scheme outperforms the current state-of-the-art methods in both the synthetic and real-world noisy datasets. The source code and data are available at https://github.com/bupt-ai-cz/HSA-NRL/.

Can air pollution reduce technology transfer? Evidence from China's prefecture-level cities

Air pollution hinders technological innovation, but the causal effects of air pollution on technology transfer are overlooked. We use thermal inversion as an instrumental variable for addressing air pollution endogeneity. The empirical results show that a one-unit increase in air pollution reduces technology transfer strength by 4.5 %. However, air pollution has a strong asymmetry in two directions of technology transfer strength. We find that those cities with worse pollution have an intention to transfer their technologies to cities in other provinces. In the PLFC model, heterogeneity varying with GDP can be addressed to estimate the marginal effect between them. Findings suggest that the marginal effects of air pollution on technology transfer can be divided into two parts based on GDP levels. Within the thresholds (lngdp = 11), the effect of environmental regulations will keep increasing and keep stable beyond the thresholds. In addition, different effects on different cities are also discussed.

Prolificacy Assessment of Spermatozoan via State-of-the-Art Deep Learning Frameworks

Childlessness or infertility among couples has become a global health concern. Due to the rise in infertility, couples are looking for medical supports to attain reproduction. This paper deals with diagnosing infertility among men and the major factor in diagnosing infertility among men is the Sperm Morphology Analysis (SMA). In this manuscript, we explore establishing deep learning frameworks to automate the classification problem in the fertilization of sperm cells. We investigate the performance of multiple state-of-the-art deep neural networks on the MHSMA dataset. The experimental results demonstrate that the deep learning-based framework outperforms human experts on sperm classification in terms of accuracy, throughput and reliability. We further analyse the sperm cell data by visualizing the feature activations of the deep learning models, providing a new perspective to understand the data. Finally, a comprehensive analysis is also demonstrated on the experimental results obtained and attributing them to pertinent reasons.

Behavioral similarity of residential customers using a neural network based on adaptive resonance theory

This article presents a methodology aiming at the comprehension and analysis of the residential electricity consumption habitual behavior by means of a similarity analysis, based on the use of an ART (Adaptive Resonance Theory) neural network, which is a neural network composed of two fuzzy ART modules whose training is performed in an unsupervised mode. ART neural networks are stable and plastic and these properties, combined with the processing of essentially binary data, give the neural system a wide capacity for producing objectives that may be easily modified to satisfy requirements predetermined by consumer. The expected result is to obtain information regarding the similarity of consumers. Thus, some benefits may be derived by consumers, such as improved habits of electricity consumption and better strategies for negotiating more favorable rates, especially in the case of smart grid systems. In this new electricity sector paradigm, there is a strong consumer trend for free choice among electricity suppliers. This methodology also benefits load forecasting studies at grid points, where there is greater uncertainty, e. g., the busbars that are closest to consumers, i.e., the uncertainties in the context of the total load forecasting system are increased from the global load to the final consumer.

Hybrid Fuzzy C-Means CPD-Based Segmentation for Improving Sensor-Based Multiresident Activity Recognition

Multiresident activity recognition (AR), which has become a popular research field in smart environments, aims to recognize the activities of multiple residents based on data collected from various types of sensors, and sensor events segmentation is an important technique for enhancing the performance of AR. While quite some segmentation methods have been proposed for the single person setting, few studies have been done for the multiresident setting. In this article, we first evaluate the baseline and the state-of-the-art segmentation methods using the popular multiresident data set CASAS, to confirm that the performance of multiresident AR can be improved by applying segmentation techniques; we then propose a novel Hybrid fuzzy c-means (FCM) change point detection (CPD)-based segmentation method that can further enhance the performance of multiresident AR. We combine a FCM method with a CPD-based method for sensor event segmentation. The FCM method is used to classify the sensor events in terms of sensor locations, and then the CPD technique is used to probe the transition actions to determine the segmentation sequence. Our experimental results show that the proposed method significantly improves the performance of multiresident AR in comparison with the baseline and state-of-the-art classification methods.

Gene expression profile suggests different mechanisms underlying sporadic and familial mesial temporal lobe epilepsy

Most patients with pharmacoresistant mesial temporal lobe epilepsy (MTLE) have hippocampal sclerosis on the postoperative histopathological examination. Although most patients with MTLE do not refer to a family history of the disease, familial forms of MTLE have been reported. We studied surgical specimens from patients with MTLE who had epilepsy surgery for medically intractable seizures. We assessed and compared gene expression profiles of the tissue lesion found in patients with familial MTLE (n = 3) and sporadic MTLE (n = 5). In addition, we used data from control hippocampi obtained from a public database (n = 7). We obtained expression profiles using the Human Genome U133 Plus 2.0 (Affymetrix) microarray platform. Overall, the molecular profile identified in familial MTLE differed from that in sporadic MTLE. In the tissue of patients with familial MTLE, we found an over-representation of the biological pathways related to protein response, mRNA processing, and synaptic plasticity and function. In sporadic MTLE, the gene expression profile suggests that the inflammatory response is highly activated. In addition, we found enrichment of gene sets involved in inflammatory cytokines and mediators and chemokine receptor pathways in both groups. However, in sporadic MTLE, we also found enrichment of epidermal growth factor signaling, prostaglandin synthesis and regulation, and microglia pathogen phagocytosis pathways. Furthermore, based on the gene expression signatures, we identified different potential compounds to treat patients with familial and sporadic MTLE. To our knowledge, this is the first study assessing the mRNA profile in surgical tissue obtained from patients with familial MTLE and comparing it with sporadic MTLE. Our results clearly show that, despite phenotypic similarities, both forms of MTLE present distinct molecular signatures, thus suggesting different underlying molecular mechanisms that may require distinct therapeutic approaches.

A comprehensive assessment of the state-of-the-art virtual synchronous generator models

The large-scale penetration of renewable energy sources has a significant impact on the steady-state operation and emergency control of modern electric power systems (EPS). Moreover, as a rule, the mentioned impact is negative. Therefore, the challenge associated with the development of a 'comprehensive' control strategy for renewables becomes extremely relevant. Such a strategy should ensure the same reliable operation of EPSs, as in the case of conventional synchronous generation. Among the existing directions, the concept of a virtual synchronous generator (VSG) is the most preferable. Within this paper, the synthesis and combination of the state-ofthe-art concepts of VSG into generic structures has been performed. For the obtained structures, a comprehensive assessment and an experimental comparison were performed. On the basis of the results obtained, the benefits and drawbacks of the generic structures are highlighted, and a conclusion is made about the VSG structure, demonstrating the most effective and reliable operation.

Depression and Quality of Life among Patients Living with HIV/AIDS in the Era of Universal Treatment Access in Vietnam

Although antiretroviral treatment (ART) access has been universal in recent years, few studies have examined if this policy contributes to the mental health of the patients. This study assessed depression and its relations with health-related quality of life (HRQOL), which is defined as the status of general well-being, physical, emotional, and psychological, among HIV patients. A cross-sectional study was conducted in 482 patients at five outpatient clinics. Patient Health Questionnaire-9 (PHQ-9) and EuroQol-5 dimensions-5 levels (EQ-5D-5L) were used to assess the severity of depression and HRQOL. About one-fifth of patients reported symptoms of depression. According to the result of a multivariate logistic regression model, patients who had a lower number of CD4 cells at the start of ART, who received ART in the clinic without HIV counseling and testing (HCT) services, who had a physical health problem, and who experienced discrimination were more likely to have depression. Depression was associated with significantly decreased HRQOL. Depression is prevalent and significantly negatively associated with HRQOL of HIV/AIDS patients. We recommend screening for depression and intervening in the lives of depressed individuals with respect to those who start ART late, and we also recommend community-based behavioral change campaigns to reduce HIV discrimination.

A 400 mV 160 nW/Ch Compact Energy Efficient Delta Sigma Modulator for Multichannel Biopotential Signal Acquisition System

Analog to digital converter circuit design for biomedical systems with multiple recording channels presents challenges in high density and very low power consumption. Passive integrator and loop-filter based delta-sigma modulators (DSMs) have been recently reported for ultra-low-power and highly energy-efficient data converters for multi-channel biopotential acquisition. However, these modulators rely on a very high oversampling ratio (OSR) to achieve the target resolution. Higher OSR leads to higher power consumption in the modulator and the digital low-pass and decimation filter succeeding the DSM. We present a low OSR passive integrator-based DSM in this work by relying on a duty-cycled resistor (DCR). DCR enables the realization of large time constants in the already passive loop-filter, with minimal area and overhead power consumption. This leads to design ofDSMsthat are highly area, power and energy-efficient, suitable for multi-channel biopotential recording systems. We demonstrate a second order, duty-cycled passive integrator based CTDSM in a 65 nm CMOS technology for a 10 kHz biopotential bandwidth. Measurement results show that the fabricated design achieves an SNDR/DR of 56.36/63.1 dB while consuming only 160 nW power with an OSR of 32 and occupies an area of 0.035 mm(2) with a state-of-the-art energy efficiency of 14.9 fJ/conv. In-vitro and in-vivo measurements are provided to further demonstrate the operation of the proposed DSM.

Random Discriminative Projection Based Feature Selection with Application to Conflict Recognition

Computational paralinguistics deals with underlying meaning of the verbal messages, which is of interest in manifold applications ranging from intelligent tutoring systems to affect sensitive robots. The state-of-the-art pipeline of paralinguistic speech analysis utilizes brute-force feature extraction, and the features need to be tailored according to the relevant task. In this work, we extend a recent discriminative projection based feature selection method using the power of stochasticity to overcome local minima and to reduce the computational complexity. The proposed approach assigns weights both to groups and to features individually in many randomly selected contexts and then combines them for a final ranking. The efficacy of the proposed method is shown in a recent paralinguistic challenge corpus to detect level of conflict in dyadic and group conversations. We advance the state-of-the-art in this corpus using the INTERSPEECH 2013 Challenge protocol.

Clinical application guidelines for blood glucose monitoring in China (2022 edition)

Glucose monitoring is an important component of diabetes management. The Chinese Diabetes Society (CDS) has been producing evidence-based guidelines on the optimal use of glucose monitoring since 2011. In recent years, new technologies in glucose monitoring and more clinical evidence, especially those derived from Chinese populations, have emerged. In this context, the CDS organised experts to revise the Clinical application guidelines for blood glucose monitoring in China in 2021. In this guideline, we focus on four methods of glucose monitoring that are commonly used in clinical practice, including capillary glucose monitoring, glycated haemoglobin A1c, glycated albumin, and continuous glucose monitoring. We describe the definitions and technical characteristics of these methods, the factor that may interfere with the measurement, the advantages and caveats in clinical practice, the interpretation of glucose metrics, and the relevant supporting evidence. The recommendations for the use of these methods are also provided. The various methods of glucose monitoring have their strengths and limitations and cannot be replaced by one another. We hope that these guidelines could aid in the optimal application of common methods of glucose monitoring in clinical practice for better diabetes care.

Long-Term Monitoring of Fresco Paintings in the Cathedral of Valencia (Spain) Through Humidity and Temperature Sensors in Various Locations for Preventive Conservation

We describe the performance of a microclimate monitoring system that was implemented for the preventive conservation of the Renaissance frescoes in the apse vault of the Cathedral of Valencia, that were restored in 2006. This system comprises 29 relative humidity (RH) and temperature sensors: 10 of them inserted into the plaster layer supporting the fresco paintings, 10 sensors in the walls close to the frescoes and nine sensors measuring the indoor microclimate at different points of the vault. Principal component analysis was applied to RH data recorded in 2007. The analysis was repeated with data collected in 2008 and 2010. The resulting loading plots revealed that the similarities and dissimilarities among sensors were approximately maintained along the three years. A physical interpretation was provided for the first and second principal components. Interestingly, sensors recording the highest RH values correspond to zones where humidity problems are causing formation of efflorescence. Recorded data of RH and temperature are discussed according to Italian Standard UNI 10829 (1999).

COVID-19 Vaccine as a Potential Triggering Factor for Anti-Glomerular Basement Membrane (GBM) Disease: A Case Report and Literature Review

Coronavirus 2019 (COVID-19) is considered one of the most significant medical pandemics of this century, with high morbidity and mortality associated with the pandemic. The virus was recognized initially as a cause of pneumonia, but subsequent studies showed significant association with gastrointestinal, neurological, and autoimmune diseases. By 2020, several vaccines became available for use, significantly reducing the infection rate. A good safety profile supported most of the studies related to vaccines. However, this area is still under study, and some reports linked the COVID-19 vaccine to the development of thrombocytopenia, thrombosis, Guillain-Barre syndrome, autoimmune diseases, and myocarditis. These side effects need to be reported to VAERS (Vaccine Adverse Event Reporting System). The exact etiology of anti-glomerular basement (Anti-GBM) disease remains unknown, but the disease is thought to be triggered by environmental factors in genetically predisposed individuals. It is considered one of the serious diseases that could lead to permanent kidney impairment if not treated early and adequately. That's why a great effort is being made by health care practitioners to figure out and avoid the risk and triggering factors. Few previously published papers linked the COVID-19 vaccine and the development of anti-GBM disease, which raised concerns about digging more into this area. Herein, we are reporting a case of a patient who developed rapidly progressive glomerulonephritis (RPGN) due to anti-glomerular basement membrane (GBM) antibody disease two days after receiving the second dose of the COVID-19 vaccine.

Complete mitochondrial genome of Schizothorax nukiangensis Tsao (Cyprinidae: Schizothorax)

In this work, we reported the complete mitochondrial genome sequence of the Schizothorax nukiangensis Tsao for the first time. The complete mtDNA genome sequence of S. nukiangensis Tsao was 16 585 bp in length, which contains 22 transfer RNA genes, 2 rRNA genes, 13 protein-coding genes, an origin of light-strand replication (OL) and a control region (D-Loop). The overall base composition of the mitogenome was calculated to be 29.6% for A, 27.0% for C, 17.9% for G and 25.5% for T. The complete mitogenome of the S. nukiangensis Tsao can provide an important data set for further studies on population history, molecular systematics, phylogeography and stock assessment.

Energy saving potential of hybrid membrane and distillation process in butanol purification: Experiments, modelling and simulation

Pervaporation experiments of dilute aqueous butanol solutions were carried out with three PDMS [poly (dimethyl siloxane)] membranes. Based on the experimental results of the best performing membrane a regression for both butanol and water permeability was done in the software R and implemented in a user defined pervaporation unit operation in the software Aspen Custom Modeler. With the implementation of the pervaporation step in Aspen Plus the hybrid pervaporation and distillation purification chain can be simulated in consistent way. Calculation of pervaporation of a 0.5 wt% BuOH-water solution with the experimentally investigated PDMS membrane results in a permeate stream with a concentration of 9 wt% BuOH. Application of membrane distillation resulted in a permeate butanol concentration of only 3 wt% and a considerable higher specific energy demand compared to pervaporation. For product purities of 99 wt% of BuOH a hybrid pervaporation and distillation process saves around 50% of the energy demand compared to state of the art distillation. A sensitivity analysis of the pervaporation step reveals, that for the hybrid pervaporation and distillation process compared to state of the art distillation the energy demand decreases already exceeding 5 wt% BuOH in the permeate stream. (c) 2016 Published by Elsevier B.V.

Squamous cell carcinoma of the tongue: subtypes and morphological features affecting prognosis

Squamous cell carcinoma (SCC) is the most common histological type of mobile tongue carcinoma. The incidence of mobile tongue carcinoma is decreasing in some countries owing to decreasing exposure to risk factors, but it has been reported to be increasing in younger people. The majority of mobile tongue cancers are conventional SCCs. Pathological diagnosis of conventional SCC is relatively easy. However, mobile tongue SCCs involve several subtypes that have distinct pathological features and biological behaviors. Some subtypes are relatively rare, and the pathological subtype influences treatment decision-making. Therefore, the recognition of SCC subtypes is crucial for proper treatment. In this review, we summarize nine SCC subtypes, including conventional SCC and highlight their pathological characteristics. We also report some morphological factors, such as the pattern of invasion, budding, desmoplastic reaction, lymphovascular invasion, and perineural invasion, which could be predictive of prognosis. As some morphological factors are closely associated with prognosis, pathologists may need to evaluate additional factors in pathological reports of near features. In summary, we highlight the basic knowledge of mobile tongue SCC with an emphasis on pathological subtypes, morphological features, and their relationship. We provide information to further elucidate SCC in the oral region.

On Load Balancing via Switch Migration in Software-Defined Networking

Switch-controller assignment is an essential task in multi-controller software-defined networking. Static assignments are not practical because network dynamics are complex and difficult to predetermine. Since network load varies both in space and time, the mapping of switches to controllers should be adaptive to sudden changes in the network. To that end, switch migration plays an important role in maintaining dynamic switch-controller mapping. Migrating switches from overloaded to underloaded controllers brings flexibility and adaptability to the network but, at the same time, deciding which switches should be migrated to which controllers, while maintaining a balanced load in the network, is a challenging task. This work presents a heuristic approach with solution shaking to solve the switch migration problem. Shift and swap moves are incorporated within a search scheme. Every move is evaluated by how much benefit it will give to both the immigration and outmigration controllers. The experimental results show that the proposed approach is able to outweigh the state-of-art approaches, and improve the load balancing results up to approximate to 14% in some scenarios when compared to the most recent approach. In addition, the results show that the proposed work is more robust to controller failure than the state-of-art methods.

Spectral brain signatures of aesthetic natural perception in the α and β frequency bands

During our everyday lives, visual beauty is often conveyed by sustained and dynamic visual stimulation, such as when we walk through an enchanting forest or watch our pets playing. Here, I devised an MEG experiment that mimics such situations: participants viewed 8 s videos of everyday situations and rated their beauty. Using multivariate analysis, I linked aesthetic ratings to 1) sustained MEG broadband responses and 2) spectral MEG responses in the α and β frequency bands. These effects were not accounted for by a set of high- and low-level visual descriptors of the videos, suggesting that they are genuinely related to aesthetic perception. My findings provide the first characterization of spectral brain signatures linked to aesthetic experiences in the real world.NEW & NOTEWORTHY In the real world, aesthetic experiences arise from complex and dynamic inputs. This study shows that such aesthetic experiences are represented in a spectral neural code: cortical α and β activity track our judgments of the aesthetic appearance of natural videos, providing a new starting point for studying the neural correlates of beauty through rhythmic brain activity.

The effect of thyroid dysfunction on breast cancer risk: an updated meta-analysis

In a previous systematic review and meta-analysis of studies reporting associations between hyper-/hypothyroidism and breast cancer incidence published through 29 January 2019, we identified a higher risk with diagnosed hyperthyroidism compared to euthyroidism, but no association with diagnosed hypothyroidism. This 2-year updated meta-analysis aims to investigate the role of menopause in this association and the dose-response relationship with blood levels of thyroid-stimulating hormone (TSH) and thyroid hormones. After the exclusion of studies with only mortality follow-up, with thyroid dysfunction evaluated as a cancer biomarker or after prior breast cancer diagnosis, we reviewed 25 studies that were published up to 01 December 2021 and identified in MEDLINE, the COCHRANE library, Embase, or Web of Science; of these, 9 were included in the previous meta-analysis. Risk estimates from 22 of the 25 studies were included in the meta-analysis and pooled using random-effects models. Compared to euthyroidism, hyperthyroidism and hypothyroidism diagnoses were associated with higher (pooled risk ratio (RR): 1.12, 95% CI: 1.06-1.18, 3829 exposed cases) and lower risks (RR = 0.93, 95% CI: 0.86-1.00, 5632 exposed cases) of breast cancer, respectively. The increased risk after hyperthyroidism was greater among postmenopausal women (RR = 1.19, 95% CI 1.09-1.30) and the decreased risk after hypothyroidism was more pronounced among premenopausal women (RR = 0.69, 95% CI 0.53-0.89). Among women with no prior history of thyroid disease, every 1 mIU/L increase in TSH level was associated with a 0.8% (95% CI > 0-1.5%) lower risk of breast cancer. In conclusion, this meta-analysis supports an association between thyroid hormone levels and breast cancer risk, which could be modified by menopausal status.

Relationships among Beliefs, Attitudes, Time Resources, Subjective Norms, and Intentions to Use Wearable Augmented Reality in Art Galleries

As a result of interactive and immersive technologies such as augmented reality, almost every service business has changed their ways of engaging with consumers. However, there has been little research on acceptance and use of wearable augmented reality (AR) in interactive services in museums and art galleries. Therefore, the aim of this study was to investigate the causal relationships among customers' beliefs, evaluation, attitudes, perceived behavior control (time resources), subjective norms, and intentions to use wearable AR and visit a tourist attraction (an art gallery) using the theory of planned behavior. The results showed that time resources affected intention to visit an art gallery, while attitude toward wearable AR had an impact on intention to use wearable AR. Subjective norms were found to predict intentions, and the intention to use wearable AR was found to influence the intention to visit an art gallery.

Surgery of iatrogenic bile duct injuries

Iatrogenic bile duct injury still represents a serious complication mostly connected with minimally invasive cholecystectomy. This complication has an important impact both on short- and long-term morbidity and is associated with non-negligible mortality. The objective of our study was to provide a comprehensive summary of information based on the most recent guidelines with recommendations for how to prevent a bile duct injury, how to reach an early diagnosis and finally, how to proceed should they occur in order to minimize further damage. We also present ATOM, a new classification of bile duct injuries that provides clear information not only about the extent of anatomical damage, but also about the time and mechanism of its occurrence.

Social vulnerability and its association with food insecurity in the South African population: findings from a National Survey

Social vulnerability refers to the attributes of society that make people and places susceptible to natural disasters, adverse health outcomes, and social inequalities. Using a social vulnerability index (SVI), we investigated social vulnerability prevalence and its relationship with food insecurity in South Africa (SA). In this nationally representative cross-sectional survey, we calculated SVI scores from 3402 respondents (median age, 35 (26-46) years) using an SVI developed by the United States (US) Centers for Disease Control and prevention (CDC) adapted for a South African context. We measured food insecurity using a modified Community Childhood Hunger Identification Project. Findings classified 20.6% and 20.4% of adults as socially vulnerable and food insecure, respectively. The risk of food insecurity was almost threefold higher in the social vulnerability group (OR 2.76, 95% CI 2.76-2.77, p < 0.001) compared to their counterparts. The SVI could be a useful tool to guide government and policymakers in the facilitation of social relief initiatives for those most vulnerable.

CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation

Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher-student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods.

Dietary Supplement Intake is Associated with Healthier Lifestyle Behaviors in College Students Attending a Regional University in the Southeast: A Cross-Sectional Study

The relationship between intake of dietary supplements and biomarkers such as insulin and insulin-like growth factor has not been well explored. The primary aim of this cross-sectional study was to investigate the associations between supplement intake and biological and lifestyle factors. We hypothesized that dietary supplement intake was associated with healthier lifestyle behaviors. College students attending a Southeast university were recruited between January 2018 and April 2019. Blood samples were collected to measure insulin, insulin-like growth factor 1 (IGF-1) and alanine aminotransferase (ALT). Statistical tests employed were linear regression and analysis of variance. Ninety-eight participants completed the study and 91% reported taking at least one supplement, while 5.1% reported taking 9+ supplements once per week. There were no differences in levels of insulin, IGF-1 and ALT by levels of dietary supplement intake. Although there were no differences in HEI-2015 score among the groups, those who consumed five or more supplements met a higher percentage of the recommended intake for fruits, performed aerobic exercise for longer duration, and had lower body fat percentage compared to participants who consumed two or less supplements at least once per week. These findings are consistent with previous studies and suggest that dietary supplement intake is highly prevalent in college students, and it may be related to healthy lifestyle behaviors. Future studies should employ mixed methodology to examine reasons by which college students consume dietary supplements and to assess perceived and direct health benefits associated with consumption.

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance-on par with two human experts-was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.

NMN ameliorated radiation induced damage in NRF2-deficient cell and mice via regulating SIRT6 and SIRT7

Risk of cancer often increases with aging, and radiotherapy is an essential component of treatment. As for abdominal and pelvic cancer, radiotherapy always inevitably causes injury to intestines through direct DNA damage or overload of reactive oxygen species (ROS). Nuclear factor erythroid 2-related factor 2 (NRF2) has been identified as a key protective factor against ionizing-radiation induced damage through promoting DNA damage repair and antioxidant modulation. However, the level of NRF2 always decreases with aging. Here, we demonstrated that NRF2 deficiency aggravated cellular DNA damage and the intestinal pathological lesion. Overexpression of SIRT6 or SIRT7 could improve cell proliferation and protect against radiation injury in NRF2 knock-out (KO) cells by modulating oxidative-stress and DNA damage repair. Consistently, supplement of nicotinamide mononucleotide (NMN), the agonist of sirtuins, increased the level of SIRT6 and SIRT7 in NRF2 KO cells, concomitant with reduced cellular ROS level and ameliorated DNA damage. In vivo, long-term oral administration of NMN attenuated the radiation-induced injury of jejunum, increased the number of intestinal stem cells, and promoted the ability of intestinal proliferation in NRF2-/- mice. Together, our results indicated that SIRT6 and SIRT7 had involved in scavenging ROS and repairing DNA damage, and NMN could be a promising candidate for preventing radiation damage when NRF2 is lacking.

Contemporary tree growth shows altered climate memory

Trees are long-lived organisms, exhibiting temporally complex growth arising from strong climatic "memory." But conditions are becoming increasingly arid in the western USA. Using a century-long tree-ring network, we find altered climate memory across the entire range of a widespread western US conifer: growth is supported by precipitation falling further into the past (+15 months), while increasingly impacted by more recent temperature conditions (-8 months). Tree-ring datasets can be biased, so we confirm altered climate memory in a second, ecologically-sampled tree-ring network. Predicted drought responses show trees may have also become more sensitive to repeat drought. Finally, plots near sites with relatively longer precipitation memory and shorter temperature memory had significantly lower recent mortality rates (R2 = 0.61). We argue that increased drought frequency has altered climate memory, demonstrate how non-stationarity may arise from failure to account for memory, and suggest memory length may be predictive of future tree mortality.

Overexpression of SYF2 correlates with enhanced cell growth and poor prognosis in human hepatocellular carcinoma

SYF2, also known as p29/NTC31/CBPIN, encodes a nuclear protein that interacts with Cyclin D-type binding-protein 1. SYF2 has been reported to be involved in pre-mRNA splicing and cell cycle regulation. In the present study, we observed that SYF2 was obviously upregulated in HCC tumor tissues and cell lines, and its level was positively correlated with the tumor grade and Ki-67 expression, as well as poor prognosis of HCC. In vitro, using serum starvation-refeeding experiment, our results suggested that SYF2 was upregulated in proliferating HCC cells, and was positive correlated with the expression of PCNA and Cyclin D1. In addition, depletion of SYF2 decreased PCNA and Cyclin D1 levels. Accordingly, interference of SYF2 resulted in cells cycle arrest at G1/S phase in Huh7 HCC cells. Furthermore, we found that SYF2 might interact with Cyclin D1 and could confer doxorubicin resistance in HCC cells. These findings revealed that SYF2 might play a regulatory role in the proliferation of HCC cells. In summary, SYF2 may be a novel prognostic marker and serve as a potential therapeutic target in HCC.

Spatio-temporal drought assessment of the Subarnarekha River basin, India, using CHIRPS-derived hydrometeorological indices

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann-Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40 years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1 month (SPI1) and 3 months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins.

CD133-Src-TAZ signaling stimulates ductal fibrosis following DDC diet-induced liver injury

Chronic liver injury follows inflammation and liver fibrosis; however, the molecular mechanism underlying fibrosis has not been fully elucidated. In this study, the role of ductal WW domain-containing transcription regulator 1 (WWTR1)/transcriptional coactivator with PDZ-binding motif (TAZ) was investigated after liver injury. Ductal TAZ-knockout (DKO) mice showed decreased liver fibrosis following a Diethyl 1,4-dihydro-2,4,6-trimethyl-3,5-pyridinedicarboxylate (DDC) diet compared to wild-type (WT) mice, as evidenced by decreased expression levels of fibrosis inducers, including connective tissue growth factor (Ctgf)/cellular communication network factor 2 (CCN2), cysteine-rich angiogenic inducer 61 (Cyr61/CCN1), and transforming growth factor beta 1 (Tgfb1), in DKO mice. Similarly, TAZ-knockout (KO) cholangiocyte organoids showed decreased expression of fibrosis inducers. Additionally, the culture supernatant of TAZ-KO cholangiocyte organoids decreased the fibrogenic gene expression in liver stellate cells. Further studies revealed that prominin 1 (PROM1/CD133) stimulated TAZ for fibrosis. After the administration of DDC diet, fibrosis was decreased in CD133-KO (CD133-KO) mice compared to that in WT mice. Similarly, CD133-KO cholangiocyte organoids showed decreased Ctgf, Cyr61, and Tgfb1 expression levels compared to WT cholangiocyte organoids. Mechanistically, CD133 stabilized TAZ via Src activation. Inhibition of Src decreased TAZ levels. Similarly, CD133-knockdown HCT116 cells showed decreased TAZ levels, but reintroduction of active Src recovered the TAZ levels. Taken together, our results suggest that TAZ facilitates liver fibrosis after a DDC diet via the CD133-Src-TAZ axis.

I got the business blues: what organizations can learn from popular music?

As the link between sustainable development and art begins to shed a new light on organizational conceptions, this article considers popular music, and especially blues music. The conceptual process of the paper is to highlight the philosophical roots of Western aesthetics in order to propose a counterpoint based on popular and ordinary living sustained by popular music. By analyzing blues music as both a durable support and a natural process of everyday experience, we open a door to Chinese philosophy and psychology for HR Management based on the body-mind-spirit alignment. This perspective generates new and creative avenues for research in HR management, organizational theory, and business education. The major issue is then how to sensitize the ears of HRM so that it offers 'vital nourishment' in the workplace at a time when the cost of the depressive state of many an employee has become a proven risk. (C) 2016 Elsevier Ltd. All rights reserved.

Deep domain adaptation with ordinal regression for pain assessment using weakly-labeled videos

Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications. Given the challenges related to subjective variations of facial expressions, and to operational capture conditions, the accuracy of state-of-the-art deep learning (DL) models for recognizing facial expressions may decline. Domain adaptation (DA) has been widely explored to alleviate the problem of domain shifts that typically occur between video data captured across various source (laboratory) and target (operational) domains. Moreover, given the laborious task of collecting and annotating videos, and the subjective bias due to ambiguity among adjacent intensity levels, weakly-supervised learning (WSL) is gaining attention in such applications. State-ofthe-art WSL models are typically formulated as regression problems, and do not leverage the ordinal relationship among pain intensity levels, nor the temporal coherence of multiple consecutive frames. This paper introduces a new DL model for weakly-supervised DA with ordinal regression (WSDA-OR) that can be adapted using target domain videos with coarse labels provided on a periodic basis. The WSDA-OR model enforces ordinal relationships among the intensity levels assigned to target sequences, and associates multiple relevant frames to sequence-level labels (instead of a single frame). In particular, it learns discriminant and domain-invariant feature representations by integrating multiple instance learning with deep adversarial DA, where soft Gaussian labels are used to efficiently represent the weak ordinal sequence-level labels from the target domain. The proposed approach was validated using the RECOLA video dataset as fully-labeled source domain data, and UNBC-McMaster shoulder pain video dataset as weakly-labeled target domain data. We have also validated WSDA-OR on BIOVID and Fatigue (private) datasets for sequence level estimation. Experimental results indicate that our proposed approach can significantly improve performance over the state-of-the-art models, allowing to achieve a greater pain localization accuracy. Code is available on GitHub link: https://github.com/praveena2j/ WSDAOR. (c) 2021 Elsevier B.V. All rights reserved.

Broadband and high-gain printed antennas constructed from Fabry-Perot resonator structure using EBG or FSS cover

Fabry-Perot resonators with EBG or FSS cover art introduced to enhance the gain of printed antenna. Based oil a systematic. c comparison study, two kinds of optimization designs are proposed for enhancing the gain and reducing the backward radiation. The validation of the simulated and experimental results confirms that the expected performance is met. (c) 2006 Wiley Periodicals, Inc.

All but blank: Artistic approaches to human Antarctica

The visual arts have played an increasingly important role in examining and critiquing past and present human activities in Antarctica as governed by the Antarctic Treaty and its Protocol on Environmental protection. This paper analyses the work of six artists who have contributed to this scrutiny, awakening us to fabrications and helping to enrich Antarctic cultures beyond the scientific and the environmental. It encourages all signatory nations to the Antarctic Treaty System to embrace and empower a more diverse artistic engagement with Antarctica and suggests that artists find new ways to address threats to the Antarctic, whether they come from within and from without.

Multi-Modal Recurrent Attention Networks for Facial Expression Recognition

Recent deep neural networks based methods have achieved state-of-the-art performance on various facial expression recognition tasks. Despite such progress, previous researches for facial expression recognition have mainly focused on analyzing color recording videos only. However, the complex emotions expressed by people with different skin colors under different lighting conditions through dynamic facial expressions can be fully understandable by integrating information from multi-modal videos. We present a novel method to estimate dimensional emotion states, where color, depth, and thermal recording videos are used as a multi-modal input. Our networks, called multi-modal recurrent attention networks (MRAN), learn spatiotemporal attention volumes to robustly recognize the facial expression based on attention-boosted feature volumes. We leverage the depth and thermal sequences as guidance priors for color sequence to selectively focus on emotional discriminative regions. We also introduce a novel benchmark for multi-modal facial expression recognition, termed as multi-modal arousal-valence facial expression recognition (MAVFER), which consists of color, depth, and thermal recording videos with corresponding continuous arousal-valence scores. The experimental results show that our method can achieve the state-of-the-art results in dimensional facial expression recognition on color recording datasets including RECOLA, SEWA and AFEW, and a multi-modal recording dataset including MAVFER.

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

Stroke is a cardiovascular disease with high mortality and long-term disability in the world. Normal functioning of the brain is dependent on the adequate supply of oxygen and nutrients to the brain complex network through the blood vessels. Stroke, occasionally a hemorrhagic stroke, ischemia or other blood vessel dysfunctions can affect patients during a cerebrovascular incident. Structurally, the left and the right carotid arteries, and the right and the left vertebral arteries are responsible for supplying blood to the brain, scalp and the face. However, a number of impairment in the function of the frontal lobes may occur as a result of any decrease in the flow of the blood through one of the internal carotid arteries. Such impairment commonly results in numbness, weakness or paralysis. Recently, the concepts of brain's wiring representation, the connectome, was introduced. However, construction and visualization of such brain network requires tremendous computation. Consequently, previously proposed approaches have been identified with common problems of high memory consumption and slow execution. Furthermore, interactivity in the previously proposed frameworks for brain network is also an outstanding issue. This study proposes an accelerated approach for brain connectomic visualization based on graph theory paradigm using compute unified device architecture, extending the previously proposed SurLens Visualization and computer aided hepatocellular carcinoma frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, USA. Significantly, our proposed framework is able to generate and extract points and edges of datasets, displays nodes and edges in the datasets in form of a network and clearly maps data volume to the corresponding brain surface. Moreover, with the framework, surfaces of the dataset were simultaneously displayed with the nodes and the edges. The framework is very efficient in providing greater interactivity as a way of representing the nodes and the edges intuitively, all achieved at a considerably interactive speed for instantaneous mapping of the datasets' features. Uniquely, the connectomic algorithm performed remarkably fast with normal hardware requirement specifications.

Cross-Modality Contrastive Learning for Hyperspectral Image Classification

Deep learning has attracted much attention in the field of hyperspectral image classification recently, due to its powerful representation and generalization abilities. Most of the current deep learning models are trained in a supervised manner, which requires large amounts of labeled samples to achieve the state-of-the-art performance. Unfortunately, pixel-level labeling in hyperspectral imageries is difficult, time-consuming, and human-dependent. To address this issue, we propose an unsupervised feature learning model using multimodal data, hyperspectral, and light detection and ranging (LiDAR) in particular. It takes advantage of the relationship between hyperspectral and LiDAR data to extract features, without using any label information. After that, we design a dual fine-tuning strategy to transfer the extracted features for hyperspectral image classification with small numbers of training samples. Such a strategy is able to explore not only the semantic information but also the intrinsic structure information of training samples. In order to test the performance of our proposed model, we conduct comprehensive experiments on three hyperspectral and LiDAR datasets. Experimental results show that our proposed model can achieve better performance than several state-of-the-art deep learning models.

Nonlinear Regression with Logistic Product Basis Networks

We introduce a novel general regression model that is based on a linear combination of a new set of non-local basis functions that forms an effective feature space. We propose a training algorithm that learns all the model parameters simultaneously and offer an initialization scheme for parameters of the basis functions. We show through several experiments that the proposed method offers better coverage for high-dimensional space compared to local Gaussian basis functions and provides competitive performance in comparison to other state-of-the-art regression methods.

CRITERIA FOR COMPREHENSIVE EVALUATION OF THE QUALITY OF A CITY'S ARTIFICIAL LIGHT MEDIUM

One of the professional creative tasks of a light designer is the creation of light medium of a city meeting certain artistic and functional features. The article determines criteria for a comprehensive evaluation of the visual quality of an artificial light medium of a city and proposes a new criterion: "design properties of light medium of a city".

WHY IT IS NECESSARY TO REVISE THE STANDARDS OF EXHIBITION LIGHTING

In the modern world, one of the main functions of museums is to organize the preservation of pieces of art and arrange their presentation to museum visitors. Since the modern exhibition is based on the artificial lighting, it is necessary to properly arrange this lighting; otherwise, it can negatively affect the safety of museum pieces. The article sets out the views on the criteria of professional lighting of works of art, as it is always a compromise between the custodians and the lighting engineers. The authors also attempt to disclose the processes of organizing museum lighting and give a generalized description of the standards and rules, which serve as a basis to realize this lighting. The main reasons for the need to rethink these standards and rules (and even to revise them), in connection with the emergence of new LED sources, have been outlined.

Asymmetric cost aggregation network for efficient stereo matching

Cost aggregation is crucial to the accuracy of stereo matching. A reasonable cost aggregation algorithm should aggregate costs within homogeneous regions where pixels have the same or similar disparities. Otherwise, the estimated disparity map is prone to the well-known edge-fattening issue and the problem of losing fine structures. However, current state-of-the-art convolutional neural networks (CNNs) mainly do cost aggregation with square-kernel convolutional layers that learn to adjust their kernel elements to make the actual receptive fields of the aggregated costs adapt to homogeneous regions with various shapes. This is a mechanism that easily leads to the above issues due to the translation equivalence and content-insensitivity properties of CNNs. To tackle these problems, a novel densely connected asymmetric convolution block (Dense-ACB) based on asymmetric convolutions is proposed to explicitly construct receptive fields with various shapes, which effectively alleviates the issues caused by mismatching shapes of receptive fields and homogeneous regions. The proposed Dense-ACB brings new insight to CNN-based stereo matching networks. Based on the proposed cost aggregation method, an efficient and effective stereo matching network is built, which not only achieves competitive overall accuracy compared with state-of-the-art models but also effectively alleviates the edge-fattening problem and preserves fine structures.

Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer

Various image pre-processing tasks in optical coherence tomography (OCT) systems involve reversing degradation effects (e.g. deblurring). Current deblurring research mainly focuses on how to build suitable degradation models using deconvolution operators. However, model-based solutions may not work well in many scenarios. To solve this problem, the authors propose a non-model architecture, called a deep convolutional neural network, to address parameter-free situations. The proposed solution employs a deep learning strategy to bridge the gap between traditional model-based methods and neural network architectures. Experiments on retinal OCT images demonstrate that the proposed approach achieves superior performance compared with the state-of-the-art model-based OCT deblurring methods.

Sequential Gesture Learning for Continuous Labanotation Generation Based on the Fusion of Graph Neural Networks

Labanotation is a symbolic recording system for human movements, and also a powerful tool for protecting and spreading folk dances and other performing arts. State-of-the-art automatic Labanotation uses end-to-end methods with sequence-based skeleton representation, which cannot capture the relationship between joints and bones in the skeleton for accurate descriptions of continuous lower limb movements such as dance steps. In this paper, we propose a novel double-stream fusion method of directed graph neural networks (DGNN), combined with connectionist temporal classification (CTC), namely DFGNN-CTC, for sequential fine-grained motion recognition, such as the Labanotation generation of unsegmented dance movement. First, we extract double-stream directed graph feature, employing an orientation-normalized directed acyclic graph (ON-DAG) and an orientation-normalized temporal directed acyclic graph (ON-TDAG), to jointly model spatiotemporal properties of movement recorded in motion capture data. Then, we design a CTC-based fusion-pooling module to fuse the spatial and temporal streams encoded by two DGNNs. It concatenates and fuses the two streams to generate discriminative descriptions of each time step, and concentrates them to make per-time-step predictions of Laban gesture type, from which the CTC searches the optimal Laban symbol sequence, corresponding to elemental motions composing the movement. In this way, the new method enables much finer discrimination for similar Laban gestures with subtle differences in spatial and temporal properties through joint contextual spatiotemporal modeling so that it achieves much superior performance in continuous Labanotation generation to existing methods, which only have single-stream analysis either spatially or temporally. The experiments on two Labanotation-labelled motion capture datasets demonstrate the effectiveness of the components in the proposed method and its superiority comparing with the state-of-the-art methods, especially for lower limb movements.

Diagnostic and interventional EUS in hepatology: An updated review

EUS has become an increasingly used diagnostic and therapeutic modality in the armamentarium of endoscopists. With ever-expanding indications, EUS is being used in patients with liver disease, for both diagnosis and therapy. EUS is playing an important role in providing additional important information to that provided by cross-sectional imaging modalities such as computerized tomography and magnetic resonance imaging. Domains of therapy that were largely restricted to interventional radiologists have become accessible to endosonologists. From liver biopsy and sampling of liver lesions to ablative therapy for liver lesions and vascular interventions for varices, there is increased use of EUS in patients with liver disease. In this review, we discuss the various diagnostic and therapeutic applications of EUS in patients with various liver diseases.

Efficient Registration of High-Resolution Feature Enhanced Point Clouds

We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.

Attention in Attention Networks for Person Retrieval

This paper generalizes the Attention in Attention (AiA) mechanism, in P. Fang et al., 2019 by employing explicit mapping in reproducing kernel Hilbert spaces to generate attention values of the input feature map. The AiA mechanism models the capacity of building inter-dependencies among the local and global features by the interaction of inner and outer attention modules. Besides a vanilla AiA module, termed linear attention with AiA, two non-linear counterparts, namely, second-order polynomial attention and Gaussian attention, are also proposed to utilize the non-linear properties of the input features explicitly, via the second-order polynomial kernel and Gaussian kernel approximation. The deep convolutional neural network, equipped with the proposed AiA blocks, is referred to as Attention in Attention Network (AiA-Net). The AiA-Net learns to extract a discriminative pedestrian representation, which combines complementary person appearance and corresponding part features. Extensive ablation studies verify the effectiveness of the AiA mechanism and the use of non-linear features hidden in the feature map for attention design. Furthermore, our approach outperforms current state-of-the-art by a considerable margin across a number of benchmarks. In addition, state-of-the-art performance is also achieved in the video person retrieval task with the assistance of the proposed AiA blocks.

PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection

Plant diseases and pests cause significant losses in agriculture, with economic, ecological and social implications. Therefore, early detection of plant diseases and pests via automated methods are very important. Recent machine learning-based studies have become popular in the solution of agricultural problems such as plant diseases. In this work, we present two classification models based on deep feature extraction from pre-trained convolutional neural networks. In the proposed models, we fine-tune and combine six state-of-the-art convolutional neural networks and evaluate them on the given problem both individually and as an ensemble. Finally, the performances of different combinations based on the proposed models are calculated using a support vector machine (SVM) classifier. In order to verify the validity of the proposed model, we collected Turkey-PlantDataset, consisting of unconstrained photographs of 15 kinds of disease and pest images observed in Turkey. According to the obtained performance results, the accuracy scores are calculated as 97.56% using the majority voting ensemble model and 96.83% using the early fusion ensemble model. The results demonstrate that the proposed models reach or exceed state-of-the-art results for this problem.

The Contribution of Cultural Heritage Owned by Local Health Authorities in the Humanization of Care: The Point of View of Top Management

After the COVID-19 pandemic, reforms in healthcare systems have the purpose to fully recover the relationship of healthcare organizations with their patients. For centuries, art was used throughout Europe in the healthcare context for its power to engage and support patients in their illnesses. This approach can be rediscovered by utilizing the cultural heritage owned by Local Health Authorities. In this context, tradition, art, innovation, and care coexist. This study aims to investigate the interest in developing projects for the humanization of care by the top management of Italian Local Health Authorities, in particular exploiting their cultural heritage. The evaluation of the proposal was conducted using semi-structured interviews with the top management of two Local Health Authorities, in which the Santa Maria Nuova hospital in Florence and the Santo Spirito in Sassia Hospital in Rome are located, as the two selected cases for this study. The interviewees welcomed the proposal to develop humanization of care projects involving the use of their cultural heritage. Moreover, they expressed their desire to invest human, economic, and structural resources in the development of these initiatives. The implementation of humanization of care projects using cultural heritage owned by Local Health Authorities is useful to apply specific policies to enhance the governance of the cultural heritage according to the health mission. On the other hand, it permits the search for additional or ad hoc resources. Finally, it is possible to humanize and improve patients' experience while increasing awareness among the health workforce and trainees.

Toward visual microprocessors

This paper outlines motivations and models underlying the design of visual microprocessors based on the cellular neural network universal machine. We also overview the state of the art regarding the realization of these microprocessors in the form of very large-scale integration chips. Examples corresponding to measurements realized on these chips are enclosed for illustration purposes.

Atrioventricular septal defect with an absent or tiny ostium primum defect: a case series of three surgical cases

Although atrioventricular septal defects are categorized according to the anatomical atrioventricular orifice, the location of the intracardiac shunt in atrioventricular septal defects is important from a surgical perspective. Herein, we report three cases of atrioventricular septal defects with a small or no ostium primum defect. Case 1 (3-month-old girl) was diagnosed preoperatively with a ventricular septal defect, secundum atrial septal defect, and mitral valve cleft. After the operation, the diagnosis was corrected to an atrioventricular septal defect and was repaired completely. Case 2 (9-year-old girl) underwent pulmonary artery banding for a ventricular septal defect with a straddling mitral valve. After the experience with Case 1, we realized similarities between Cases 1 and 2. Therefore, we corrected the diagnosis to atrioventricular septal defect and achieved definitive repair. Based on these experiences, we accurately diagnosed Case 3 (3-month-old boy) with an atrioventricular septal defect. This variant is poorly known; however, proper morphological understanding is necessary to facilitate anatomical repair and prevent postoperative atrioventricular blocks. Some cases of this variant may be diagnosed as a ventricular septal defect with straddling mitral valve and are unable to receive definitive repair. The direction of the cleft, absence of atrioventricular valve offsetting, and trileaflet of the left atrioventricular valve all seem useful for making a diagnosis, and these can be easily confirmed by echocardiography.

A weighted feature extraction method based on temporal accumulation of optical flow for micro-expression recognition

Spatiotemporal features are widely used in micro-expression (ME) recognition to represent facial appearance and action. The features extracted from different face regions are usually given different weights according to the motion intensities in the corresponding regions. The weighted features are reported to be more discriminative than the unweighted ones for ME recognition. However, MEs are so subtle that their motion intensities are usually as low as noises, therefore small image noises can cause similar weights with MEs and degenerate the effectiveness of these weights. To address this issue, a novel weighted feature extraction method is proposed in this paper, whereby the neighboring optical flows in a time interval are accumulated to compute motion intensities. In this manner, the displacements caused by image noises in optical flow are decreased because these displacements are random and direction-inconsistent. Meanwhile, the displacements caused by facial expressions are enhanced because the displacements caused by facial expressions are usually direction-consistent among neighboring frames. The weights computed from the accumulated optical flows are multiplied with the spatiotemporal features, then the weighted features are fed to SVM to classify MEs. The experimental results demonstrate that our method achieves comparable recognition performances with the state-of-the-art methods on SMIC-HS and outperforms the state-of-the-art methods on CASME II.

[The effects of the deployment of artificial intelligence on the healthcare professions]

Understanding the effects of the spread of artificial intelligence and robotization on the healthcare professions must be free of prejudice. In this way, it will be possible to promote a real methodology for evaluating and supporting these transformations.

Environmental performance measurement in arts and cultural organisations: Exploring factors influencing organisational changes

Arts and Cultural Organisations (ACOs) have received significant attention over the last few years regarding their environmental performance. ACOs are often non-profit organisations, relying on government funding to implement various programmes to support societal development. Funding dependence can shift ACOs' focus from creating socio-cultural value to being more commercially driven. This paper explores factors influencing organisational changes in ACOs related to environmental performance measurement. Stakeholders in ACOs based in Nottingham, England, were interviewed and participated in a workshop to validate and collect addi-tional data. Our research uncovered five interrelated factors that influence organisational change: the role of funding bodies; local policies and networks; organisational culture and leadership; lack of resources; and building proprietary-tenant relationships. This paper contributes to understanding ACOs responses to measuring environmental performance and the challenges they face as they move from measuring to implementation. Implications are explored for how funding is allocated and understood in terms of moving beyond merely measuring the carbon footprint of activities. ACOs' funding dependence indicates a focus on carbon measure-ment, omitting a more holistic approach towards the environment and sustainability.

Tensor Factorization and Attention-Based CNN-LSTM Deep-Learning Architecture for Improved Classification of Missing Physiological Sensors Data

One of the essential issues for efficient control of prosthesis is the accurate classification of target movements hidden in electroencephalography (EEG) and electromyography (EMG) signals. However, in the presence of missing data in acquired signals, the classification accuracy degrades significantly as the amount of missing data increases, reducing the control performance of the prosthesis. This research proposes a framework based on tensor (multidimensional array) factorization and attention-based convolutional neural network (CNN)-long short-term memory (LSTM) deep learning (DL) for recovering missing data and performing classification of target movements, respectively. To recover missing data in tensor factorization, Canonical/Polyadic Weighted OPTimization (CP-WOPT) is employed, and its performance is compared to state-of-the-art factorization methods, whereas the performance of CNN-LSTM-attention layer (Attn) is compared to state-of-the-art machine learning and DL classifiers. Results show that CNN-LSTM-Attn obtained the mean classification accuracy of 98%, 83%, and 90% on complete (0% missing data), partially complete (10% to 50% missing data), and tensor-recovered real-world EEG and EMG data, respectively, demonstrating the applicability of the proposed framework.

Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage requirement. Online decision tree learning algorithms have been devised to tackle this problem by concurrently training with incoming samples and providing inference results. However, even the most up-to-date online tree learning algorithms still suffer from either high memory usage or high computational intensity with dependency and long latency, making them challenging to implement in hardware. To overcome these difficulties, we introduce a new quantile-based algorithm to improve the induction of the Hoeffding tree, one of the state-of-the-art online learning models. The proposed algorithm is lightweight in terms of both memory and computational demand, while still maintaining high generalization ability. A series of optimization techniques dedicated to the proposed algorithm have been investigated from the hardware perspective, including coarse-grained and fine-grained parallelism, dynamic and memory-based resource sharing, pipelining with data forwarding. Following this, we present Hard-ODT, a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques. Performance and resource utilization are modeled for the complete learning system for early and fast analysis of the tradeoff between various design metrics. Finally, we propose a design flow in which the proposed learning system is applied to FPGA run-time power monitoring as a case study. Experimental results show that our proposed algorithm outperforms the state-of-the-art Hoeffding tree learning method, leading to 0.05% to 12.3% improvement in inference accuracy. Real implementation of the complete learning system on the FPGA demonstrates a 384x to 1581x speedup in execution time over the state-of-the-art design. The power modeling strategy with Hard-ODT achieves an average power prediction error within 4.93% of a commercial gate-level power estimation tool.

Symmetry-Based Scalable Lossless Compression of 3D Medical Image Data

We propose a novel symmetry-based technique for scalable lossless compression of 3D medical image data. The proposed method employs the 2D integer wavelet transform to decorrelate the data and an intraband prediction method to reduce the energy of the sub-bands by exploiting the anatomical symmetries typically present in structural medical images. A modified version of the embedded block coder with optimized truncation (EBCOT), tailored according to the characteristics of the data, encodes the residual data generated after prediction to provide resolution and quality scalability. Performance evaluations on a wide range of real 3D medical images show an average improvement of 15% in lossless compression ratios when compared to other state-of-the art lossless compression methods that also provide resolution and quality scalability including 3D-JPEG2000, JPEG2000, and H.264/AVC intra-coding.

Repression of SLC22A3 by the AR-V7/YAP1/TAZ axis in enzalutamide-resistant castration-resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive and fatal disease, with most patients succumbing within 1-2 years despite undergoing multiple treatments. Androgen-receptor (AR) inhibitors, including enzalutamide (ENZ), are used for the treatment of mCRPC; however, most patients develop resistance to ENZ. Herein, we propose that the repression of SLC22A3 by AR-V7/YAP1/TAZ conferred ENZ resistance in mCRPC. SLC22A3 expression is specifically downregulated in the ENZ-resistant C4-2B MDVR cells, and when YAP1/TAZ is hyperactivated by AR full-length or AR-V7, these proteins interact with DNMT1 to repress SLC22A3 expression. We observed low SLC22A3 expression and high levels of TAZ or YAP1 in mCRPC patient tissues harbouring AR-V7 and the opposite expression patterns in normal patient tissues. Our findings suggest a mechanism underlying ENZ resistance by providing evidence that the AR-V7/YAP1/TAZ axis represses SLC22A3, which could be a potential treatment target in prostate cancer.

Back to the past: Symbolism and archaeology in Altxerri B (Gipuzkoa, Northern Spain)

In a previous publication on Altxerri B Cave, we explained a chronological hypothesis which proposed that the graphic activity in the site dates to an early Aurignacian phase. This paper presents a complete study of the parietal ensemble, including descriptions of the graphic motifs and other anthropic evidence that has been documented. The number of figures identified in the only panel documented in previous studies has been increased considerably, while several previously unpublished panels in other parts of the cave are described. The iconographic and stylistic characteristics of the rock art, far from contradicting our first conclusions about the chronology, support these and link the art in Altxerri B with other European Early Upper Palaeolithic graphic ensembles. (C) 2015 Elsevier Ltd and INQUA. All rights reserved.

How should we address the inevitable harms from non-negligent variant reclassification in predictive genetic testing?

The process of interpreting genetic variants, in which experts use all available evidence to determine whether an identified variant is associated with a current or future disease, is both scientific and nevertheless subjective. In this paper, we summarize the existing evidence that any given variant could be reclassified and that such a reclassification could lead to harm. Furthermore, the racial gap in genetic databases could lead to a higher likelihood of harm for non-white patients. We also review recent legal analyses indicating it is unlikely that an individual who sues for restitution would be successful, especially in the absence of evidence of lab negligence. We then propose a compensation program for medical genetic tests to ensure that individuals who experience demonstrable harm due to a variant reclassification can be made whole financially. We conclude by discussing outstanding questions that must be answered for such a program to be feasible.

Implications of the changing epidemiology of inflammatory bowel disease in a changing world

The epidemiology of inflammatory bowel disease (IBD) has undergone considerable shifts since its emergence in the Western world over a century ago, especially in the last few decades, with increasing global burden of disease. IBD incidence continues to rise in developed countries in all age groups which is contributing to compounding prevalence. Further, IBD incidence is rising sharply in Asia and other recently developed and developing countries. In this review, we discuss the implications of changing trends of IBD epidemiology. First, changing patterns provide insights into IBD causes, as they occur concurrent with shifts in the environment, cultures, and attitudes. Understanding the impact of the environment on IBD risk can help towards prediction and prevention strategies. Second, we must prepare healthcare systems for the rising burden of IBD and address it at various levels towards improving outcomes and health, overall.

Antibacterial activity of essential oils extracted from the unique Chinese spices cassia bark, bay fruits and cloves

Spices are widely used in daily life such as diet and have certain activity. Especially in China, spices have been mainly used as condiments for thousands of years in order to improve the sensory quality of food; in addition, they and their derivatives can also be used as preservatives. In this study, three spices with unique Chinese characteristics widely used were selected: cassia bark (bark of Cinnamomum camphora Presl), bay fruits (Laurus nobilis), and cloves (Syzygiumaromaticum). The main components and antibacterial ability of these three spices were analyzed by simulated extraction method. Through headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) analysis, it was determined that the main active compounds in the essential oils of cassia bark, bay fruits and cloves were cinnamaldehyde (78.11%), cinnamaldehyde (61.78%) and eugenol (75.23%), respectively. The agar plate diffusion test and the simulated food culture medium experiment confirmed that the essential oils extracted from the three flavors have antibacterial effects on Listeria monocytogenes, Listeria innocua, Listeria welshimeri, Listeria ivanovii, Listeria grayi and Vibrio parahaemolyticus. The antibacterial activity of different strains has different optimal extraction conditions. Generally speaking, cinnamon essential oil has the strongest antibacterial activity, while laurel fruit has the lowest antibacterial activity. The study proved the antibacterial activity of these three Chinese-specific spices and provided some new ideas and methods for the subsequent research and preparation of natural food additives and food antibacterial agents.

TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution

Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to $0.19 \sim 2.3dB $ , while it is memory efficient.

Association between history of miscarriage and autism spectrum disorder

This case-control study was designed to examine the association between different types of miscarriage history and autism spectrum disorder (ASD), and determine whether the number of miscarriage history affects the risk of ASD. All of 2274 children with ASD and 1086 healthy controls were recruited. Sociodemographic and prenatal, perinatal, and neonatal characteristics were compared between the two groups. Multivariable logistic regression analyses were applied to investigate association between miscarriage history and ASD. Stratified analyses based on sex and types of miscarriages were similarly performed. History of miscarriage was potential risk factors for ASD ([aOR] = 2.919; 95% [CI] = 2.327-3.517). Stratified analyses revealed that induced ([aOR] = 2.763, 95% [CI] = 2.259-3.379) and spontaneous miscarriage history ([aOR] = 3.341, 95% [CI] = 1.939-4.820) were associated with high risk of ASD, respectively. A sex-biased ratio in the risk of ASD was observed between females ([aOR] = 3.049, 95% [CI] = 2.153-4.137) and males ([aOR] = 2.538, 95% [CI] = 1.978-3.251). Stratified analysis of induced miscarriage history revealed that only iatrogenic miscarriage history was associated with an increased risk ASD ([aOR] = 2.843, 95% [CI] = 1.534-4.268). Also, multiple spontaneous miscarriage histories ([aOR] = 1.836, 95% [CI] = 1.252-2.693) were associated with higher autism risk than one spontaneous miscarriages history ([aOR] = 3.016, 95% [CI] = 1.894-4.174). In conclusion, miscarriage history is related to an increased risk for ASD in offspring, which is affected by the types of miscarriage and sex of the fetus.

Surgery of the skull base as a new section in Acta Otorhinolaryngologica Italica

La chirurgia della base del cranio in una nuova sezione di Acta Otorhinolaryngologica Italica.

Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training

Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly for natural images. ZSL for medical images has remained largely unexplored. We design a novel strategy for generalized zero-shot diagnosis of chest radiographs. In doing so, we leverage the potential of multi-view semantic embedding, a useful yet less-explored direction for ZSL. Our design also incorporates a self-training phase to tackle the problem of noisy labels alongside improving the performance for classes not seen during training. Through rigorous experiments, we show that our model trained on one dataset can produce consistent performance across test datasets from different sources including those with very different quality. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for generalized zero-shot chest x-ray diagnosis.

Construction of a p-n heterojunction based on magnetic Mn0.6Zn0.4Fe2O4 and ZnIn2S4 to improve photocatalytic performance

To improve the photocatalytic performance of Mn0.6Zn0.4Fe2O4 (MZFO) and ZnIn2S4 (ZIS) for organic pollutants, the p-n MZFO@ZIS heterojunctions with different weight percentage (10 ~ 40%) of MZFO are constructed from spent batteries and added indium ion via a green bioleaching and hydrothermal method. Structural, optical, and photocatalytic properties for the heterojunctions are investigated systematically by XRD, FT-IR, SEM-EDX, TEM, BET, VB-XPS, UV-vis DRS, PL, etc. The results confirm that p-n junction significantly improves the visible light adsorption and the separation efficiency of photogenerated carriers. Specifically, MZFO-25%@ZIS shows the highest photodegradation performance toward Congo red (CR), and its reactive kinetic constant is about 9.6, 7.8, and 7.0 times higher than that of P25 TiO2, MZFO, and ZIS, respectively, and MZFO-25%@ZIS still possesses a high reusability and simple magnetic separation after 5 cycles of reuse. The radical trapping experiments and electronic paramagnetic resonance (EPR) tests show that ·O2-, ·OH, and h+ are the most important active substance for degrading CR. The pathways for the CR degradation are further proposed based on the intermediate analysis. DFT + U calculations confirm that the high charge density of Zn-O, Fe-O, and Zn-S bonds in the MZFO and ZIS molecules provides the electrons for the sufficient production of free radicals. This work also provides a novel high value-added strategy for the green utilization of spent batteries.

Digital Twin for Urban Planning in the Green Deal Era: A State of the Art and Future Perspectives

This paper provides a state of the art of contemporary Digital Twins (DTs) projects for urban planning at an international level. The contribution investigates the evolution of the DT concept and contextualises this tool within the scientific-cultural debate, highlighting the interconnection between global policies and local needs/wishes. Specifically, six case studies of DTs are compared, illustrating their application, content, technological infrastructure, and priority results. The projects presented provide an overview of the existing DT typologies, focusing on the evaluative/prefigurative use and the limits/potential of the tool in light of the socio-health, climate, and environmental crises. Reflections on DT reveal, on the one hand, its potential role in supporting decision-making and participatory processes and, on the other, the potential utopian trend of data-driven planning encouraged by public-private investments in the smart city/twin city sector. In conclusion, the study underlines the innovative role of DT as a cutting-edge scientific format in the disciplinary framework but highlights that the practical use of the tool is still in an experimental research-action phase. From this theoretical-critical review, it is possible to hypothesise new research paths to implement the realism and application potential of DTs for urban planning and urban governance.

Pictorial review: radiological diagnosis of anastomotic leakage with water-soluble contrast enema after anterior resection of the rectum

For patients who have undergone colorectal surgery, anastomotic leakage is a serious and challenging complication with a variable rate ranging between 1.8% and 19.2%. Postoperative anastomotic leaks after colorectal surgery can have severe consequences for patients, particularly ones who present with few or no symptoms. Computed tomography and/or water-soluble contrast enema (WSE) are the most frequently utilized imaging methods to identify and diagnose anastomotic leaks early. WSE is a safe and complication-free procedure that allows to identify the presence of otherwise unrecognized anastomotic leaks, both in asymptomatic and symptomatic patients. Fluoroscopic rectal examination using a water-soluble contrast agent for postoperative patients is never an easy examination to perform since it requires careful preparation, skill, and knowledge. Four morphological types of anastomotic dispersion have been described: "saccular type", "horny type", "serpentine type" and "dendritic type". Among 4 types of leakage, dendritic and serpentine types are more frequently followed by clinical symptoms and none of the dendritic type resolves spontaneously. On the other hand, the saccular and horny types have a better prognosis after healing of the loss and subsequent restoration of the ostomy as they consist of a cavity that provides a sort of physical barrier to the spread of inflammation. The aim of this pictorial essay was to illustrate the spectrum of imaging findings of morphological types of radiologic leakages on WCE in patients with colorectal surgical anastomosis. We have also tried to provide tips and tools to enable identification of radiological leakages on retrograde WCE, particularly of the smallest leaks which can be more easily missed.

PdS Quantum Dots as a Hole Attractor Encapsulated into the MOF@Cd0.5Zn0.5S Heterostructure for Boosting Photocatalytic Hydrogen Evolution under Visible Light

Herein, a new photocatalyst PdS@UiOS@CZS is successfully synthesized, where thiol-functionalized UiO-66 (UiOS), a metal-organic framework (MOF) material, is used as a host to encapsulate PdS quantum dots (QDs) in its cages, and Cd0.5Zn0.5S (CZS) solid solution nanoparticles (NPs) are anchored on its outer surface. The resultant PdS@UiOS@CZS with an optimal ratio between components displays an excellent photocatalytic H2 evolution rate of 46.1 mmol h-1 g-1 under visible light irradiation (420∼780 nm), which is 512.0, 9.2, and 5.9 times that of pure UiOS, CZS, and UiOS@CZS, respectively. The reason for the significantly enhanced performance is that the encapsulated PdS QDs strongly attract the photogenerated holes into the pores of UiOS, while the photogenerated electrons are effectively migrated to CZS due to the heterojunction effect, thereby effectively suppressing the recombination of charge carriers for further high-efficiency hydrogen production. This work provides an idea for developing efficient photocatalysts induced by hole attraction.

ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include 'No Referable Diabetic Macular Edema Grade (DME)' and 'Referable DME' while five categories consist of 'Proliferative diabetic retinopathy', 'Severe', 'Moderate', 'Mild', and 'No diabetic retinopathy'. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.