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RESEARCH ON THE TREND OF ECOLOGICAL AESTHETICS IN URBAN ENVIRONMENTAL ART DESIGN

In the relatively young field of landscape ecology, a prominent theme is the development of ecological aesthetics. Landscape ecologists and others are concerned with eiwironmental planning and management of ecological sustainability and enviromnental aesthetics. This article mainly studies the Trend of Ecological Aesthetics in Urban Environmental Art Design through literature research New evidence from various research fields is presented, which indicates that ecological aesthetics in urban environmental art design may reduce stress and have an impact on health, cognition and social psychology. Although in its infancy, research in these areas has shown important potential benefits related to the visual quality of the environment, which underscores the importance of research regarding the potential benefits of ecological aesthetics in urban enviromnental art design.

Precise Dynamic Consensus under Event-Triggered Communication

This work addresses the problem of dynamic consensus, which consists of estimating the dynamic average of a set of time-varying signals distributed across a communication network of multiple agents. This problem has many applications in robotics, with formation control and target tracking being some of the most prominent ones. In this work, we propose a consensus algorithm to estimate the dynamic average in a distributed fashion, where discrete sampling and event-triggered communication are adopted to reduce the communication burden. Compared to other linear methods in the state of the art, our proposal can obtain exact convergence under continuous communication even when the dynamic average signal is persistently varying. Contrary to other sliding-mode approaches, our method reduces chattering in the discrete-time setting. The proposal is based on the discretization of established exact dynamic consensus results that use high-order sliding modes. The convergence of the protocol is verified through formal analysis, based on homogeneity properties, as well as through several numerical experiments. Concretely, we numerically show that an advantageous trade-off exists between the maximum steady-state consensus error and the communication rate. As a result, our proposal can outperform other state-of-the-art approaches, even when event-triggered communication is used in our protocol.

Prediction of focal image for solar parabolic dish concentrator with square facets-an analytical model

Solar parabolic dish concentrator is one of the high-temperature applications of more than 400 °C for thermal and electrical power generation. In the solar parabolic dish concentrator, the arrangement of reflectors over the surface area is the significant factor for effective concentration of solar radiation. Also, focal image is one of the most influencing parameters in the design of receiver. Among the various reflectors, the square-shaped reflectors (facets) are comparatively effective in converging the incoming radiations to attain better focal image. In this regard, an attempt has been made to predict the focal image diameter of a solar parabolic dish concentrator with a square facet of different influencing parameters using a novel mathematical model. The influencing parameters considered for the study are aperture diameter, rim angle, and facet length of the dish concentrator. Using the model, the focal image dimension and aperture area of a solar parabolic dish concentrator with square facets can be predicted accurately for efficient design of a solar parabolic dish collector system. Finally, the model is validated with the experimentally obtained focal image diameter. The current model is in good agreement with the experimental value, with a deviation of 8.84%. Hence, the proposed model can be used for the design of parabolic dish systems.

Evaluation of an inflammation-based score for identification of appropriate patients for comprehensive genomic profiling

Performance status (PS) is widely used as an assessment of general condition in patients before performing comprehensive genomic profiling (CGP). However, PS scoring is dependent on each physician, and there is no objective and universal indicator to identify appropriate patients for CGP. Overall, 263 patients were scored using the modified Glasgow prognostic score (mGPS) from 0 to 2 based on the combination of serum albumin and c-reactive protein (CRP): 0, albumin ≥ 3.5 g/dl and CRP ≤ 0.5 mg/dl; 1, albumin < 3.5 g/dl or CRP > 0.5 mg/dl; and 2, albumin < 3.5 g/dl and CRP > 0.5 mg/dl. Overall survival was compared between mGPS 0-1 and mGPS 2 groups. The prognosis of patients with PS 0-1 and mGPS 2 was also evaluated. Thirty-nine patients (14.8%) were mGPS 2. Patients with mGPS 2 had significant shorter survival (14.7 months vs 4.6 months, p < 0.01). Twenty-eight patients were PS 0-1 and mGPS 2, and their survival was also short (5.6 months). Evaluation of mGPS is a simple and useful method for identifying patients with adequate prognosis using CGP.

Regulated Restructuring of Mucins During Secretory Granule Maturation In Vivo

Mucins are large, highly glycosylated transmembrane and secreted proteins that line and protect epithelial surfaces. However, the details of mucin biosynthesis and packaging in vivo are largely unknown. Here, we demonstrate that multiple distinct mucins undergo intragranular restructuring during secretory granule maturation in vivo, forming unique structures that are spatially segregated within the same granule. We further identify temporally-regulated genes that influence mucin restructuring, including those controlling pH (Vha16-1), Ca2+ ions (fwe) and Cl- ions (Clic and ClC-c). Finally, we show that altered mucin glycosylation influences the dimensions of these structures, thereby affecting secretory granule morphology. This study elucidates key steps and factors involved in intragranular, rather than intergranular segregation of mucins through regulated restructuring events during secretory granule maturation. Understanding how multiple distinct mucins are efficiently packaged into and secreted from secretory granules may provide insight into diseases resulting from defects in mucin secretion.

Robust stack-run coding for low bit-rate image transmission over noisy channels

We propose a multiresolution algorithm to jointly optimize a source coder and channel coder. The variable-rate source coder combines an optimal bit-allocation strategy and efficient stack-run coding. This compression scheme, concatenated with appropriate rate-compatible punctured convolutional codes, provides a competitive approach to recent state-of-the-art extensions of zerotree methods to noisy channels. Results show that under the assumption of almost zero probability of decoding error, the proposed scheme provides good performance for a lower complexity.

Preliminary investigation of a hypertonic saline nasal rinse as a hygienic intervention in dairy workers

Livestock workers experience an increased burden of bioaerosol-induced respiratory disease including a high prevalence of rhinosinusitis. Dairy operations generate bioaerosols spanning the inhalable size fraction (0-100 μm) containing bacterial constituents such as endotoxin. Particles with an aerodynamic diameter between 10 and 100 μm are known to deposit in the nasopharyngeal region and likely affect the upper respiratory tract. We evaluated the effectiveness of a hypertonic saline nasal lavage in reducing inflammatory responses in dairy workers from a high-volume dairy operation. Inhalable personal breathing zone samples and pre-/post-shift nasal lavage samples from each participant over five consecutive days were collected. The treatment group (n = 5) received hypertonic saline while the control group (n = 5) received normotonic saline. Personal breathing zone samples were analyzed for particulate concentrations and endotoxin using gravimetric and enzymatic methods, respectively. Pro- and anti-inflammatory cytokines (i.e., IL-8, IL-10, and TNF-α) were measured from nasal lavage samples using a multiplex assay. Inhalable dust concentrations ranged from 0.15 to 1.9 mg/m3. Concentrations of both pro- and anti-inflammatory cytokines, specifically IL-6, IL-8, and IL-10, were significantly higher in the treatment group compared to the control group (p < 0.02, p < 0.04, and p < 0.01, respectively). Further analysis of IL-10 anti-inflammatory indicates a positive association between hypertonic saline administration and IL-10 production. This pilot study demonstrates that hypertonic saline nasal lavages were successful in upregulating anti-inflammatory cytokines to support larger interventional studies.

Low Dimensional Representation of Fisher Vectors for Microscopy Image Classification

Microscopy image classification is important in various biomedical applications, such as cancer subtype identification, and protein localization for high content screening. To achieve automated and effective microscopy image classification, the representative and discriminative capability of image feature descriptors is essential. To this end, in this paper, we propose a new feature representation algorithm to facilitate automated microscopy image classification. In particular, we incorporate Fisher vector (FV) encoding with multiple types of local features that are handcrafted or learned, and we design a separation-guided dimension reduction method to reduce the descriptor dimension while increasing its discriminative capability. Our method is evaluated on four publicly available microscopy image data sets of different imaging types and applications, including the UCSB breast cancer data set, MICCAI 2015 CBTC challenge data set, and IICBU malignant lymphoma, and RNAi data sets. Our experimental results demonstrate the advantage of the proposed lowdimensional FV representation, showing consistent performance improvement over the existing state of the art and the commonly used dimension reduction techniques.

Tuning the Micro-coordination Environment of Al in Dealumination Y Zeolite to Enhance Electron Transfer at the Cu-Mn Oxides Interface for Highly Efficient Catalytic Ozonation of Toluene at Low Temperatures

The development of stable, highly active, and inexpensive catalysts for the ozone catalytic oxidation of volatile organic compounds (VOCs) is challenging but of great significance. Herein, the micro-coordination environment of Al in commercial Y zeolite was regulated by a specific dealumination method and then the dealuminated Y zeolite was used as the support of Cu-Mn oxides. The optimized catalyst Cu-Mn/DY exhibited excellent performance with around 95% of toluene removal at 30 °C. Besides, the catalyst delivered satisfactory stability in both high-humidity conditions and long-term reactions, which is attributed to more active oxygen vacancies and acidic sites, especially the strong Lewis acid sites newly formed in the catalyst. The decrease in the electron cloud density around aluminum species enhanced electron transfer at the interface between Cu-Mn oxides. Moreover, extra-framework octahedrally coordinated Al in the support promoted the electronic metal-support interaction (EMSI). Compared with single Mn catalysts, the incorporation of the Cu component changed the degradation pathway of toluene. Benzoic acid, as the intermediate of toluene oxidation, can directly ring-open on Cu-doped catalysts rather than being further oxidized to other byproducts, which increased the rate of the catalytic reaction. This work provides a new insight and theoretical guidance into the rational design of efficient catalysts for the catalytic ozonation of VOCs.

FDD: a deep learning-based steel defect detectors

Surface defects are a common issue that affects product quality in the industrial manufacturing process. Many companies put a lot of effort into developing automated inspection systems to handle this issue. In this work, we propose a novel deep learning-based surface defect inspection system called the forceful steel defect detector (FDD), especially for steel surface defect detection. Our model adopts the state-of-the-art cascade R-CNN as the baseline architecture and improves it with the deformable convolution and the deformable RoI pooling to adapt to the geometric shape of defects. Besides, our model adopts the guided anchoring region proposal to generate bounding boxes with higher accuracies. Moreover, to enrich the point of view of input images, we propose the random scaling and the ultimate scaling techniques in the training and inference process, respectively. The experimental studies on the Severstal steel dataset, NEU steel dataset, and DAGM dataset demonstrate that our proposed model effectively improved the detection accuracy in terms of the average recall (AR) and the mean average precision (mAP) compared to state-of-the-art defect detection methods. We expect our innovation to accelerate the automation of industrial manufacturing process by increasing the productivity and by sustaining high product qualities.

Emerging approaches for decoding neuropeptide transmission

Neuropeptides produce robust effects on behavior across species, and recent research has benefited from advances in high-resolution techniques to investigate peptidergic transmission and expression throughout the brain in model systems. Neuropeptides exhibit distinct characteristics which includes their post-translational processing, release from dense core vesicles, and ability to activate G-protein-coupled receptors (GPCRs). These complex properties have driven the need for development of specialized tools that can sense neuropeptide expression, cell activity, and release. Current research has focused on isolating when and how neuropeptide transmission occurs, as well as the conditions in which neuropeptides directly mediate physiological and adaptive behavioral states. Here we describe the current technological landscape in which the field is operating to decode key questions regarding these dynamic neuromodulators.

Practical factors affecting the application of interactive environmental panorama art in diverse urban exploration: A case study of San Francisco

When touring multicultural cities, planning everything out in advance can enrich the visiting experience. Advances in the internet and computer graphics technology have resulted in the trend of using virtual tourism for trip planning. However, the mere presentation of virtual street views of a geographical location does not highlight its cultural elements and characteristics. The lack of imaginative space and extensive guidance result in the lack of aesthetic artistic visual stimulation and interest. Accordingly, this study developed a novel experience model and an experimental platform where images with panoramic oil painting art styles were used to provide participants with interactive tours that presented different cultural characteristics and elements. This platform enabled the re-creation of crucial scenic urban spots in spherical, panoramic, and three-dimensional spaces, which allowed the participants to explore and experience these spots, aroused their interest, and stimulated their senses. Moreover, timelines were used and the beauty of visual art was highlighted to achieve efficient virtual tourism. A total of 100 students were invited to experience the designed virtual tourism system and fill out questionnaires. After the participants operated the system, statistical analyses were conducted to determine the categories of and the factors that influenced participant assessment. The categories of participant assessment were assessment based on design elements, subjective assessment based on feelings, assessment based on multicultural exploration, and assessment based on system improvement expectations. Moreover, the eight factors influencing participant assessment were immersive presence, ease of use, connectivity of diverse cultures, content appealing, visual pleasure, interesting graphic design, adaptive UI, and UI affordance. A modular design model was used to perform project analyses, design relevant items, and assess the participants' experiences when visiting multicultural scenic spots. On the basis of the aforementioned steps, conclusions were drawn, and system design recommendations are proposed for platforms offering multicultural experiences.

EDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy

Present architecture of convolution neural network for diabetic retinopathy (DR-Net) is based on normal convolution (NC). It incurs high computational cost as NC uses a multiplicative weight that measures a combined correlation in both cross-channel and spatial dimension of layer's inputs. This might cause the overall DR-Net architecture to be over-parameterised and computationally inefficient. This paper proposes EDR-Net a new end-to-end, DR-Net architecture with depth-wise separable convolution module. The EDR-Net architecture was trained with DRKaggle-train dataset (35,126 images), and tested on two datasets, i.e. DRKaggle-test (53,576 images) andMessidor-2 (1,748 images). Results showed that the proposed EDR-Net achieved predictive performance comparable with current state-of-the-arts in detecting referable diabetic retinopathy (rDR) from fundus images and outperformed other light weight architectures, with at least two times less computation cost. This makes it more amenable for mobile device based computer-assisted rDR screening applications.

Lateral and Vertical Scaling of In0.7Ga0.3As HEMTs for Post-Si-CMOS Logic Applications

In this paper, we have experimentally investigated the impact of lateral and vertical scaling of In0.7Ga0.3As high-electron-mobility transistors (HEMTs) onto their logic performance. We have found that reducing the In0.52Al0.48As insulator thickness results in much better electrostatic integrity and improved short-channel behavior down to a gate length of around 60 nm. Our nearly enhancement-mode 60-nm HEMTs feature V-T = -0.02 V, DIBL = 93 mV/V, S = 88 mV/V, and ION/ I-OFF = 1.6 x 10(4), at V-DD = 0.5 V. We also estimate a gate delay of CV/1 = 1.6 ps at VDD = 0.5 V. We have bench-marked these devices against state-of-the-art Si CMOS. For the same leakage current, which includes the gate leakage current, the InGaAs HEMTs exhibit 1.2X more current drive (ION) than the state-of-the-art 65-nm low-power CMOS technology at V-DD=0.5 V.

Curriculum self-paced learning for cross-domain object detection

Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e.g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN. On top of combining Cycle-GAN transformations and self-paced learning in a smart and efficient way, in this paper, we propose a novel self-paced algorithm that learns from easy to hard. Our method is simple and effective, without any overhead during inference. It uses only pseudo-labels for samples taken from the target domain, i.e. the domain adaptation is unsupervised. We conduct experiments on four cross-domain benchmarks, showing better results than the state of the art. We also perform an ablation study demonstrating the utility of each component in our framework. Additionally, we study the applicability of our framework to other object detectors. Furthermore, we compare our difficulty measure with other measures from the related literature, proving that it yields superior results and that it correlates well with the performance metric.

Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions

In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter-and intra-subject image registration.

Person Re-Identification by Iterative Re-Weighted Sparse Ranking

In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft-and hard-re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single-and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.

Artificially Layered CoSe2 Nanosheets by a Dual-Templating Strategy for High-Performance Lithium-Sulfur Batteries

Owing to the attractive merits of layered transition metal dichalcogenides (LTMDs) with van der Waals interactions, it is significant to modulate electronic structures and endow them with fascinating physiochemical properties by converting a nonlayered metal dichalcogenide into an atomic layered one. Herein, a dual-templating strategy is designed to prepare artificially layered CoSe2 nanosheets on carbon fiber cloth (L-CoSe2/CFC). It is found that not only the nanosheet morphology but also the layered structure is well inherited from the precursor of layered Co(OH)2 nanosheets through a wet-solution ion-exchange approach. The as-prepared L-CoSe2/CFC serves as an efficient multifunctional interlayer to solve the challenges of "shuttling effect" and slow multistep reaction kinetics in lithium-sulfur batteries (LSBs), thus dramatically improving their electrochemical performance. Benefiting from the L-CoSe2 nanosheets with large interlayer spacing, strong chemical adsorption, and superior catalytic activity, L-CoSe2/CFC promotes the anchoring of lithium polysulfides (LiPSs) and their catalytic conversion. Consequently, the L-CoSe2/CFC cell yields a large reversible capacity of 1584 mAh g-1 at 0.2C and a high rate capability of 987 mAh g-1 at 4C. A high areal capacity of 4.38 mAh cm-2 after 100 cycles at 0.2C is achieved for the high-S-loading LSB (4.6 mg cm-2) using the L-CoSe2/CFC interlayer.

Identification of genes associated with male sterility in a mutant of white birch (Betula platyphylla Suk.)

White birch (Betula platyphylla Suk.) is a monoecious tree species with unisexual flowers. In this study, we used a spontaneous mutant genotype that produced normal-like male (NLM) inflorescences and mutant male (MM) inflorescences at different locations within the tree to investigate the genes necessary for pollen development. A cDNA-amplified fragment length polymorphism (cDNA-AFLP) analysis was used to identify genes differentially expressed between the two types of inflorescences. Of approximately 5000 transcript-derived fragments (TDFs) obtained, 323 were significantly differentially expressed, of which 141 were successfully sequenced. BLAST analyses revealed 51.8% of the sequenced TDFs showed significant homology with proteins of known or predicted functions, 10.6% showed significant homology with putative proteins without any known or predicted function, and the remaining 37.6% had no hits in the NCBI database. Further, in a functional categorization based on the BLAST analyses, the protein fate, metabolism, energy categories had in order the highest percentages of the proteins. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the known TDFs were mainly involved in metabolic (28.4%), signal transduction (23.5%) and folding, sorting and degradation (13.6%) pathways. Ten genes from the NLM and MM development stages in the mutant were analyzed by quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR). The information generated in this study can provide some useful clues to help understand male sterility in B. platyphylla.

ART-TV Algorithm for Diffuse Correlation Tomography Blood Flow Imaging

Near-infrared diffuse correlation imaging (DCT) is an important method of tissue blood flow imaging for the prognosis and diagnosis of various diseases. A new solution of DCT that is based on the N th-order linear (NL) algorithm, termed as NL-DCT, was proposed in our previous study to overcome the limitations of tissue geometry and heterogeneity. The NL-DCT converts the image reconstruction into linear equations, and this solution is an ill-posed problem in mathematics. To improve the accuracy and robustness of the DCT image reconstruction, a combination of algebra reconstruction technique (ART) and total variation (TV), namely ART-TV, is proposed in this study. After each ART iteration, the TV model is used as an a priori constraint to reduce noise. The validations from computer simulation and phantom experiments with different anomalies demonstrate that the proposed ART-TV algorithm is efficient in DCT blood flow image reconstruction.

Semantic Labeling of ALS Point Cloud via Learning Voxel and Pixel Representations

Semantic labeling is a fundamental task that can provide useful semantics for many other 3-D processing tasks. To tackle the challenge of airborne laser scanning (ALS) point cloud classification, current state-of-the-art methods leverage the capabilities of deep learning. However, they are limited due to the weaknesses of the isolated use of individual representations of point clouds. To address this issue, this letter presents a novel network, VPNet, which ensembles voxel and pixel representation-based networks, to predict class probabilities for each light detection and ranging (LiDAR) point. A fully connected conditional random field-based global refinement is then performed over each point in the point cloud to produce a fine-grained classification result. On the ISPRS 3-D Semantic Labeling Contest, our solution sets a new state of the art by improving the highest average F1-score and the highest average per-class accuracy from 69.3% to 73.9%, and 69.0% to 74.9%, respectively. The overall accuracy of our approach is 84.0%.

Immediate Antiretroviral Therapy: The Need for a Health Equity Approach

Immediate antiretroviral therapy (iART), defined as same-day initiation of ART or as soon as possible after diagnosis, has recently been recommended by global and national clinical care guidelines for patients newly diagnosed with human immunodeficiency virus (HIV). Based on San Francisco's Rapid ART Program Initiative for HIV Diagnoses (RAPID) model, most iART programs in the US condense ART initiation, insurance acquisition, housing assessment, and mental health and substance use evaluation into an initial visit. However, the RAPID model does not explicitly address structural racism and homophobia, HIV-related stigma, medical mistrust, and other important factors at the time of diagnosis experienced more poignantly by African American, Latinx, men who have sex with men (MSM), and transgender patient populations. These factors negatively impact initial and subsequent HIV care engagement and exacerbate significant health disparities along the HIV care continuum. While iART has improved time to viral suppression and linkage to care rates, its association with retention in care and viral suppression, particularly in vulnerable populations, remains controversial. Considering that in the US the HIV epidemic is sharply defined by healthcare disparities, we argue that incorporating an explicit health equity approach into the RAPID model is vital to ensure those who disproportionately bear the burden of HIV are not left behind.

A recent survey on image watermarking techniques and its application in e-governance

This survey presents a brief discussion of different aspects of digital image watermarking. Included in the present discussion are these general concepts: major characteristics of digital watermark, novel and recent applications of watermarking, different kinds of watermarking techniques and common watermark embedding and extraction process. In addition, recent state-of-art watermarking techniques, potential issues and available solutions are discussed in brief. Further, the performance summary of the various state-of-art watermarking techniques is presented in tabular format. This survey contribution will be useful for the researchers to implement efficient watermarking techniques for secure e-governance applications.

Graphene Magnetic Field Sensors

Graphene extraordinary magnetoresistance (EMR) devices have been fabricated and characterized in varying magnetic fields at room temperature. The atomic thickness, high carrier mobility and high current carrying capabilities of graphene are ideally suited for the detection of nanoscale sized magnetic domains. The device sensitivity can reach 10 mV/Oe, larger than state of the art InAs 2DEG devices of comparable size and can be tuned by the electric field effect via a back gate or by imposing a biasing magnetic field.

The future of targeted therapies for brain metastases

Brain metastases (BM) are an increasing challenge in the management of patients with advanced cancer. Treatment options for BM are limited and mainly focus on the application of local therapies. Systemic therapies including targeted therapies are only poorly investigated, as patients with BM were frequently excluded from clinical trials. Several targeted therapies have shown promising activity in patients with BM. In the present review we discuss existing and emerging targeted therapies for the most frequent BM primary tumor types. We focus on challenges in the conduction of clinical trials on targeted therapies in BM patients such as patient selection, combination with radiotherapy, the obstacles of the blood-brain barrier and the definition of study end points.

THE FIRST CASE IN RUSSIA OF DYNAMIC ILLUMINATION USING LIGHT EMITTING DIODES FOR A MUSEUM INSTALLATION

This article describes the dynamic illumination of the masterpieces collection of carriage art in the State Hermitage museum using light emitting diodes. Philips (the first example of such an illumination in Russia) implements this illumination.

Deep metric learning for image retrieval in smart city development

Deep metric learning (DML) aims to learn a consistent distance embedding where an anchor is closer within the same category than others. It underpins a variety of essential and significant tasks in the development of smart city including face recognition, landmark retrieval, pedestrian detection, person/vehicle re-identification, and so on. Traditional pair-based DML methods try to make full use of the data-to-data relations within a (mini-)batch, but they cannot grasp the data distribution information due to the batch size limitation. On the other hand, proxy-based DML schemes use different proxies to approximate the data distribution. However, the proxies are too sample to represent the intra-category variance. In this paper, we propose a simple but effective method, named soft-instance-label proxy, for embedding learning. It can capture the globe data distribution information while depicting the detailed intra-class data structure. The state-of-the-art empirical results on three public image retrieval benchmarks and two backbone networks demonstrate the superiority of our proposed method. Our Softinstance-label proxy method can have a Recall@1 improvement of 2.4% with Googlenet, largely surpassing the current state-of-art-methods while demonstrating great potential in the development of smart city.

Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.

Carbonized Chinese Art Paper-Based High-Performance Wearable Strain Sensor for Human Activity Monitoring

Because of the rapid evolution of wearable devices, great effort has been devoted to widely developing flexible strain sensors, as one of the most important components. However, realizing the low-cost, large-scale manufacturing of flexible strain sensor with a wide response range and excellent sensitivity remains difficult to date. Chinese art paper (CAP), a significant Chinese cultural heritage, is a processed product of biological materials prepared from plant fibers that can be considered as a nonwoven type of film. Herein, we show the manufacturing of the outstanding strain sensors based on carbonized commercial CAP (CCAP), which possess fascinating properties, including large response range (0-120%), excellent sensitivity (gauge factor (GF) of 68 under the strain of 100% and 248 in the strain of (100-120%)), high durability, high stability, and low detection limits (0.01% strain). Furthermore, we demonstrate their abilities in monitoring both vigorous and tiny human motions, showing promising applications in wearable devices and artificial intelligence robots.

LiveNet: Improving features generalization for face liveness detection using convolution neural networks

Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face anti-spoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIA-FASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches. (C) 2018 Elsevier Ltd. All rights reserved.

Hepatic transcriptome profiling according to growth rate reveals acclimation in metabolic regulatory mechanisms to cyclic heat stress in broiler chickens

Climate change has numerous effects on poultry that result in welfare concerns and economic losses in agricultural industries. However, the mechanisms underlying the acclimation to heat stress in poultry have not been comprehensively defined. Therefore, identifying associated patterns of gene regulation and understanding the molecular mechanisms of acclimation to a warmer environment will provide insights into the acclimation system of broiler chickens. We profiled differentially expressed genes (DEGs) associated with differences in growth performance under heat stress conditions in the liver tissues of broilers based on RNA sequencing data. The DEGs were identified by comparison to the gene expression levels of broilers exhibiting average growth at 28 d of age (D28A) and D36A relative to those at D21A. In D36A, 507 and 312 DEGs were up- and downregulated, respectively, whereas 400 and 156 DEGs were up- and downregulated in D28A, respectively. Pathway enrichment analysis further revealed that "fatty acid degradation" and "heat shock protein expression" were upregulated in broilers exhibiting a higher growth and weight, whereas "cell cycle arrest" and "amino acid metabolism" were downregulated. Transcriptome profiling revealed that the acclimatized group supplied fat and energy from the liver to tissues through the breakdown of fatty acids. Furthermore, homeostasis was maintained via heat shock proteins and antioxidant enzymes. The characterized candidate genes and mechanisms associated with the response to heat stress might serve as a foundation for improving the ability of broilers to acclimatize under heat stress conditions.

Inverse relationship between elemental selenium nanoparticle size and inhibition of cancer cell growth in vitro and in vivo

Elemental selenium nanoparticles (SeNPs) have been demonstrated to be equivalent to selenomethionine and methylselenocysteine in upregulating selenoenzymes; however, the toxicity of SeNPs is markedly lower than these two organic selenium compounds. The objective of this study was to determine the effect of SeNP size on cancer cell growth and ascertain whether production of reactive oxygen species (ROS) is implicated as a candidate mechanism of action. Two types of SeNPs (averaging 35 nm and 91 nm) were investigated. Cell accumulation was inhibited in vitro and in vivo in a manner inversely proportional to particle size. In vitro modeling experiments showed the reduction of SeNPs to be glutathione concentration dependent and to result in ROS formation. Both SeNP biotransformation and ROS production were size dependent, with the smaller SeNPs being more active, thereby suggesting that small-sized SeNPs are more effective in inhibiting cancer cell proliferation through an ROS mediated mechanism.

Practitioners' Experiences of the Influence of Bonsai Art on Health

Bonsai art refers to the cultivation of a miniature tree. This study was motivated by the hypothesis that bonsai art may also be an ecopsychological, therapeutic practice that can have meaningful healing qualities. An international online survey elicited the meaning of bonsai art for 255 skilled bonsai practitioners. Questionnaires and interviews were used to elicit the experiences of participants. The findings supported the hypothesis that, for skilled practitioners, bonsai art was associated with meaningful healing experiences. In particular, the evidence suggests that bonsai art facilitates improved ecological, spiritual and emotional awareness, as well as various healing dimensions, including aesthetic creativity, resilience, adaptability, and social, physical, and personal health. It is viewed as an intervention technique that requires few resources, is easy to apply, and has a minimal impact on any environmental setting. The conclusions drawn point to the ethically sound health promotion value of bonsai art in various settings, such as psychiatric hospitals, retirement homes, rehabilitation centres and prisons.

Gold(I)-Catalyzed Benzylic C(sp3 )-H Functionalizations: Divergent Synthesis of Indole[a]- and [b]-Fused Polycycles

Phenyl azides substituted by an (alkylphenyl)ethynyl group facilitate benzylic sp3 (C-H) functionalization in the presence of a JohnPhosAu catalyst, resulting in indole-fused tetra- and pentacycles via divergent N- or C-cyclization. The chemoselectivity is influenced depending on the counter-anion, the electron density of the α-imino gold(I) carbene, and the alkyl groups stabilizing the benzylic carbocation originating from a 1,5-hydride shift. An isotopic labeling experiment demonstrates the involvement of an indolylgold(I) species resulting from a tautomerization that is much faster than the deauration. The formation of a benzylic sp3 (C-H) functionalization leading to an indole-fused seven-membered ring is also demonstrated.

Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models such as COSMO-ART. Based on simultaneous measurement of Ambrosia pollen concentrations and meteorological variables in the source area, ANNs were trained to predict the diurnal profile of pollen emission. Six different combinations of explanatory meteorological variables were trained with five different ANN configurations resulting in 30 candidate emission models. The best network configuration for each combination of explanatory variables were used as emission parameterization in the numerical pollen dispersion model COSMO-ART. In addition, two benchmarks were implemented: an emission parameterization based on sigmoid functions and an artificial neural network using only time as an explanatory variable. The Ambrosia pollen seasons of 2015 and 2016 were simulated using the two benchmarks and the six emission parameterizations. The modelled diurnal profile of emission fluxes at 15 different sites from Serbia, Hungary and France with strong local pollen sources were compared with observed concentrations. Artificial Neural Networks based emission parameterization substantially improved the performance of the Ambrosia pollen emission in COSMO-ART compared to the emission based on the sigmoid functions in all these three countries. However, a time-related explanatory variable must be used. This suggests that the ANN-based emission parameterizations can be used at distant locations as well. On the other hand, the use of meteorological related parameters did not increase the performance compared with the time-only benchmark.

Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images

Many land cover change detection (LCCD) approaches applied on very high resolution (VHR) remote sensing images utilize spatial information by using a regular window or strict mathematical model. However, regular shape or strict models cannot fit the various shapes and sizes of the ground targets. In this article, a novel LCCD approach without the parameter is proposed to detect land cover change with VHR remote sensing images. First, an adaptive spatial-context extraction algorithm is applied to explore contextual information around a pixel. Second, the change magnitude between pairwise pixels is quantitatively measured by computing the band-to-band distance which is defined by the pairwise adaptive regions around the corresponding pixels. Finally, after the generation of a change magnitude image (CMI), a binary threshold method called double-window flexible pace search (DFPS) is adopted to divide CMI into a binary change detection map. The performance of the proposed approach is verified by comparing it with five state-of-the-art methods with three pairs of VHR images. The comparisons demonstrated that the proposed approach achieved the improved detected results comparing with state-of-the-art LCCD methods. The code of the proposed approach is available at https://github.com/TongfeiLiu/ASEA-CD.

Noise Reduction in CT Using Learned Wavelet-Frame Shrinkage Networks

Encoding-decoding (ED) CNNs have demonstrated state-of-the-art performance for noise reduction over the past years. This has triggered the pursuit of better understanding the inner workings of such architectures, which has led to the theory of deep convolutional framelets (TDCF), revealing important links between signal processing and CNNs. Specifically, the TDCF demonstrates that ReLU CNNs induce low-rankness, since these models often do not satisfy the necessary redundancy to achieve perfect reconstruction (PR). In contrast, this paper explores CNNs that do meet the PR conditions. We demonstrate that in these type of CNNs soft shrinkage and PR can be assumed. Furthermore, based on our explorations we propose the learned wavelet-frame shrinkage network, or LWFSN and its residual counterpart, the rLWFSN. The ED path of the (r)LWFSN complies with the PR conditions, while the shrinkage stage is based on the linear expansion of thresholds proposed Blu and Luisier. In addition, the LWFSN has only a fraction of the training parameters (<1%) of conventional CNNs, very small inference times, low memory footprint, while still achieving performance close to state-of-the-art alternatives, such as the tight frame (TF) U-Net and FBPConvNet, in low-dose CT denoising.

Techno-economic optimization of biomass-to-methanol with solid-oxide electrolyzer

The purpose of this paper is to assess techno-economically the integration of solid-oxide electrolysis in biomass-to-methanol processes: (1) The hydrogen produced by electrolysis can be used to adjust the composition of syngas from gasification to increase the conversion of carbon in biomass, (2) the oxygen as a byproduct of electrolysis can be used in the gasifier to avoid expensive air separation units, and (3) the overall process can be thermally integrated. Two integration concepts are proposed with different sizing methods of the electrolyzer: (1) the case of full conversion of carbon in biomass, in which a large electrolyzer is driven by the electricity purchased from the grid, and (2) the case of zero power exchange, in which only part of the carbon in biomass is converted reaching self-sufficiency of electricity. The three cases including the state-of-the-art biomass-to-methanol process are investigated to identify (1) possible trade-offs between efficiency and costs, and (2) under which conditions, these concepts become economically viable. With a reference methanol production of 100 kton/year, the results show that there is an optimal design for the state-of-the-art case, which offers an efficiency of 53.3% due to steam cycles and a payback time of 4.8 years. For the integrated concepts, there are sharp trade-offs between the system efficiency and methanol production cost rate. The case of full carbon conversion can reach an energy efficiency of 64.5-66.0% but results in a longer payback time of over 11 years. The case of zero-power exchange can achieve a similar efficiency as the state-of-the-art case with a slightly increased payback time of over 5.5 years. The payback time of the full carbon conversion case can be shorter than 5 years with a reduction in stack cost and electricity price, and an increase in stack lifetime.

Systemic racism alters wildlife genetic diversity

In the United States, systemic racism has had lasting effects on the structure of cities, specifically due to government-mandated redlining policies that produced racially segregated neighborhoods that persist today. However, it is not known whether varying habitat structures and natural resource availability associated with racial segregation affect the demographics and evolution of urban wildlife populations. To address this question, we repurposed and reanalyzed publicly archived nuclear genetic data from 7,698 individuals spanning 39 terrestrial vertebrate species sampled in 268 urban locations throughout the United States. We found generally consistent patterns of reduced genetic diversity and decreased connectivity in neighborhoods with fewer White residents, likely because of environmental differences across these neighborhoods. The strength of relationships between the racial composition of neighborhoods, genetic diversity, and differentiation tended to be weak relative to other factors affecting genetic diversity, possibly in part due to the recency of environmental pressures on urban wildlife populations. However, the consistency of the direction of effects across disparate taxa suggest that systemic racism alters the demography of urban wildlife populations in ways that generally limit population sizes and negatively affect their chances of persistence. Our results thus support the idea that limited capacity to support large, well-connected wildlife populations reduces access to nature and builds on existing environmental inequities shouldered by predominantly non-White neighborhoods.

A modularity design approach to behavioral research with immersive virtual reality: A SkyrimVR-based behavioral experimental framework

Virtual reality (VR) has been shown to be a potential research tool, yet the gap between traditional and VR behavioral experiment systems poses a challenge for many behavioral researchers. To address the challenge posed, the present study first adopted a modularity design strategy and proposed a five-module architectural framework for a VR behavioral experiment system that aimed to reduce complexity and costs of development. Applying the five-module architectural framework, the present study developed the SkyrimVR-based behavioral experimental framework (SkyBXF) module, a basic experimental framework module that adopted and integrated the classic human behavior experiment structure (i.e., session-block-trial model) with the modifiable VR massive gaming franchise The Elder Scrolls V: Skyrim VR. A modified version of previous behavioral research to investigate the effects of masked peripheral vision on visually-induced motion sickness in an immersive virtual environment was conducted as a proof of concept to showcase the feasibility of the proposed five-module architectural framework and the SkyBXF module developed. Behavioral data acquired through the case study were consistent with those from previous behavioral research. This indicates the viability of the proposed five-module architectural framework and the SkyBXF module developed, and provides proof that future behavioral researchers with minimal programming proficiency, 3D environment development expertise, time, personnel, and resources may reuse ready-to-go resources and behavioral experiment templates offered by SkyBXF to swiftly establish realistic virtual worlds that can be further customized for experimental need on the go.

Glutathione S-transferase genetic polymorphisms and fluoride-induced reproductive toxicity in men with idiopathic infertility

Male infertility caused by idiopathic oligoasthenospermia (OAT) is known as idiopathic male infertility. Glutathione S-transferase (GST) and fluoride may play important roles in idiopathic male infertility, but their effects are still unknown. Our study examined the relationship between GST polymorphisms and fluoride-induced toxicity in idiopathic male infertility and determined the underlying mechanism. Sperm, blood, and urine samples were collected from 560 males. Fluoride levels were measured by a highly selective electrode method, and GST genotypes were identified using polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (PCR-RFLP). Semen parameters, DNA fragmentation index (DFI), mitochondrial membrane potential (MMP), and oxidative stress (OS) biomarkers were statistically assessed at the P < 0.05 level. Compared with healthy fertile group, semen parameters, fluoride levels, OS biomarkers, sex hormone levels, and MMP and DFI levels were lower in the idiopathic male infertility group. For glutathione S-transferase M1 (GSTM1[-]) and glutathione S-transferase T1 (GSTT1[-]) or glutathione S-transferase P1 (GSTP1) mutant genotypes, levels of semen fluoride, OS, MMP, and DFI were considerably higher, and the mean levels of sperm parameters and testosterone were statistically significant in GSTM1(+), GSTT1(+), and GSTP1 wild-type genotypes. Both semen and blood fluoride levels were associated with oxidative stress in idiopathic male infertility patients. Elevated fluoride in semen with the genotypes listed above was linked to reproductive quality in idiopathic male infertility patients. In conclusion, GST polymorphisms and fluorine may have an indicative relationship between reproductive quality and sex hormone levels, and OS participates in the development of idiopathic male infertility.

Drug testing for mitragynine and kratom: Analytical challenges and medico-legal considerations

Mitragyna speciosa, known as kratom, is a tropical tree native to Southeast Asia that has long been used to increase energy and in traditional medicine. Kratom leaves contain several indole alkaloids including mitragynine, mitraciliatine, speciogynine, and speciociliatine, which have the same molecular formula and connectivity, but different spatial arrangements (i.e., diastereomers). A routine liquid-chromatographic-high-resolution mass-spectrometric (LC-HRMS) multi-analyte method for addictive and herbal drugs in urine did not separate mitragynine from speciogynine and speciociliatine. Separation and individual measurement of the four diastereomers was possible with an improved LC method. All diastereomers were detected in 29 patient urine samples who tested positive for mitragynine with the routine method, albeit at variable absolute amounts and relative proportions. The presence of all diastereomers rather than individual substances indicated that they originated from the intake of kratom (i.e., plant material). Speciociliatine dominated in most samples (66%), whereas mitragynine and mitraciliatine were the highest in 17% each. A kratom product (powdered plant material) marketed in Sweden contained all diastereomers with mitragynine showing the highest level. In Sweden, there are signs of an increasing use of kratom in society, based on the results from drug testing, the number of poisons center consultations on intoxications, and customs seizure statistics. Because there may be health risks associated with kratom use, including dependence, serious adverse reactions, and death, analytical methods should be able to identify and quantify all diastereomers. In Sweden, this is important from a legal perspective, as only mitragynine is classified, whereas the other three diastereomers, and kratom (plant material), are not.

Safety-Optimized Inductive Powering of Implantable Medical Devices: Tutorial and Comprehensive Design Guide

A tutorial and comprehensive guide are presented for the design of planar spiral inductors with maximum energy delivery in biomedical implants. Rather than maximizing power transfer efficiency (PTE), the ratio of the received power to the square of the magnetic flux density is maximized in this technique. This ensures that the highest power is delivered for a given level of safe electromagnetic radiation, as measured by the specific absorption rate (SAR) in the tissue. By using quasi-static field approximations, the maximum deliverable power under SAR constraints is embedded in a lumped-element model of a 2-coil inductive link, from which planar coil geometries are derived. To compare the proposed methodology with the conventional approach that maximizes PTE, the results of both techniques are compared for three examples of state-of-the-art designs. It is demonstrated that the presented technique increases the maximum deliverable power while operating at a given level of non-ionizing radiation by factors of 8x, 410x, and 560x as compared to the three existing designs, and maintaining moderate link efficiencies of 12%, 23%, and 12%, respectively.

Recent advances in improving intranasal allergen-specific immunotherapy; focus on delivery systems and adjuvants

Allergen-specific Immunotherapy (AIT) is the main therapeutic strategy to control and treat allergic disorders. Intranasal Immunotherapy (INIT) was introduced as a needle-free, noninvasive, and efficient approach among various routes of allergen administration. Since direct exposure of nasal mucosa to allergen extracts could induce local and systemic reactions, recent studies focus on establishing novel formulations using various delivery systems and adjuvants to improve INIT efficacy. This review categorizes and describes natural and synthetic micro/nanoparticles such as chitosan, PLGA, liposome, exosome, and nano-emulation droplets used as delivery systems or immunomodulatory and immune-regulatory agents. Also, multiple microbial agents, including probiotics, mycobacterial and viral components, TLR ligands, and biologic agents, i.e., antibody fragments, recombinant cytokines, vitamin A, and pulsed dendritic cells (DCs), are other platforms that are discussed. In addition, future perspectives and proposed strategies to help INIT were provided.

A Fast Algorithm for Fractional QCQP and Applications to Secure Beamforming in Cognitive Nonorthogonal Multiple Access Networks

In this paper, we investigate a nonconvex fractional quadratically constrained quadratic problem (fractional QCQP), which has a wide application to the resource allocation optimization in wireless communication systems. Different from the state-of-the-art methods needing to solve semidefinite programmings or second-order cone programmings, we propose a fast algorithm for solving fractional QCQP by combining the successive convex approximation method and the consensus alternating direction method of multipliers, which has only simple computations and works very fast in applications with modest accuracy. We also apply the proposed fast algorithm to secure beamforming design for enhancing physical layer security in cognitive nonorthogonal multiple access (NOMA) networks, where secrecy rate optimization problems in both underlay and overly cognitive NOMA networks are typical fractional QCQPs and have not been well studied. Simulation results have shown that our proposed fast algorithm achieves almost the same performance as the state-of-the-art methods, however, the proposed fast algorithm has very low computation complexity.

DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images

Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applications, especially for graph-based or seed-based reconstruction methods and can help to visualize the morphology structures. There are a few hand-crafted models proposed to detect the branch points. However, they are highly dependent on the empirical setting of the parameters for different images. In this paper, we propose a DeepBranch model for branch point detection with two-level designed convolutional networks, a candidate region segmenter and a false positive reducer. On the first level, an improved 3D U-Net model with anisotropic convolution kernels is employed to detect initial candidates. Compared with the traditional sliding window strategy, the improved 3D U-Net can avoid massive redundant computations and dramatically speed up the detection process by employing dense-inference with fully convolutional neural networks (FCN). On the second level, a method based on multi-scale multi-view convolutional neural networks (MSMV-Net) is proposed for false positive reduction by feeding multi-scale views of 3D volumes into multiple streams of 2D convolution neural networks (CNNs), which can take full advantage of spatial contextual information as well as fit different sizes. Experiments on multiple 3D biomedical images of neurons, retinal blood vessels and bronchi confirm that the proposed 3D branch point detection method outperforms other state-of-the-art detection methods, and is helpful for graph-based or seed-based reconstruction methods.

EasyGuide Plasmids Support in Vivo Assembly of gRNAs for CRISPR/Cas9 Applications in Saccharomyces cerevisiae

Most CRISPR/Cas9 applications in yeast rely on a plasmid-based expression of Cas9 and its guide RNA (gRNA) containing a 20-nucleotides (nts) spacer tailored to each genomic target. The lengthy assembly of this customized gRNA requires at least 3-5 days for its precloning in Escherichia coli, purification, validation, and cotransformation with Cas9 into a yeast strain. Here, we constructed a series of 12 EasyGuide plasmids to simplify CRISPR/Cas9 applications in Saccharomyces cerevisiae. The new vectors provide templates for generating PCR fragments that can assemble up to six functional gRNAs directly into yeasts via homologous recombination between the 20-nts spacers. By dispensing precloning in E. coli, yeast in vivo gRNA assembly significantly reduces the CRISPR/Cas9 experimental workload. A highly efficient yeast genome editing procedure, involving PCR amplification of gRNAs and donors, followed by their transformation into a Cas9-expressing strain, can be easily accomplished through a quick protocol.

Zero-shot event detection via event-adaptive concept relevance mining

Zero-shot complex event detection has been an emerging task in coping with the scarcity of labeled training videos in practice. Aiming to progress beyond the state-of-the-art zero-shot event detection, we propose a new zero-shot event detection approach, which exploits the semantic correlation between an event and concepts. Based on the concept detectors pre-trained from external sources, our method learns the semantic correlation from the concept vocabulary and emphasizes on the most related concepts for the zero-shot event detection. Particularly, a novel Event-Adaptive Concept Integration algorithm is introduced to estimate the effectiveness of semantically related concepts by assigning different weights to them. As opposed to assigning weights by an invariable strategy, we compute the weights of concepts using the area under score curve. The assigned weights are incorporated into the confidence score vector statistically to better characterize the event-concept correlation. Our algorithm is proved to be able to harness the related concepts discriminatively tailored for a target event. Extensive experiments are conducted on the challenging TRECVID event video datasets, which demonstrate the advantage of our approach over the state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.

Improving the Asymptotic Radiative Transfer Model to Better Characterize the Pure Snow Hyperspectral Bidirectional Reflectance

The asymptotic radiative transfer (ART) model has been widely used in snow remote sensing. However, the anisotropic effects of snow reflectance challenge this model because of its underestimation in the forward-scattering direction. To exhibit these strong scattering properties of the snow surface, a microfacet specular kernel has been supplemented with the ART model (hereinafter named the ARTS model). In this study, we propose a method of multiplying by a correction term for improving the ART model (hereinafter named the ARTF model). We validate the performance of the ARTF model using various data sources. Our results demonstrate that: 1) the ARTF model has higher accuracy in characterizing snow bidirectional signatures, with R-2 and root mean square error (RMSE) values in the ranges from 0.722 to 0.990 and 0.007 to 0.041, respectively, than the ART (R-2 = 0.507-0.802 and RMSE = 0.038-0.088) and ARTS (R-2 = 0.686-0.962 and RMSE = 0.021-0.044) models, especially in the long-wave near-infrared region and 2) the ARTF model can effectively represent snow hyperspectral reflectance, while the ART and ARTS models significantly underestimate snow reflectance in the visible and shortwave near-infrared region. The R-2 values of these three models reach similar to 0.99, and the RMSE values of the ARTF model range from 0.012 to 0.024, which are smaller than those of the ART (RMSE = 0.021-0.061) and ARTS (RMSE = 0.021-0.049) models. These results demonstrate that the ARTF model is better than the ART and ARTS models for characterizing snow hyperspectral bidirectional reflectance.

Mutation with Local Searching and Elite Inheritance Mechanism in Multi-Objective Optimization Algorithm: A Case Study in Software Product Line

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail.

Molecular and functional characterization of an evolutionarily conserved CREB-binding protein in the Lymnaea CNS

In eukaryotes, CREB-binding protein (CBP), a coactivator of CREB, functions both as a platform for recruiting other components of the transcriptional machinery and as a histone acetyltransferase (HAT) that alters chromatin structure. We previously showed that the transcriptional activity of cAMP-responsive element binding protein (CREB) plays a crucial role in neuronal plasticity in the pond snail Lymnaea stagnalis. However, there is no information on the molecular structure and HAT activity of CBP in the Lymnaea central nervous system (CNS), hindering an investigation of its postulated role in long-term memory (LTM). Here, we characterize the Lymnaea CBP (LymCBP) gene and identify a conserved domain of LymCBP as a functional HAT. Like CBPs of other species, LymCBP possesses functional domains, such as the KIX domain, which is essential for interaction with CREB and was shown to regulate LTM. In-situ hybridization showed that the staining patterns of LymCBP mRNA in CNS are very similar to those of Lymnaea CREB1. A particularly strong LymCBP mRNA signal was observed in the cerebral giant cell (CGC), an identified extrinsic modulatory interneuron of the feeding circuit, the key to both appetitive and aversive LTM for taste. Biochemical experiments using the recombinant protein of the LymCBP HAT domain showed that its enzymatic activity was blocked by classical HAT inhibitors. Preincubation of the CNS with such inhibitors blocked cAMP-induced synaptic facilitation between the CGC and an identified follower motoneuron of the feeding system. Taken together, our findings suggest a role for the HAT activity of LymCBP in synaptic plasticity in the feeding circuitry.

An Integrated Approach for Effective Injection Vulnerability Analysis of Web Applications Through Security Slicing and Hybrid Constraint Solving

Malicious users can attack Web applications by exploiting injection vulnerabilities in the source code. This work addresses the challenge of detecting injection vulnerabilities in the server-side code of Java Web applications in a scalable and effective way. We propose an integrated approach that seamlessly combines security slicing with hybrid constraint solving; the latter orchestrates automata-based solving with meta-heuristic search. We use static analysis to extract minimal program slices relevant to security from Web programs and to generate attack conditions. We then apply hybrid constraint solving to determine the satisfiability of attack conditions and thus detect vulnerabilities. The experimental results, using a benchmark comprising a set of diverse and representative Web applications/services as well as security benchmark applications, show that our approach (implemented in the JOACO tool) is significantly more effective at detecting injection vulnerabilities than state-of-the-art approaches, achieving 98 percent recall, without producing any false alarm. We also compared the constraint solving module of our approach with state-of-the-art constraint solvers, using six different benchmark suites; our approach correctly solved the highest number of constraints (665 out of 672), without producing any incorrect result, and was the one with the least number of time-out/failing cases. In both scenarios, the execution time was practically acceptable, given the offline nature of vulnerability detection.

Beyond Bag-of-Words: combining generative and discriminative models for scene categorization

This paper proposes an efficient framework for scene categorization by combining generative model and discriminative model. A state-of-the-art approach for scene categorization is the Bag-of-Words (BoW) framework. However, there exist many categories in scenes. Generally when a new category is considered, the codebook in BoW framework needs to be re-generated, which will involve exhaustive computation. In view of this, this paper tries to address the issue by designing a new framework with good scalability. When an additional category is considered, much lower computational cost is needed while the resulting image signatures are still discriminative. The image signatures for training discriminative model are carefully designed based on the generative model. The soft relevance value of the extracted image signatures are estimated by image signature space modeling and are incorporated in Fuzzy Support Vector Machine (FSVM). The effectiveness of the proposed method is validated on UIUC Scene-15 dataset and NTU-25 dataset, and it is shown to outperform other state-of-the-art approaches for scene categorization.

Regularizing the Deepsurv Network Using Projection Loss for Medical Risk Assessment

State-of-the-art deep survival prediction approaches expand network parameters to accommodate performance over a fine discretization of output time. For medical applications where data are limited, the regression-based Deepsurv approach is more advantageous because its continuous output design limits unnecessary network parameters. Despite the practical advantage, the typical network lacks control over the feature distribution causing the network to be more prone to noisy information and occasional poor prediction performance. We propose a novel projection loss as a regularizing objective to improve the time-to-event Deepsurv model. The loss formulation maximizes the lower bound of the multiple-correlation coefficient between the network's features and the desired hazard value. Reducing the loss also theoretically lowers the upper bound on the likelihood of discordant pair and improves C-index performance. We observe superior performances and robustness of regularized Deepsurv over many state-of-the-art approaches in our experiments with five public medical datasets and two cross-cohort validation tasks.

Constrained Gaussian mixture model framework for automatic segmentation of MR brain images

An automated algorithm for tissue segmentation of noisy, low-contrast magnetic resonance (MR) images of the brain is presented. A mixture model composed of a large number of Gaussians is used to represent the brain image. Each tissue is represented by a large number of Gaussian components to capture the complex tissue spatial layout. The intensity of a tissue is considered a global feature and is incorporated into the model through tying of all the related Gaussian parameters. The expectation-maximization (EM) algorithm is utilized to learn the parameter-tied, constrained Gaussian mixture model. An elaborate initialization scheme is suggested to link the set of Gaussians per tissue type, such that each Gaussian in the set has similar intensity characteristics with minimal overlapping spatial supports. Segmentation of the brain image is achieved by the affiliation of each voxel to the component of the model that maximized the a posteriori probability. The presented algorithm is used to segment three-dimensional, T1-weighted, simulated and real MR images of the brain into three different tissues, under varying noise conditions. Results are compared with state-of-the-art algorithms in the literature. The algorithm does not use an atlas for initialization or parameter learning. Registration processes are therefore not required and the applicability of the framework can be extended to diseased brains and neonatal brains.

A Unifying Approach to Moment-Based Shape Orientation and Symmetry Classification

In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods.

ART FOR CHANGE: Transformative learning and youth empowerment in a changing climate

Young people represent a powerful force for social change, and they have an important role to play in climate change responses. However, empowering young people to be "systems changers" is not straightforward. It is particularly challenging within educational systems that prioritize instrumental learning over critical thinking and creative actions. History has shown that by creating novel spaces for reflexivity and experimentation, the arts have played a role in shifting mindsets and opening up new political horizons. In this paper, we explore the role of art as a driver for societal transformation in a changing climate and consider how an experiment with change can facilitate reflection on relationships between individual change and systems change. Following a review of the literature on transformations, transformative learning and the role of art, we describe an experiment with change carried out with students at an Art High School in Lisbon, Portugal, which involved choosing one sustainable behavior and adopting it for 30 days. A transformative program encouraged regular reflection and group discussions. During the experiment, students started developing an art project about his or her experience with change. The results show that a transformative learning approach that engages students with art can support critical thinking and climate change awareness, new perspectives and a sense of empowerment. Experiential, arts-based approaches also have the potential to create direct and indirect effects beyond the involved participants. We conclude that climate-related art projects can serve as more than a form of science communication. They represent a process of opening up imaginative spaces where audiences can move more freely and reconsider the role of humans as responsible beings with agency and a stake in sustainability transformations.

Image Recovery based on Local and Nonlocal Regularizations

Recently, a nonlocal low-rank regularization based compressive sensing approach (NLR) which exploits structured sparsity of similar patches has shown the state-of-the-art performance in image recovery. However, NLR cannot efficiently preserve local structures because it ignores the relationship between pixels. In addition, the surrogate logdet function used in NLR cannot well approximate the rank. In this paper, a novel approach based on local and nonlocal regularizations toward exploiting the sparse-gradient property and nonlocal low-rank property (SGLR) has been proposed. Weighted schatten-p norm and l (q) norm have been used as better non-convex surrogate functions for the rank and l0 norm. In addition, an efficient iterative algorithm is developed to solve the resulting recovery problem. The experimental results have demonstrated that SGLR outperforms existing state-of-the-art CS algorithms.

Internet of Things: State-of-the-art, Computing Paradigms and Reference Architectures

The Internet of Things (IoT) makes it possible to connect objects or things to the internet, with the purpose of collecting data and controlling processes or machines remotely. IoT enables the physical world to be integrated into the digital world in order to optimize time, save costs and facilitate human labor. An IoT system comprises a rich ecosystem of elements that make up its value chain, which includes a computational and communication architecture or model, components and technologies. IoT has evolved rapidly, producing an exuberant scientific literature. This document presents the state of the art of IoT, with updated sources, that guides the reader, who is entering the world of IoT, to have a starting point for future research. In addition to the review of IoT architectures, components of the IoT ecosystem, computational paradigms and, security and governance aspects. Our main contribution is focused on the analysis of the Middleware layer in IoT architectures, oriented to the storage and processing of data.

A non-parametric approach to extending generic binary classifiers for multi-classification

Ensemble methods, which combine generic binary classifier scores to generate a multi-classification output, are commonly used in state-of-the-art computer vision and pattern recognition systems that rely on multi-classification. In particular, we consider the one-vs-one decomposition of the multi-class problem, where binary classifier models are trained to discriminate every class pair. We describe a robust multi-classification pipeline, which at a high level involves projecting binary classifier scores into compact orthogonal subspaces, followed by a non-linear probabilistic multi-classification step, using Kernel Density Estimation (KDE). We compare our approach against state-of-the-art ensemble methods (DCS, DRCW) on 16 multi-class datasets. We also compare against the most commonly used ensemble methods (VOTE, NEST) on 6 real-world computer vision datasets. Finally, we measure the statistical significance of our approach using non-parametric tests. Experimental results show that our approach gives a statistically significant improvement in multi-classification performance over state-of-the-art. (C) 2016 Elsevier Ltd. All rights reserved.

A role for axon-glial interactions and Netrin-G1 signaling in the formation of low-threshold mechanoreceptor end organs

Low-threshold mechanoreceptors (LTMRs) and their cutaneous end organs convert light mechanical forces acting on the skin into electrical signals that propagate to the central nervous system. In mouse hairy skin, hair follicle-associated longitudinal lanceolate complexes, which are end organs comprising LTMR axonal endings that intimately associate with terminal Schwann cell (TSC) processes, mediate LTMR responses to hair deflection and skin indentation. Here, we characterized developmental steps leading to the formation of Aβ rapidly adapting (RA)-LTMR and Aδ-LTMR lanceolate complexes. During early postnatal development, Aβ RA-LTMRs and Aδ-LTMRs extend and prune cutaneous axonal branches in close association with nascent TSC processes. Netrin-G1 is expressed in these developing Aβ RA-LTMR and Aδ-LTMR lanceolate endings, and Ntng1 ablation experiments indicate that Netrin-G1 functions in sensory neurons to promote lanceolate ending elaboration around hair follicles. The Netrin-G ligand (NGL-1), encoded by Lrrc4c, is expressed in TSCs, and ablation of Lrrc4c partially phenocopied the lanceolate complex deficits observed in Ntng1 mutants. Moreover, NGL-1-Netrin-G1 signaling is a general mediator of LTMR end organ formation across diverse tissue types demonstrated by the fact that Aβ RA-LTMR endings associated with Meissner corpuscles and Pacinian corpuscles are also compromised in the Ntng1 and Lrrc4c mutant mice. Thus, axon-glia interactions, mediated in part by NGL-1-Netrin-G1 signaling, promote LTMR end organ formation.

Iterative Shrinkage Algorithm for Patch-Smoothness Regularized Medical Image Recovery

We introduce a fast iterative shrinkage algorithm for patch-smoothness regularization of inverse problems in medical imaging. This approach is enabled by the reformulation of current non-local regularization schemes as an alternating algorithm to minimize a global criterion. The proposed algorithm alternates between evaluating the denoised inter-patch differences by shrinkage and computing an image that is consistent with the denoised inter-patch differences and measured data. We derive analytical shrinkage rules for several penalties that are relevant in non-local regularization. The redundancy in patch comparisons used to evaluate the shrinkage steps are exploited using convolution operations. The resulting algorithm is observed to be considerably faster than current alternating non-local algorithms. The proposed scheme is applicable to a large class of inverse problems including deblurring, denoising, and Fourier inversion. The comparisons of the proposed scheme with state-of-the-art regularization schemes in the context of recovering images from undersampled Fourier measurements demonstrate a considerable reduction in alias artifacts and preservation of edges.

Tabrizicola rongguiensis sp. nov., isolated from the sediment of a river in Ronggui, Foshan city, China

A novel Gram-negative, aerobic, non-spore-forming, non-motile and rod-shaped bacterium, designated J26T, was isolated from the sediment of a river in Ronggui, Foshan city, China. Strain J26T grew optimally at 0 % (w/v) NaCl, pH 6.5-7.5, and 30 °C, and it formed milky white irregular colonies on Reasoner's 2A agar medium. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain J26T had the highest similarity to Tabrizicola aquatica RCRI19T (97.1 %) and formed a distinct clade in the genus Tabrizicola. Cellular components of J26T supported this strain as a member of the genus Tabrizicola. The predominant fatty acids were C18 : 1 ω7c, C18 : 1 ω7c-11 methyl and C16 : 0. Polar lipids were diphosphatidylglycerol, phosphatidylglycerol and phosphorylethanolamine. Ubiquinone Q-10 was the major respiratory quinone, and the DNA G+C content was 64.2 mol%. However, low 16S rRNA gene sequence similarity and average nucleotide identity (73.56 % for ANIb between strain J26T with RCRI19T) demonstrated that strain J26T should be assigned to a novel species. Moreover, the differences between J26T and RCRI19T in terms of physiological and biochemical properties, such as carbon, nitrogen and sulphur metabolism, further supported that J26T represents a novel species, for which the name Tabrizicola rongguiensis sp. nov. is proposed. The type strain is J26T (=GDMCC 1.2843T=KCTC 92112T).

Detection and Quantification of Antimicrobial-Resistant Cells in Aquatic Environments by Bioorthogonal Noncanonical Amino Acid Tagging

Aquatic environments are important reservoirs of antibiotic wastes, antibiotic resistance genes, and bacteria, enabling the persistence and proliferation of antibiotic resistance in different bacterial populations. To prevent the spread of antibiotic resistance, effective approaches to detect antimicrobial susceptibility in aquatic environments are highly desired. In this work, we adopt a metabolism-based bioorthogonal noncanonical amino acid tagging (BONCAT) method to detect, visualize, and quantify active antimicrobial-resistant bacteria in water samples by exploiting the differences in bacterial metabolic responses to antibiotics. The BONCAT approach can be applied to rapidly detect bacterial resistance to multiple antibiotics within 20 min of incubation, regardless of whether they act on proteins or DNA. In addition, the combination of BONCAT with the microscope enables the intuitive characterization of antibiotic-resistant bacteria in mixed systems at single-cell resolution. Furthermore, BONCAT coupled with flow cytometry exhibits good performance in determining bacterial resistance ratios to chloramphenicol and population heterogeneity in hospital wastewater samples. In addition, this approach is also effective in detecting antibiotic-resistant bacteria in natural water samples. Therefore, such a simple, fast, and efficient BONCAT-based approach will be valuable in monitoring the increase and spread of antibiotic resistance within natural and engineered aquatic environments.

Detection of 18-methyl steroids: Case report on a forensic urine sample and corresponding dietary supplements

The detection of a putative 18-methyl-19-nortestosterone metabolite in a forensic bodybuilder's urine sample collected as part of a criminal proceeding has triggered a follow-up investigation. Four different dietary supplements in the possession of the suspect were examined with regard to possible precursor steroids. This led to the detection of the declared ingredient methoxydienone, which was confirmed by both, GC-MSMS and LC-HRMSMS. As neither 18-methyl-testosterone, nor 18-methyl-19-nortestosterone were detectable in the supplements, the possibility that the metabolite originates from methoxydienone was investigated. For this purpose, the metabolic fate of methoxydienone was studied in vitro using human HepG2 cells and in vivo by a single oral administration. While the 18-methyl-19-nortestosterone metabolite was not generated by HepG2 cells incubated with methoxydienone, it was observed in the urine samples collected at 2, 6, 10 and 24 h after methoxydienone administration. Moreover, the potential binding of methoxydienone as ligand to the human androgen receptor was modelled in silico in comparison with 18-methylnandrolone, for which androgen receptor activation had been shown in an in vitro approach before. In conclusion, we could ascribe the presence of the 18-methyl-19-nortestosterone metabolite in a forensic urine sample to originate from methoxydienone present in dietary supplements. Methoxydienone was observed to slowly degrade by demethylation of the methoxy substituent in liquid solutions. While no compound-specific intermediates were identified that allowed differentiation from other 18-methyl steroids, the 18-methyl-19-nortestosterone metabolite proved to be a suitable marker for reliable detection in doping analysis.

Molecular Mechanisms Involved in Pseudomonas aeruginosa Bacteremia

Bloodstream infections (BSI) with Pseudomonas aeruginosa account for 8.5% of all BSIs, their mortality rate, at about 40%, is the highest among causative agents. For this reason and due to its intrinsic and acquired resistance to antibiotics, P. aeruginosa represents a threat to public health systems. From the primary site of infection, often the urinary and respiratory tracts, P. aeruginosa uses its arsenal of virulence factors to cross both epithelial and endothelial barriers, ultimately reaching the bloodstream. In this chapter, we review the main steps involved in invasion and migration of P. aeruginosa into blood vessels, and the molecular mechanisms governing bacterial survival in blood. We also review the lifestyle of P. aeruginosa "on" and "in" host cells. In the context of genomic and phenotypic diversity of laboratory strains and clinical isolates, we underline the need for more standardized and robust methods applied to host-pathogen interaction studies, using several representative strains from distinct phylogenetic groups before drawing general conclusions. Finally, our literature survey reveals a need for further studies to complete our comprehension of the complex interplay between P. aeruginosa and the immune system in the blood, specifically in relation to the complement system cascade(s) and the Membrane Attack Complex (MAC), which play crucial roles in counteracting P. aeruginosa BSI.

A Qualitative Exploration of the Role of Antiretroviral Therapy on Chinese Rural Life

Objective To explore factors influencing the quality of life of people living with HIV/AIDS (PLHA) and receiving antiretroviral therapy (ART) in rural China. Methods In-depth interviews with 20 PLHA were conducted in March 1999. Participants were recruited from the USAID-funded Longitudinal Enhanced Evaluation of ART Project, which tracks a cohort of eligible PLHA receiving treatment at five collaborating treatment centers in Guangxi Autonomous Region, China. An interview guide (semi-structured with open-ended questions) was developed to provide a qualitative examination of the quality of life of PLHA. Results Participants identified that ART affects physical health, including the experience of pain, side effects, and opportunistic infections. ART imposes lifestyle constraints such as reduced mobility due to drug procurement, and social restrictions due to the daily drug regimen. Participants discussed the psychological burden of taking drugs, and the fear of accidental transmission to others, or having their disease status known by others, as well as optimistic feelings about their future due to ART. ART poses a significant drain on individual's economic resources due to related medical costs, and inability to seek seasonal migrant labor due to reduced mobility. Conclusion While China's national free ART program improved the physical health of those surveyed, their social and economic needs were left unaddressed. To improve life outcomes for PLHA, and by extension, the wider Chinese population, quality of life measures should be included when evaluating the success of the ART program.

A Unique Appliance for Median Facial Cleft Management With Sleep Apnea due to Undeveloped Nasal Septum: A Rare Case Report

The role of a prosthodontist in the management of facial cleft patients is the restoration of feeding, respiration, facial harmony, dental harmony and phonation. This case report presents the fabrication of an appliance for a pediatric patient with congenital median facial cleft who had sleep apnea due to the absence of nasal septum, which is a rare condition, with a unique method that fulfilled the patient's needs and improved quality of life. The patient also had median cleft lip and premaxilla along with hypertelorism.

Comparing State-of-the-Art and Emerging Augmented Reality Interfaces for Autonomous Vehicle-to-Pedestrian Communication

Providing pedestrians and other vulnerable road users with a clear indication about a fully autonomous vehicle status and intentions is crucial to make them coexist. In the last few years, a variety of external interfaces have been proposed, leveraging different paradigms and technologies including vehicle-mounted devices (like LED panels), short-range on-road projections, and road infrastructure interfaces (e.g., special asphalts with embedded displays). These designs were experimented in different settings, using mockups, specially prepared vehicles, or virtual environments, with heterogeneous evaluation metrics. Promising interfaces based on Augmented Reality (AR) have been proposed too, but their usability and effectiveness have not been tested yet. This paper aims to complement such body of literature by presenting a comparison of state-of-the-art interfaces and new designs under common conditions. To this aim, an immersive Virtual Reality-based simulation was developed, recreating a well-known scenario represented by pedestrians crossing in urban environments under non-regulated conditions. A user study was then performed to investigate the various dimensions of vehicle-to-pedestrian interaction leveraging objective and subjective metrics. Even though no interface clearly stood out over all the considered dimensions, one of the AR designs achieved state-of-the-art results in terms of safety and trust, at the cost of higher cognitive effort and lower intuitiveness compared to LED panels showing anthropomorphic features. Together with rankings on the various dimensions, indications about advantages and drawbacks of the various alternatives that emerged from this study could provide important information for next developments in the field.

A hybrid machine learning method for procurement risk assessment of non-ferrous metals for manufacturing firms

With the growing complexity of manufacturing systems nowadays, the effective assessment of important risk factors inherent in the manufacturing process is crucial for the stability and reliability of such complex systems. Thus, this article proposes a data-driven approach using the state-of-art machine learning techniques to assess and forecast the procurement risks of non-ferrous metals associated with complex manufacturing systems. A variety of state-of-art machine learning models including ANN, LSTM, BLSTM, GARCH, as well as their combinations which compose the proposed hybrid models, are deployed and analyzed. The testing results show that the proposed hybrid machine learning method can forecast the price uncertainty in procurement and effectively evaluate the procurement risk in a precautious manner. Moreover, it is shown that the hybrid model that combines GARCH, ANN, and LSTM significantly improves the forecasting results. Besides, the optimal choice of the network configurations in the hybrid model is also analyzed via a series of sensitivity analyses. This research can serve as a useful reference for the effective assessment and control of procurement risk for manufacturing firms.

Design for invention: annotation of functional geometry interaction for representing novel working principles

In some mechanical engineering devices the novelty or inventive step of a patented design relies heavily upon how geometric features contribute to device functions. Communicating the functional interactions between geometric features in existing patented designs may increase a designer's awareness of the prior art and thereby avoid conflict with their emerging design. This paper shows how functional representations of geometry interactions can be developed from patent claims to produce novel semantic graphical and text annotations of patent drawings. The approach provides a quick and accurate means for the designer to understand the patent that is well suited to the designer's natural way of understanding the device. Through several example application cases we show the application of a detailed representation of functional geometry interactions that captures the working principle of familiar mechanical engineering devices described in patents. A computer tool that is being developed to assist the designer to understand prior art is also described.

Preterm Delivery and Increased Risk of Recurrent Cardiovascular Events in Australian Women

Background: Women with a history of preterm delivery (PTD) have significantly increased risk of experiencing cardiovascular disease (CVD) later in life. However, the risk of long-term recurrence of CVD in this population remains unknown. Materials and Methods: The study was based on a cohort of Victorian women who had a singleton birth between 1999 and 2008. The primary outcome was a CVD event resulting in an Emergency Department visit/hospitalization or death. Women who do not have a PTD during the study period were adopted as the comparator. The Andersen and Gill model, which generalizes the Cox proportional hazards regression model, was used for the analysis of recurrent CVD, while adjusting for covariates, including indigenous status. Results: After excluding cases not meeting the inclusion criteria, 34,128 Victoria women who had a history of PTD and 374,538 women who had deliveries at terms were analyzed. A history of PTD was shown to be associated with significantly increased risk of recurrent CVD, while adjusting for all covariates, including indigenous status, with an adjusted hazard ratio (aHR) of 1.70 (95% confidence interval [CI]: 1.54-1.86, p < 0.0001). Aboriginal and Torres Strait Islander women had substantially increased risk of experiencing recurrent CVD after birth over their lifetime (aHR: 3.22, 95% CI: 2.39-4.35, p < 0.0001). Conclusions: Recognizing PTD as a nontraditional risk factor of CVD may play a role in the formulation of care plans for primary and secondary CVD prevention in women with such a history.

A quick test method for predicting the adsorption of organic micropollutants on activated carbon

Controlling the contamination of water cycles with organic micropollutants (OMPs) has been targeted in many regions. Adsorption with activated carbon is an effective technology to remove OMPs from different water matrices. To efficiently design or operate the adsorption process, the adsorption of OMPs should be properly assessed, usually with time-consuming batch adsorption tests and sophisticated analyses. In this study, a quick adsorption test method has been developed by loading powdered activated carbon (PAC) into a syringe filter which can be used subsequently to filtrate the water sample in short time (<60 s). Treated wastewater was applied to compare the quick test method and conventional batch test regarding the adsorption of 14 frequently detected OMPs, the abatement of UV254, and changes in fractions of dissolved organic matter (DOM). Similar adsorption patterns of individual OMPs, total OMPs, and DOM fractions was found with two methods. UV254 can predict the removal of total OMPs and most individual OMPs in both methods. Both the abatement of UV254 or the removal of OMPs determined in the quick test led to a highly accurate prediction of OMP adsorption in the conventional adsorption tests. The novel quick test method thus could help operators and researchers quickly monitor the adsorption capacity of PAC products.

Chronic lipopolysaccharide impairs motivation when delivered to the ventricles, but not when delivered peripherally in male rats

Increased neuroinflammation relative to controls is observed in major depression. Moreover, depressive disorders are significantly elevated in conditions which increase neuroinflammation (e.g., brain injury, Parkinson's disease, Alzheimer's disease). To better understand the relationship between neuroinflammation and depression, additional research is needed. The current set of studies made use of the progressive ratio (PR) task in male rats, a stable measure of motivation which can be evaluated daily and thus is ideally suited for examining a potential role for chronic neuroinflammation in depressive-like behavior. Lipopolysaccharide (LPS) was used to induce an inflammatory response. Experiment 1 confirmed prior acute LPS administration experiments for sensitivity of the PR task, with a large effect at 2 mg/kg, a partial effect at 1 mg/kg, and no effect at 0.5 mg/kg. Experiment 2 evaluated a dose-response of continuous s.c. LPS infusion but found no significant elevation in brain cytokines after 14 days at any doses of 0.1, 0.5, 1, or 2 mg/kg/week. Experiment 3 assessed motivation during continuous s.c. infusion of a large 5 mg/kg/week LPS dose and found no significant impairments in motivation, but transient decreases in rates of lever pressing (i.e., only motoric deficits). Experiment 4 measured motivation during continuous ICV infusion of 10.5 μg/kg/week LPS and found significantly decreased motivation without changes to rates of lever pressing (i.e., only motivational deficits). Together these results suggest that the PR task is efficient for evaluating models of chronic inflammation, and that the adaptive response to chronic LPS exposure, even when delivered centrally, may necessitate alternative strategies for generating long-term neuroinflammation.

Freeze-resistant, rapidly polymerizable, ionic conductive hydrogel induced by Deep Eutectic Solvent (DES) after lignocellulose pretreatment for flexible sensors

In this paper, ternary DES (choline chloride, glycerol, Lewis acid) was used to pretreat lignocellulose, and the DES solution with dissolved lignin was utilized as the medium of hydrogel to prepare DES-based polyacrylic acid/polyvinyl alcohol (PAA/PVA) double network hydrogels with great mechanical properties, self-adhesion, and high electrochemical sensitivity. The entanglement of PAA with PVA chains, the covalent linkage between Al3+ and PAA chains and the metal phenol network (MPN) formed by Al3+ and lignin improved the mechanical properties of the hydrogels, enabling the prepared hydrogels to achieve a tensile strain of 400 % and an elongation at break of 150 kPa. Secondly, the introduction of DES solution endowed the hydrogel with excellent electrical sensing ability and anti-freezing property, so that the hydrogel still maintains good flexibility and ionic conductivity at -20 °C. It was also found that the above hydrogel can achieve a high gauge factor of 4.19 as a flexible sensor, which provides scientific ideas for the application of the pretreated DES solution in the field of flexible wearable.

Linking hydrological, hydraulic and water quality models for river water environmental capacity assessment

The water environment of a river network can self-clean to a certain extent; however, when the wastewater discharge load exceeds a certain threshold, the balance of nature is disrupted, leading to water pollution. This emphasises the urgent need to evaluate river water environmental capacity (RWEC) as a necessary parameter for sustainable development. However, to quantify the RWEC, it is important to estimate the hydrological and hydrodynamic factors in the basin, leading to the linking of these models. The present investigation aims to propose an integrated framework, named RWEC, consisting of hydrological and hydrodynamic models, a database system, and GIS to evaluate the water environmental carrying capacity of the selected river network. The rain - runoff (RR), hydrodynamic (HD), ecological (ECO), and RWEC models were used. Groups of data, including meteorology, hydrology, and the environment, combined with topographic data and waste sources, were applied. Groups of models and data were integrated into a seven-step framework to calculate the RWEC. The case study is a basin in Binh Duong Province, Vietnam and four pollutants were selected: NH4+, BOD5, NO3-, PO43-. The flow and water quality factors in the river basin in the study area were measured based on hydraulic models, and the water quality was calibrated. The role of hydrological, hydraulic, and water quality models in the RWEC calculation was clarified. According to the baseline and forecast scenarios, the calculation of the RWEC for the scenarios was performed. In the baseline scenario, RWECNH4+ is in the range (-283, -22) kg/day, RWECBOD5 ranges from (143, 3126) kg/day, RWECNO3- is in the range (-778, 2166) kg/day, and RWECPO43- is in the range (-31, 46) kg/day. The dependence of RWEC on environmental factors, self-cleaning factors, and the difference between the baseline and forecast scenarios were clarified.

MPC-Based Delay-Aware Fountain Codes for Real-Time Video Communication

With the prevalence of smart mobile devices and surveillance cameras, the traffic load within the Internet of Things (IoT) has shifted away from nonmultimedia data to multimedia traffics, particularly, the video content. However, the explosive demand for real-time video communication over wireless networks in IoT is constantly challenging both video coding and communication research communities. The state-of-the-art answer to this challenge is sliding-window-based delay-aware fountain (DAF) codes, which combine the channel-adaptive feature in rateless coding and the delay-aware feature in video coding. However, the high computational cost and large delay make it impractical for real-time streaming. To address this issue, we integrate the model predictive control (MPC) technique into DAF codes, so the complexity is lowered to an affordable level so that real-time video encoding is supported. Two schemes are developed in this paper: 1) DAF-S, the smallhorizon DAF codes and 2) DAF-O, the MPC-based DAF using video bit rate prediction. The advantages of both designs are validated through theoretical analysis and comprehensive experiments. The results of simulation experiments show that the decoding ratio of DAF-S is close to the global optimum in DAF codes, and higher than the other existing schemes; DAF-O outperforms the state-of-the-art real-time video communication algorithms.

Automatic Source Code Summarization of Context for Java Methods

Source code summarization is the task of creating readable summaries that describe the functionality of software. Source code summarization is a critical component of documentation generation, for example as Javadocs formed from short paragraphs attached to each method in a Java program. At present, a majority of source code summarization is manual, in that the paragraphs are written by human experts. However, new automated technologies are becoming feasible. These automated techniques have been shown to be effective in select situations, though a key weakness is that they do not explain the source code's context. That is, they can describe the behavior of a Java method, but not why the method exists or what role it plays in the software. In this paper, we propose a source code summarization technique that writes English descriptions of Java methods by analyzing how those methods are invoked. We then performed two user studies to evaluate our approach. First, we compared our generated summaries to summaries written manually by experts. Then, we compared our summaries to summaries written by a state-of-the-art automatic summarization tool. We found that while our approach does not reach the quality of human-written summaries, we do improve over the state-of-the-art summarization tool in several dimensions by a statistically-significant margin.

Characterization of the electrical performance of different signal via geometries

Several signal via geometries are analyzed for across and through transmission by varying the drill-hole diameter, ratio of the pad and antipad diameters to drill-hole diameter, and number of ground planes. Time-and frequency-domain results showing impedance-, insertion-, and return-loss trends, which are useful for designing via geometries for high-speed transmission, art obtained. (C) 2005 Wiley Periodicals. Inc.

Global profiling of arginine dimethylation in regulating protein phase separation by a steric effect-based chemical-enrichment method

Protein arginine methylation plays an important role in regulating protein functions in different cellular processes, and its dysregulation may lead to a variety of human diseases. Recently, arginine methylation was found to be involved in modulating protein liquid-liquid phase separation (LLPS), which drives the formation of different membraneless organelles (MLOs). Here, we developed a steric effect-based chemical-enrichment method (SECEM) coupled with liquid chromatography-tandem mass spectrometry to analyze arginine dimethylation (DMA) at the proteome level. We revealed by SECEM that, in mammalian cells, the DMA sites occurring in the RG/RGG motifs are preferentially enriched within the proteins identified in different MLOs, especially stress granules (SGs). Notably, global decrease of protein arginine methylation severely impairs the dynamic assembly and disassembly of SGs. By further profiling the dynamic change of DMA upon SG formation by SECEM, we identified that the most dramatic change of DMA occurs at multiple sites of RG/RGG-rich regions from several key SG-contained proteins, including G3BP1, FUS, hnRNPA1, and KHDRBS1. Moreover, both in vitro arginine methylation and mutation of the identified DMA sites significantly impair LLPS capability of the four different RG/RGG-rich regions. Overall, we provide a global profiling of the dynamic changes of protein DMA in the mammalian cells under different stress conditions by SECEM and reveal the important role of DMA in regulating protein LLPS and SG dynamics.

Age-friendly care in the Veterans Health Administration: Past, present, and future

The Veterans Health Administration (VHA) has long recognized the need for age-friendly care. VHA leadership anticipated the impact of aging World War II veterans on VA healthcare systems and in 1975 developed Geriatric Research, Education, and Clinical Centers (GRECCs) to meet this need. GRECCs catalyzed a series of innovations in geriatric models of care that span the continuum of care, most of which endure. These innovative care models also contributed to the evidence base supporting the present-day Age-Friendly Health Systems movement, with which VHA is inherently aligned. As both a provider of and payor for care, VHA is strongly incentivized to promote coordination across the continuum of care, with resultant cost savings. VHA is also a major contributor to developing the workforce that is essential for the provision of age-friendly care. As VHA continues to develop and refine innovative geriatric models of care, policymakers and non-VHA health care systems should look to VHA programs as exemplars for the development and implementation of age-friendly care.

A broad-spectrum integrative design for cancer prevention and therapy: The challenge ahead

Despite exciting advances in targeted therapies, high drug costs, marginal therapeutic benefits and notable toxicities are concerning aspects of today's cancer treatments. This special issue of Seminars in Cancer Biology proposes a broad-spectrum, integrative therapeutic model to complement targeted therapies. Based on extensive reviews of the cancer hallmarks, this model selects multiple high-priority targets for each hallmark, to be approached with combinations of low-toxicity, low-cost therapeutics, including phytochemicals, adapted to the well-known complexity and heterogeneity of malignancy. A global consortium of researchers has been assembled to advance this concept, which is especially relevant in an era of rapidly expanding capacity for genomic tumor analyses, alongside alarming growth in cancer morbidity and mortality in low- and middle-income nations.

Denormalization of visibilities for in-orbit calibration of interferometric radiometers

This paper reviews the relative calibration of an interferometric radiometer taking into account the experimental results of the first batch of receivers developed in the frame of the European Space Agency's Soil Moisture and Ocean Salinity mission. Measurements show state-of-the-art baseline performance as long as the system is capable of correcting the effect of orbital, temperature swing. A method to validate internal calibration during in-orbit deep-sky views and to correct linearity errors is also presented.

A DCT Approximation for Image Compression

An orthogonal approximation for the 8-point discrete cosine transform (DCT) is introduced. The proposed transformation matrix contains only zeros and ones; multiplications and bit-shift operations are absent. Close spectral behavior relative to the DCT was adopted as design criterion. The proposed algorithm is superior to the signed discrete cosine transform. It could also outperform state-of-the-art algorithms in low and high image compression scenarios, exhibiting at the same time a comparable computational complexity.

Putting the Radio in "Software-Defined Radio": Hardware Developments for Adaptable RF Systems

The prospects for and the state of the art of adaptable RF hardware are reviewed, focusing primarily on the traditional frequency planning bottleneck, the filtering stages. First, a case is made that even banded systems can be greatly impacted by a modest amount of tuning. This is done by showing the results of a traditional fixed system in an unlicensed band upgraded with a programmable front-end filter. Next, a system built specifically for wideband tuning is shown that enables band selection across the 20-MHz-6-GHz-band. Cooperative operation of multiple colocated nodes is enabled by high-quality pre-LNA filtering across the bands of operation. Future capabilities of adaptable systems are shown by reviewing the state of the art of adaptable systems, heading toward a field-programmable filter array in which a sea of resonators are dynamically interconnected to create a transfer function on demand. Additionally, a novel synthesis approach is highlighted in which multiple filters can cooperate gracefully without crossover issues between the bands. This approach allows for a vast number of filter states by turning on and off pass-bands without affecting the adjacent bands. The advancements in adaptable hardware will enable new classes of RF systems which much more efficiently utilize the spectrum.

Depth-Aware Object Tracking With a Conditional Variational Autoencoder

Object tracking is a fundamental task in computer vision and artificial intelligence. However, state-of-the-art object tracking approaches are still prone to failures and are imprecise when applied to challenging scenarios, and their results are generally confidence agnostic. An imprecise deterministic output with low confidence may lead to disastrous consequences and a lack of proof for subsequent operations and human interventions. Deep network training with ambiguous data or the noise inherent in observations (i.e., data uncertainty or aleatoric uncertainty) will result in inherent uncertainties in predictions. In this paper, we exploit probabilistic depth-aware object tracking with a conditional variational autoencoder (CVAE). First, we build a bridge between the Siamese network and the variational autoencoder conditioned with depth images and propose a novel multimodal Bayesian object tracking method. Second, our proposed method yields a complete probability distribution that enables the production of multiple plausible features. Third, the variational autoencoder conditioned by depth images encodes a low-dimensional latent space that conducts depth-aware tracking, which has obvious advantages for challenging tracking scenarios. Our proposed tracking method outperformed the state-of-the-art trackers on the VOT 2016, VOT 2018, and VOT 2019 datasets.

License Plate Detection via Information Maximization

License plate (LP) detection in the wild remains challenging due to the diversity of environmental conditions. Nevertheless, prior solutions have focused on controlled environments, such as when LP images frequently emerge as from an approximately frontal viewpoint and without scene text which might be mistaken for an LP. However, even for state-of-the-art object detectors, their detection performance is not satisfactory for real-world environments, suffering from various types of degradation. To solve these problems, we propose a novel end-to-end framework for robust LP detection, designed for such challenging settings. Our contribution is threefold: (1) A novel information-theoretic learning that takes advantage of a shared encoder, an LP detector and a scene text detector (excluding LP) simultaneously; (2) Localization refinement for generalizing the bounding box regression network to complement ambiguous detection results; (3) a large-scale, comprehensive dataset, LPST-110K, representing real-world unconstrained scenes including scene text annotations. Computational tests show that the proposed model outperforms other state-of-the-art methods on a variety of challenging datasets.

Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT

Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion online.(1) (1) https://github.com/viggin/DeepLesion_manual_test_set

ARTS, an AR Tourism System, for the Integration of 3D Scanning and Smartphone AR in Cultural Heritage Tourism and Pedagogy

Interactions between cultural heritage, tourism, and pedagogy deserve investigation in an as-built environment under a macro- or micro-perspective of urban fabric. The heritage site of Shih Yih Hall, Lukang, was explored. An Augmented Reality Tourism System (ARTS) was developed on a smartphone-based platform for a novel application scenario using 3D scans converted from a point cloud to a portable interaction size. ARTS comprises a real-time environment viewing module, a space-switching module, and an Augmented Reality (AR) guide graphic module. The system facilitates scenario initiations, projection and superimposition, annotation, and interface customization, with software tools developed using ARKit((R)) on the iPhone XS Max((R)). The three-way interaction between urban fabric, cultural heritage tourism, and pedagogy was made possible through background block-outs and an additive or selective display. The illustration of the full-scale experience of the smartphone app was made feasible for co-relating the cultural dependence of urban fabric on tourism. The great fidelity of 3D scans and AR scenes act as a pedagogical aid for students or tourists. A Post-Study System Usability Questionnaire (PSSUQ) evaluation verified the usefulness of ARTS.

Research Note: Intergenerational Transmission Is Not Sufficient for Positive Long-Term Population Growth

All leading long-term global population projections agree on continuing fertility decline, resulting in a rate of population size growth that will continue to decline toward zero and would eventually turn negative. However, scholarly and popular arguments have suggested that because fertility transmits intergenerationally (i.e., higher fertility parents tend to have higher fertility children) and is heterogeneous within a population, long-term population growth must eventually be positive, as high-fertility groups come to dominate the population. In this research note, we show that intergenerational transmission of fertility is not sufficient for positive long-term population growth, for empirical and theoretical reasons. First, because transmission is imperfect, the combination of transmission rates and fertility rates may be quantitatively insufficient for long-term population growth: higher fertility parents may nevertheless produce too few children who retain higher fertility preferences. Second, today even higher fertility subpopulations show declining fertility rates, which may eventually fall below replacement (and in some populations already are). Therefore, although different models of fertility transmission across generations reach different conclusions, depopulation is likely under any model if, in the future, even higher fertility subpopulations prefer and achieve below-replacement fertility. These results highlight the plausibility of long-term global depopulation and the importance of understanding the possible consequences of depopulation.

Complex community health and social care interventions - Which features lead to reductions in hospitalizations for ambulatory care sensitive conditions? A systematic literature review

Preventing hospitalizations due to ambulatory care sensitive conditions (ACSCs) is traditionally the responsibility of primary care. The determinants of ACSC hospitalizations, however, are not purely medical, but also influenced by other factors like patients' social and personal circumstances. Interventions that include or consist entirely of community health services and social care could potentially reduce the ACSC hospitalization rate. Comparisons of the features of successful interventions of this nature, however, are still lacking. We therefore conducted a systematic review of the literature to identify out-of-hospital interventions that (a) included aspects or consisted entirely of community health services and social care and (b) analyzed the ACSC hospitalization rate as an outcome measure. We identified papers reporting the results of 32 interventions and extracted structural and behavioral features to determine which of these were shared by most or all of the successful interventions. We found that all of the successful interventions included a primary care physician and provided care management. Moreover, most of the successful interventions were characterized by a high degree of interconnectedness between professional groups and provided care within so-called health care homes. We also identified a set of care coordination activities that were implemented in most of the successful interventions. Policy makers may wish to consider adopting these features when designing interventions that aim to reduce the ACSC hospitalization rate.

Reflect on emotional events from an observer's perspective: a meta-analysis of experimental studies

Self-distancing has been proposed as an emotion regulation strategy to reduce the duration and intensity of emotions. This meta-analysis synthesised 48 studies and 102 effect sizes examining the effects of self-distancing on emotion regulation. The results showed an overall significant, small effect of self-distancing in attenuating emotional responses (Hedges' g = -0.26, 95%CI: [-0.36, -0.15]). Moderator analyses highlighted the efficacy of one intervention feature: approach. Stronger effect was associated with the visual and verbal approach to process emotional events, in comparison to the visual only approach and the pronouns approach. The effectiveness of self-distancing was consistent across other intervention features (context, stimuli, time, emotional outcome) and individual characteristics (emotional vulnerability, age, culture). These findings suggest that self-distancing is effective in emotion regulation when people externalise and articulate thoughts through writing and talking. Practical implications were discussed in relation to the design of interventions to enhance emotional well-being.

Aortopulmonary window, aortic arch interruption, and anomalous origin of the right pulmonary artery in a neonate with Goldenhar syndrome

The combination of aortopulmonary window, interruption of the aortic arch, and anomalous origin of the right pulmonary artery from the ascending aorta is a rare and complex congenital cardiac malformation. Despite good prenatal care in our case, this cardiac anomaly was not detected prior to birth. Untreated infants who do not undergo surgical correction have a mortality rate of 70% in their first year, and 30% will die within the first 3 months of life.

A feature extraction based support vector machine model for rectal cancer T-stage prediction using MRI images

Accurate clinical cancer T-stage diagnosis is crucial for effective treatment. However, it is difficult, time-consuming, and laborious for physicians to recognize T-stage manually using rectal Magnetic Resonance Imaging (MRI) images. Machine learning methods have played important roles in medical image processing. With the goal of automatic rectal cancer T-stage prediction, we train the proposed Feature Extraction based Support Vector Machine (FE-SVM) model with the newly acquired dataset consisting of 147 patients' MRI images with primary rectal cancer. Our method adapts SVM as the training framework as SVM is effective enough for dealing with small datasets as opposed to state-of-the-art deep learning models. FE-SVM firstly extracts image similarity as an initial feature because the feature of image similarity can better reflect the differences among various types of MRI images, and the final 10-dimensional features are obtained by a 5-layers Autoencoder. To evaluate the performance of FE-SVM, we compared it with six benchmark models: CNN, Alexnet, Resnet18, Resnet50, Capsule Network, and Random Forest. FE-SVM outperforms these state-of-the-art models with significant evaluation scores.

A broadly conserved fungal alcohol oxidase (AOX) facilitates fungal invasion of plants

Alcohol oxidases (AOXs) are ecologically important enzymes that facilitate a number of plant-fungal interactions. Within Ascomycota they are primarily associated with methylotrophy, as a peroxisomal AOX catalysing the conversion of methanol to formaldehyde in methylotrophic yeast. In this study we demonstrate that AOX orthologues are phylogenetically conserved proteins that are common in the genomes of nonmethylotrophic, plant-associating fungi. Additionally, AOX orthologues are highly expressed during infection in a range of diverse pathosystems. To study the role of AOX in plant colonization, AOX knockout mutants were generated in the broad host range pathogen Sclerotinia sclerotiorum. Disease assays in soybean showed that these mutants had a significant virulence defect as evidenced by markedly reduced stem lesions and mortality rates. Chemical genomics suggested that SsAOX may function as an aromatic AOX, and growth assays demonstrated that ΔSsAOX is incapable of properly utilizing plant extract as a nutrient source. Profiling of known aromatic alcohols pointed towards the monolignol coniferyl alcohol (CA) as a possible substrate for SsAOX. As CA and other monolignols are ubiquitous among land plants, the presence of highly conserved AOX orthologues throughout Ascomycota implies that this is a broadly conserved protein used by ascomycete fungi during plant colonization.

SMT Solder Joint Inspection via a Novel Cascaded Convolutional Neural Network

Due to the excellent self-learning ability of deep learning, we propose a novel deep-learning-based method to inspect surface-mount technology (SMT) solder joints in this paper. In contrast to the state-of-the-art learning-based methods in which low-level features are extracted before learning, our method directly implements the inspection task without low-level feature extraction, which is based on a novel cascaded convolutional neural network (CNN). Three kinds of CNNs with different network parameters compose the proposed cascaded CNN. First, one kind of CNN is employed to adaptively learn the regions of interest (ROIs) of SMT solder joint images. Then, both the learned ROIs and the entire solder joint images are fed into the other two kinds of CNNs, respectively. Finally, inspection results are achieved by the learned cascaded CNN. Comparison experiments indicate that our proposed method can achieve more excellent inspection performance for SMT solder joints than that of the state-of-the-art methods.

Image Contrast Enhancement Using Weighted Transformation Function

This paper presents a contrast enhancement technique using weighted transformation functions. The transformation functions obtained at different levels (using modified histograms) are weighted according to their similarity/dissimilarity from mean value. To increase the dynamic range of enhanced image, bins having very slight contribution in the histogram are filtered out. Simulation results (evaluated visually and quantitatively) on different images show the significance of the proposed technique as compared with the state-of-the-art existing techniques.

Ferroptosis heterogeneity in triple-negative breast cancer reveals an innovative immunotherapy combination strategy

Treatment of triple-negative breast cancer (TNBC) remains challenging. Deciphering the orchestration of metabolic pathways in regulating ferroptosis will provide new insights into TNBC therapeutic strategies. Here, we integrated the multiomics data of our large TNBC cohort (n = 465) to develop the ferroptosis atlas. We discovered that TNBCs had heterogeneous phenotypes in ferroptosis-related metabolites and metabolic pathways. The luminal androgen receptor (LAR) subtype of TNBC was characterized by the upregulation of oxidized phosphatidylethanolamines and glutathione metabolism (especially GPX4), which allowed the utilization of GPX4 inhibitors to induce ferroptosis. Furthermore, we verified that GPX4 inhibition not only induced tumor ferroptosis but also enhanced antitumor immunity. The combination of GPX4 inhibitors and anti-PD1 possessed greater therapeutic efficacy than monotherapy. Clinically, higher GPX4 expression correlated with lower cytolytic scores and worse prognosis in immunotherapy cohorts. Collectively, this study demonstrated the ferroptosis landscape of TNBC and revealed an innovative immunotherapy combination strategy for refractory LAR tumors.

Chidamide: Targeting epigenetic regulation in the treatment of hematological malignancy

Epigenetic alterations frequently participate in the onset of hematological malignancies. Histone deacetylases (HDACs) are essential for regulating gene transcription and various signaling pathways. Targeting HDACs has become a novel treatment option for hematological malignancies. Chidamide is the first oral selective HDAC inhibitor for HDAC1, HDAC2, HDAC3, and HDAC10 and was first approved for the treatment of R/R peripheral T-cell lymphoma by the China Food and Drug Administration in 2014. Chidamide was also approved under the name Hiyasta (HBI-8000) in Japan in 2021. In vitro studies revealed that chidamide could inhibit proliferation and induce apoptosis via cell cycle arrest and the regulation of apoptotic proteins. In clinical studies, chidamide was also efficacious in multiple myeloma, acute leukemia and myelodysplastic syndrome. This review includes reported experimental and clinical data on chidamide monotherapy or chidamide treatment in combination with chemotherapy for various hematological malignancies, offering a rationale for the renewed exploration of this drug.

The Role of Civil Society Sector in the Development of Art-Driven Regional Social Innovation: The Case of Benesse Art Site Naoshima and Art Setouchi

Recently art is increasing its presence as an "creative industry" to sustain local communities, by generating socio-economic values. Still, whether art can be a tool for social innovation to regenerate communities, especially in rural areas in aging societies, is an unanswered question. In this paper, we take the example of Benesse Art Site Naoshima and Art Setouchi in the island area of Western Japan, viewing how it transformed from a corporate-established museum to a regional initiative involving various stakeholders, including local residents and thus creating the process of dialogues and collaboration. By reconstructing the existing evidence with supplementary fieldwork and interviews and applying a tri-sectoral analysis of the processes, we present how the art sites developed to become a social innovation. We then illustrate the role of two key individuals, Soichiro Fukutake and Fram Kitagawa, and shed light on the different values and methodologies they brought into these art sites. We argue that such contributions from the civil society and philanthropy sector made a critical contribution to characterize BASN and Art Setouchi, in addition to the well-documented and recognized efforts from local government and business sectors. Finally, we propose that such values, methodologies, and persons who can embody and implement such values are crucial if other countries and areas are to replicate the model.