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Methanol synthesis revisited: reaction mechanisms in CO/CO2 hydrogenation over Cu/ZnO and DFT analysis

One of the very long-standing controversies in CO/CO2 hydrogenation chemistry has been the following: What is the principal source of C in the methanol product, CO or CO2? In other words, do CO hydrogenation and CO2 hydrogenation proceed independently, in parallel, over the industrial Cu/ZnO/Al2O3 catalyst? Using state of the art experimental data and our analysis of prior studies and experimental techniques including isotopic labeling studies, including radioactive tracers, FTIR, XPS, and E-TEM, we discuss the experimental findings of the prior art and analyze the experimental and theoretical first-principles calculational data, to come up with a rational and cogent explanation for all controversial areas in methanol synthesis chemistry.

All-Aqueous, Surfactant-Free, and pH-Driven Nanoformulation Methods of Dual-Responsive Polymer Nanoparticles and their Potential use as Nanocarriers of pH-Sensitive Drugs

All-aqueous, surfactant-free, and pH-driven nanoformulation methods to generate pH- and temperature-responsive polymer nanoparticles (NPs) are described. Copolymers comprising a poly(methyl methacrylate) (PMMA) backbone with a few units of 2-(dimethylamino)ethyl methacrylate (DMAEMA) are solubilized in acidic buffer (pH 2.0) to produce pH-sensitive NPs. Copolymers of different molar mass (2.3-11.5 kg mol-1 ) and DMAEMA composition (7.3-14.2 mol%) are evaluated using a "conventional" pH-driven nanoformulation method (i.e., adding an aqueous polymer solution (acidic buffer) into an aqueous non-solvent (basic buffer)) and a robotized method for pH adjustment of polymer dispersions. Dynamic light scattering, zeta-potential (ζ), and sedimentation-diffusion analyses suggest the formation of dual-responsive NPs of tunable size (from 20 to 110 nm) being stable for at least 28 days in the pH and temperature intervals from 2.0 to 6.0 and 25 to 50 °C, respectively. Ultraviolet-visible spectroscopic experiments show that these NPs can act as nanocarriers for the pH-sensitive dipyridamole drug, expanding its bioavailability and potential controlled release as a function of pH and temperature. These approaches offer alternative strategies to prepare stimuli-responsive NPs, avoiding the use of harmful solvents and complex purification steps, and improving the availability of biocompatible polymer nanoformulations for specific controlled release of pH-sensitive cargos.

Saliency modeling via outlier detection

Based on the fact that human attention is more likely to be attracted by different objects or statistical outliers of a scene, a bottom-up saliency detection model is proposed. Our model regards the saliency patterns of an image as the outliers in a dataset. For an input image, first, each image element is described as a feature vector. The whole image is considered as a dataset and an image element is classified as a saliency pattern if its corresponding feature vector is an outlier among the dataset. Then, a binary label map can be built to indicate the salient and the nonsalient elements in the image. According to the Boolean map theory, we compute multiple binary maps as a set of Boolean maps which indicate the outliers in multilevels. Finally, we linearly fused them into the final saliency map. This saliency model is used to predict the human eye fixation, and has been tested on the most widely used three benchmark datasets and compared with eight state-of-the-art saliency models. In our experiments, we adopt the shuffled the area under curve metric to evaluate the accuracy of our model. The experimental results show that our model outperforms the state-of-the-art models on all three datasets. (C) 2014 SPIE and IS&T

Pathways to depression: Dynamic associations between neural responses to appetitive cues in the environment, stress, and the development of illness

This review focuses on research my colleagues and I have conducted on etiological pathways to depression. Much of this work has focused on the measurement of neural responses to appetitive cues, using two event-related brain potential (ERP) components, the Late Positive Potential (LPP) and the Reward Positivity (RewP). Reductions in each of these components have been associated with current symptoms of depression, and in some cases have been shown to differentiate anxious from depressive phenotypes. In this review, I will describe three broad and related approaches we have taken in our research to address a series of interdependent issuess. The first attempts to understand different sources of variation in the LPP and RewP, and how these sources interact with one another. The second tries to identify whether variation in the processes measured by these ERP components might reflect a latent vulnerability to depression and its symptoms, that is evident prior to illness onset. And the third examines the possibility that the processes reflected in the LPP and RewP might play a mechanistic role in the development of depression.

Fetal electrocardiogram estimation using polynomial eigenvalue decomposition

In this paper, we propose the application of polynomial matrix eigenvalue decomposition (PEVD) to the problem of fetal electrocardiogram (ECG) extraction from real ECG recordings obtained from abdominal leads. We model the fetal ECG extraction problem as a broadband sensor array signal processing problem in order to account for the broadband nature of the ECG noise present in the recordings. An algorithm for providing an approximate PEVD is used in order to estimate the broadband noise subspace. Suppression of the broadband noise and maternal ECG is achieved by carrying out an orthonormal projection of the recordings onto the estimated fetal subspace. The proposed scheme was evaluated with multichannel synthetic ECG signals and real ECG data from the PhysioNet Challenge database and is shown to perform favorably as compared to prior-art methods. Results indicate that our method is more robust than the prior-art ones for the task of fetal ECG estimation.

Sexual Risk Behaviors of Patients with HIV/AIDS over the Course of Antiretroviral Treatment in Northern Vietnam

Antiretroviral therapy (ART) improves the health and well-being of people living with the human immunodeficiency virus (HIV, PLWH), and reduces their risk of transmitting the virus to sexual partners. However, patterns of sexual risk behavior among HIV-positive patients taking ART in Vietnam remain largely unknown. In this study, we sought to examine sexual risk behaviors and their associated factors among HIV-positive patients receiving ART in northern Vietnam. The socio-demographic characteristics, ART use, health status, and sexual behaviors of 1133 patients taking ART in the Hanoi and Nam Dinh provinces were explored through face-to-face interviews. There were 63.5% of patients who had one sex partner, while 3.6% and 5.6% of patients had sexual intercourse with casual partners or sex workers, respectively, in the previous 12 months. Most participants tended to use condoms more often with commercial sex partners (90.2%) and intimate partners (79.7%), and less often with casual partners (60.9%). Higher age (odds ratio, OR = 1.0; 95% CIs = 1.0, 1.1) or suffering pain/discomfort (OR = 1.7; 95% CIs = 1.2, 2.4) were factors more likely to be associated with multiple sex partners. Patients who were self-employed were more likely to have sexual intercourse with casual partners/sex workers (OR = 2.1; 95% CIs = 1.1, 4.0). Meanwhile, a higher score on the EuroQol visual analog scale (EQ-VAS), an unknown HIV stage, and a longer duration of ART were adversely associated with not using condoms with casual partners/sex workers. Patients with longer durations of ART had a lower likelihood of not using a condom with casual partners/sex workers (OR = 0.5; 95% CIs = 0.3, 0.8). Our study underscored a relatively high rate of unsafe sexual behaviors among HIV-positive patients. Continuing to improve the physical and psychological well-being of HIV-positive patients in Vietnam is important in reducing the spread of HIV via risky sexual behaviors. In addition, safe-sex education should be provided to older people, and to those who are self-employed.

Efficient finite impulse response filters in massively-parallel recursive systems

This paper presents strategies to massively parallelize complete recursive systems. Each algorithm handles systems with feedforward and feedback coefficients allowing to compute high-complexity filtering operators. The final algorithm is linear in time and memory, exposes a high number of parallel tasks, and it is implemented on graphics processing units, i.e. GPUs. The key to the final algorithm is the derivation of closed-form formulas to combine both non-recursive and recursive linear filters, based on an efficient state-of-the-art block-based strategy. Applications to early vision are considered in this work, hence the GPU implementation runs on images computing an approximation of the Gaussian filter and its first and second derivatives. Finally, comparison results are given showing that this work outperforms prior state-of-the-art algorithms, enabling it to achieve real-time image filtering on ultra-high-definition videos.

An Effective Semantic Code Clone Detection Framework Using Pairwise Feature Fusion

Code clones. In this work, we propose a novel detection framework using machine learning for automated detection of all four type of clones. The features extracted from a pair of code blocks are combined for possible detection of a clone with respect to a reference block. We use AST and PDG features of both code blocks to prepare labelled training samples after fusing the two feature vectors using three different alternatives. We use six state-of-the-art classification models including Deep Convolutional Neural Network to assess the prediction performance of our scheme. To access the effectiveness of our framework we use seven datasets and compare its performance with five state-of-the-art clone detectors. We also compare a large number of algorithms for code clone detection. Comparing the performance of a large number of machine learning techniques, ANN and non-ANN, using such features, and establishing that fusing of AST and PDG features gives competitive results using deep learning as well as boosted tree algorithms, we find that boosted tree algorithms like XGBoost are quite competitive in clone detection. Experimental results demonstrate that our approach outperforms existing clone detection methods in terms of prediction accuracy.

Cross-Corpus Speech Emotion Recognition Based on Domain-Adaptive Least-Squares Regression

In this letter, a novel cross-corpus speech emotion recognition (SER) method using domain-adaptive least-squares regression (DaLSR) model is proposed. In this method, an additional unlabeled data set from target speech corpus is used to serve as an auxiliary data set and combined with the labeled training data set from source speech corpus for jointly training the DaLSR model. In contrast to the traditional least-squares regression (LSR) method, the major novelty of DaLSR is that it is able to handle the mismatch problem between source and target speech corpora. Hence, the proposed DaLSR method is very suitable for coping with cross-corpus SER problem. For evaluating the performance of the proposed method in dealing with the cross-corpus SER problem, we conduct extensive experiments on three emotional speech corpora and compare the results with several state-of-the-art transfer learning methods that are widely used for cross-corpus SER problem. The experimental results show that the proposed method achieves better recognition accuracies than the state-of-the-art methods.

Fast Edge Detection Using Structured Forests

Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.

Pattern analysis of neuroimaging data reveals novel insights on threat learning and extinction in humans

Several decades of rodent neurobiology research have identified a network of brain regions that support Pavlovian threat conditioning and extinction, focused predominately on the amygdala, hippocampus, and medial prefrontal cortex (mPFC). Surprisingly, functional magnetic resonance imaging (fMRI) studies have shown inconsistent evidence for these regions while humans undergo threat conditioning and extinction. In this review, we suggest that translational neuroimaging efforts have been hindered by reliance on traditional univariate analysis of fMRI. Whereas univariate analyses average activity across voxels in a given region, multivariate pattern analyses (MVPA) leverage the information present in spatial patterns of activity. MVPA therefore provides a more sensitive analysis tool to translate rodent neurobiology to human neuroimaging. We review human fMRI studies using MVPA that successfully bridge rodent models of amygdala, hippocampus, and mPFC function during Pavlovian learning. We also highlight clinical applications of these information-sensitive multivariate analyses. In sum, we advocate that the field should consider adopting a variety of multivariate approaches to help bridge cutting-edge research on the neuroscience of threat and anxiety.

Integrated energy systems of data centers and smart grids: State-of-the-art and future opportunities

Cloud computing platforms are critical cyber infrastructures in modern society. As the backbone of cloud systems, data centers act as large energy consumers in today's power grids. The integration of on-site renewable energy sources and energy storage systems further transforms data centers to be energy prosumers (producersand-consumers). As a result, optimizing data centers' energy production and consumption, and exploiting their potential of actively engaging in the external grid's planning, operation, and control has been drawing increasing attention in the last few years. This paper conducts a comprehensive review of the state-of-the-art research efforts on integrated energy systems of data centers and smart grids. A taxonomy of such integration scenarios is provided. Consequently, this paper identifies several future application scenarios of integrating data centers and smart grids, which serves as a roadmap towards future research. This article is expected to provide a useful reference for researchers and engineers in the areas of energy systems and cloud computing.

Data-driven health deficit assessment improves a frailty index's prediction of current cognitive status and future conversion to dementia: results from ADNI

Frailty is a dementia risk factor commonly measured by a frailty index (FI). The standard procedure for creating an FI requires manually selecting health deficit items and lacks criteria for selection optimization. We hypothesized that refining the item selection using data-driven assessment improves sensitivity to cognitive status and future dementia conversion, and compared the predictive value of three FIs: a standard 93-item FI was created after selecting health deficit items according to standard criteria (FIs) from the ADNI database. A refined FI (FIr) was calculated by using a subset of items, identified using factor analysis of mixed data (FAMD)-based cluster analysis. We developed both FIs for the ADNI1 cohort (n = 819). We also calculated another standard FI (FIc) developed by Canevelli and coworkers. Results were validated in an external sample by pooling ADNI2 and ADNI-GO cohorts (n = 815). Cluster analysis yielded two clusters of subjects, which significantly (pFDR < .05) differed on 26 health items, which were used to compute FIr. The data-driven subset of items included in FIr covered a range of systems and included well-known frailty components, e.g., gait alterations and low energy. In prediction analyses, FIr outperformed FIs and FIc in terms of baseline cognition and future dementia conversion in the training and validation cohorts. In conclusion, the data show that data-driven health deficit assessment improves an FI's prediction of current cognitive status and future dementia, and suggest that the standard FI procedure needs to be refined when used for dementia risk assessment purposes.

Current strategies in biomaterial-based periosteum scaffolds to promote bone regeneration: A review

The role of periosteum rich in a variety of bone cells and growth factors in the treatment of bone defects has gradually been discovered. However, due to the limited number of healthy transplantable periosteum, there are still major challenges in the clinical treatment of critical-size bone defects. Various techniques for preparing biomimetic periosteal scaffolds that are similar in composition and structure to natural periosteal scaffold have gradually emerged. This article reviews the current preparation methods of biomimetic periosteal scaffolds based on various biomaterials, which are mainly divided into natural periosteal materials and various polymer biomaterials. Several preparation methods of biomimetic periosteal scaffolds with different principles are listed, their strengths and weaknesses are also discussed. It aims to provide a more systematic perspective for the preparation of biomimetic periosteal scaffolds in the future.

Modern yeast development: finding the balance between tradition and innovation in contemporary winemaking

A key driver of quality in wines is the microbial population that undertakes fermentation of grape must. Winemakers can utilise both indigenous and purposefully inoculated yeasts to undertake alcoholic fermentation, imparting wines with aromas, flavours and palate structure and in many cases contributing to complexity and uniqueness. Importantly, having a toolbox of microbes helps winemakers make best use of the grapes they are presented with, and tackle fermentation difficulties with flexibility and efficiency. Each year the number of strains available commercially expands and more recently, includes strains of non-Saccharomyces, strains that have been improved using both classical and modern yeast technology and mixed cultures. Here we review what is available commercially, and what may be in the future, by exploring recent advances in fermentation relevant strain improvement technologies. We also report on the current use of microbes in the Australian wine industry, as reported by winemakers, as well as regulations around, and sentiment about the potential use of genetically modified organisms in the future.

A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications

As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by [Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1-29.]. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total). In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP's variant); (iv) Chang's extent analysis method, which is used for FPCMs' weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature). (C) 2016 Elsevier Ltd. All rights reserved.

RNA m6A reader IGF2BP3 promotes metastasis of triple-negative breast cancer via SLIT2 repression

Triple-negative breast cancer (TNBC) is a group of fatal malignancies characterized by high metastatic capacity, the underlying mechanisms of which remain largely elusive. We have found here that insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) is highly expressed in TNBC and correlates clinically with distant metastasis-free survival of TNBC patients. IGF2BP3 promotes the migration and invasion capabilities of TNBC cells dependent upon cellular RNA N6-methyladenosine (m6A) modification. Mechanistically, IGF2BP3 binds to and destabilizes m6A-methylated mRNA of the extracellular matrix glycoprotein, SLIT2, impairs its downstream signaling via the cognate receptor ROBO1, and consequently triggers the activation of canonical PI3K/AKT and MEK/ERK pathways. The IGF2BP3/SLIT2 axis is critically involved in the regulation of TNBC metastasis in vivo. These findings shed light into the regulatory network of distant metastasis of breast cancer and provide rationale for targeting the m6A machinery in the treatment of TNBC.

Multiaxis Contour Control-the State of the Art

Contour control is an important task for many motion control applications. This paper reviews the state-of-the-art control methods in academic research for contour tracking, including individual axis tracking control, cross coupled control and its variants, and other contour control methods, such as neural networks and velocity field control. Different methods for contour error estimation are also reviewed. Areas for future multiaxis contour control research are discussed.

Recent developments in the creation of a single molecular sensing tool for ternary iron (III), chromium (III), aluminium (III) ionic species: A review

Rational design of a molecular sensing tool is an important topic in molecular recognition, signalling, and optoelectronics that has piqued the interest of chemists, biologists, and environmental scientists. Approximately 150 years have passed since the beginning of the fluorescent chemosensor sector. Due to the paramagnetic properties of Cr3+ and Al3+ , it is tough to prepare a photoluminescence plug-in detector. Most dye-based Al3+ sensors must be utilized in organic or mixed solvents for robust hydration of Al3+ in water. The sophisticated molecular design of sensors, conversely, allows for the detection of these metal ions in aqueous medium. The design of chemosensors using various fluorophores and their mechanisms of action have been thoroughly discussed. A literature survey covering the design of chemosensors and their mechanisms of action have been thoroughly discussed covering the period 2010-2022 and that was carried out including innovative and exemplary activities from numerous groups throughout the world that have significantly contributed to this sector. The most important advantages of these probes are their aqueous solubility and quick response with outstanding selectivity and sensitivity for temporal distribution with high fidelity of metals in living cells.

Increased Incidence Of Inhalation Burn Injury During The Covid-19 Pandemic: A National Database Study

The COVID-19 pandemic has forced many Americans to adapt their daily routines. In 2020, there was a significant increase in house fires according to the National Fire Prevention Association (NFPA). The objective of this study was to characterize the changes in suspected smoke inhalations during the first year of the pandemic in the National Emergency Medical Services Information System (NEMSIS). The NEMSIS database was queried for all EMS transports captured between 2017-2020. Differences in the incidences of suspected smoke inhalations and fire dispatches in 2020 were estimated using Poisson regression models. There was a 13.4% increase in the incidence of fire dispatches and a 15% increase in suspected smoke inhalations transported in 2020 compared to the previous 3 years. The IRR of both fire dispatches (1.271; 95% CI: 1.254-1.288; p<0.001) and suspected smoke inhalation (1.152; 95% CI: 1.070-1.241; p<0.001) was significantly elevated in 2020. The increases in fire dispatches and suspected smoke inhalations observed in the NEMSIS database are in concordance with other literature indicating the increase in fire incidence and morbidity observed during the pandemic. These results should inform fire prevention outreach efforts and resource allocation in burn centers in the event of future pandemic.

Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. In this paper, we propose a novel method called SPE-DNR that combines spherical-patches extraction (SPE) and deep-learning for neuron reconstruction (DNR). Based on 2D Convolutional Neural Networks (CNNs) and the intensity distribution features extracted by SPE, it determines the tracing directions and classifies voxels into foreground or background. This way, starting from a set of seed points, it automatically traces the neurite centerlines and determines when to stop tracing. To avoid errors caused by imperfect manual reconstructions, we develop an image synthesizing scheme to generate synthetic training images with exact reconstructions. This scheme simulates 3D microscopy imaging conditions as well as structural defects, such as gaps and abrupt radii changes, to improve the visual realism of the synthetic images. To demonstrate the applicability and generalizability of SPE-DNR, we test it on 67 real 3D neuron microscopy images from three datasets. The experimental results show that the proposed SPE-DNR method is robust and competitive compared with other state-of-the-art neuron reconstruction methods.

Minimizing the deleterious effects of endophytes in plant shoot tip cryopreservation

Plant cryopreservation technologies are used within gene banks for the long-term preservation of vegetatively propagated collections. Surface-sterilized plant tissues grown in the field, greenhouse/screenhouse, growth chamber, or in vitro are the source of shoot tips subjected to vitrification-based cryopreservation methods. Here, we describe the methods used to minimize microbial contamination during the tissue culture initiation process. We also discuss the occurrence and possible elimination of endophytes after extended in vitro culture and during recovery after liquid nitrogen exposure. We describe two case studies in which bacterial endophytes were observed in Citrus gene bank accessions during recovery after cryopreservation. These were identified using the MinION Oxford Nanopore system and Kirby-Bauer disc diffusion assays to examine the bacterial responses to antibiotic exposure. The methods used in this case study could be applied to identify endophytes to better target antimicrobial treatments of plant tissue collections.

Allergen Content and Protease Activity in Milk Feeds from Mothers of Preterm Infants

Rationale: There is little information regarding the allergen content of milk feeds in the preterm population. Previous studies have not performed a broad analysis of the allergenic peptide content and protease activity of milk feeds in this population. Methods: To evaluate feasibility, we initially performed mass spectrometry on 4 human milk (HM) samples (2 term and 2 preterm) from the Mommy's Milk Human Milk Biorepository (HMB) and analyzed the results against the University of Nebraska FASTA database and UniProt for a total of 2,211 protein sequences. We then further analyzed five samples from the Microbiome, Atopy, and Prematurity (MAP) study including peptidomic and protease activity analysis. Results: Each HMB sample had between 806 and 1,007 proteins, with 37-44 nonhuman proteins/sample encompassing 26 plant and animal species. In the preterm MAP samples, 784 digested nonhuman proteins were identified, 30 were nonbovine in origin. Proteins from 23 different species including aeroallergens, food, and contact allergens were identified. Protease activity was highest in HM samples without human milk fortifier and lowest in preterm formula. Conclusions: These findings represent the first preterm milk feed mass spectrometry and protease analysis with identification of known allergenic proteins to food, contact, and aeroallergens. These results raise questions of whether the composition of milk feeds in the neonatal intensive care unit impact the development of atopic disease in the preterm population and whether the complex interaction between allergens, proteases, and other HM components can serve to induce sensitization or tolerance to allergens in infants. Clinical Trial Registration Number: NCT04835935.

Survival of Patient With Hemorrhagic Meningitis Associated With Inhalation Anthrax

This report describes a 49-year-old male construction worker who acquired a Bacillus anthracis infection after working on a sheep farm. He experienced a severe respiratory infection, septic shock, and hemorrhagic meningoencephalitis with severe intracranial hypertension. After several weeks with multiple organ dysfunction syndrome, he responded favorably to antibiotic treatment. Three weeks into his hospitalization, an intracranial hemorrhage and cerebral edema led to an abrupt deterioration in his neurological status. A single dose of raxibacumab was added to his antimicrobial regimen on hospital day 27. His overall status, both clinical and radiographic, improved within a few days. He was discharged 2 months after admission and appears to have fully recovered.

Adaptive Denoising by Singular Value Decomposition

This letter presents an adaptive denoising method based on the singular value decomposition (SVD). By incorporating a global subspace analysis into the scheme of local basis selection, the problems of previous adaptive methods are effectively tackled. Experimental results show that the proposed method achieves outstanding preservation of image details, and at high noise levels it provides improvements in both objective and subjective quality of the denoised image when compared to the state-of-the-art methods.

Laser nano-manufacturing - State of the art and challenges

This paper provides an overview of advances in laser based nano-manufacturing technologies including surface nano-structure manufacturing, production of nano materials (nanoparticles, nanotubes and nanowires) and 3D nano-structures manufacture through multiple layer additive techniques and nano-joining/forming. Examples of practical applications of laser manufactured nano-structures, materials and components are given. A discussion on the challenges and outlooks in laser nano-manufacturing is presented. (C) 2011 CIRP.

Factors impacting educational outcomes for students with traumatic brain injury in BrainSTEPS

Purpose: To describe child pre-injury and injury factors impacting post-injury educational outcomes for students with traumatic brain injury (TBI) participating in a state-wide, school-based, school re-entry consultation program, BrainSTEPS in Pennsylvania.Method: Retrospective analysis of a BrainSTEPS annual follow-up survey.Result: A total of 296 parent surveys were completed. Analysis revealed a significant difference between levels of severity of TBI and current educational placement (p < 0.001), receipt of current therapy (p < 0.05) and need for additional consult (p < 0.05). Severity of TBI was not related to other examined educational outcome variables (i.e. school performance, current symptoms). History of TBI, symptoms and treatment were not found to be associated with educational outcomes.Conclusion: These results both support findings from previous studies, and extend previous work by highlighting ongoing needs, including continued, individualised support, of children who sustain a moderate-severe TBI during childhood, and are currently in the chronic stages of injury, with consideration of pre- and post- injury factors. Programs such as BrainSTEPS provide identification of educational needs and provide needed services and supports for children with TBI. Sensitive, validated measures are needed to further understand the role of pre-injury and injury factors on educational outcomes, particularly in programs like BrainSTEPS.

Optimised quantisation method for approximate nearest neighbour search

We propose optimised group quantisation (OGQ) for approximate nearest neighbour (ANN) search. Specifically, we construct a group of codebooks and select a group of codewords from the codebooks to approximate the original data such that small quantisation error is obtained. We also propose an effective learning algorithm for optimisation. The experiments show that OGQ can significantly outperform several state-of-the-art ANN methods.

Carbon dots modified/prepared by supramolecular host molecules and their potential applications: A review

Supramolecular host molecules are used as tools in the design of multifunctional nanoparticles for sensors, catalysts, biometric elements, etc. Combining with carbon dots (CDs) has excellent host-guest recognition properties and fluorescence characteristics, which can precisely capture and identify target analytes. Consequently, supramolecular host molecules-based CDs can significantly improve the detection performance of ions and molecules with different structures or intrinsic chemical properties. This currently responds to a wide range of analytes including metal cations, anions, organic compounds and other biomolecules, yielding fascinating achievements in the field of chemistry. Therefore, the present review summarizes outstanding supramolecular host molecules-based CDs reported in the past ten years. The focus is on elucidating the mechanisms, methodologies, advantages and disadvantages of modifying or preparing CDs with supramolecular host molecules. Current challenges encountered and outlooks are also be discussed.

Monkeypox treatment: Current evidence and future perspectives

As of September 11, 2022, 57 669 reports of monkeypox infection raised global concern. Previous vaccinia virus vaccination can protect from monkeypox. However, after smallpox eradication, immunization against that was stopped. Indeed, therapeutic options following the disease onset are of great value. This study aimed to review the available evidence on virology and treatment approaches for monkeypox and provide guidance for patient care and future studies. Since no randomized clinical trials were ever performed, we reviewed monkeypox animal model studies and clinical trials on the safety and pharmacokinetics of available medications. Brincidofovir and tecovirimat were the most studied medications that got approval for smallpox treatment according to the Animal Rule. Due to the conserved virology among Orthopoxviruses, available medications might also be effective against monkeypox. However, tecovirimat has the strongest evidence to be effective and safe for monkeypox treatment, and if there is a choice between the two drugs, tecovirimat has shown more promise so far. The risk of resistance should be considered in patients who failed to respond to tecovirimat. Hence, the target-based design of novel antivirals will enhance the availability and spectrum of effective anti-Orthopoxvirus agents.

A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

In recent years, sentiment analysis is becoming more and more important as the number of digital text resources increases in parallel with the development of information technology. Feature selection is a crucial sub-stage for the sentiment analysis as it can improve the overall predictive performance of a classifier while reducing the dimensionality of a problem. In this study, we propose a novel wrapper feature selection algorithm based on Iterated Greedy (IG) metaheuristic for sentiment classification. We also develop a selection procedure that is based on pre-calculated filter scores for the greedy construction part of the IG algorithm. A comprehensive experimental study is conducted on commonly-used sentiment analysis datasets to assess the performance of the proposed method. The computational results show that the proposed algorithm achieves 96.45% and 90.74% accuracy rates on average by using Multi-nomial Naive Bayes classifier for 9 public sentiment and 4 Amazon product reviews datasets, respectively. The results also reveal that our algorithm outperforms state-of-the-art results for the 9 public sentiment datasets. Moreover, the proposed algorithm produces highly competitive results with state-of-the-art feature selection algorithms for 4 Amazon datasets. (C) 2020 Elsevier Ltd. All rights reserved.

An Adaptive Multiobjective Approach to Evolving ART Architectures

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).

Open-view human action recognition based on linear discriminant analysis

In the last decades, action recognition task has evolved from single view recording to unconstrained environment. Recently, multi-view action recognition has become a hot topic in computer vision. However, we notice that only a few works have focused on the open-view action recognition, which is a common problem in the real world. Open-view action recognition focus on doing action recognition in unseen view without using any information from it. To address this issue, we firstly introduce a novel multi-view surveillance action dataset and benchmark several state-of-the-art algorithms. From the results, we observe that the performance of the state-of-the-art algorithms would drop a lot under open-view constraints. Then, we propose a novel open-view action recognition method based on the linear discriminant analysis. This method can learn a common space for action samples under different view by using their category information, which can achieve a good result in open-view action recognition.

Can Aikido Help With the Comprehension of Physics? A First Step Towards the Design of Intelligent Psychomotor Systems for STEAM Kinesthetic Learning Scenarios

Educational stakeholders are promoting the development of educational approaches that go beyond classroom and engage learners in acquiring science, technology, engineering and mathematical (STEM) concepts with the support of arts (known as STEAM). Taking into account the recent advances in wearable technology to sense human motion, psychomotor intelligent tutoring systems (where students' motion interactions are collected with sensors and used to provide personalized vibrotactile feedback to support the learning process) can be designed to teach STEM concepts in a kinesthetic way. In order to investigate the feasibility of this approach, in this paper we have carried out an empirical study with high school students to determine if watching specific techniques of the defense martial art Aikido (which makes use of different types of rectilinear and curvilinear motion) can be used to learn some concepts of physics. Nonetheless, other martial arts could be used for similar purposes, but further studies are needed. Analyzing the outcomes of the 30 participants that took part in the study, we conclude that the proposed approach seems to have benefits in the learning of STEM concepts, and thus, the usage of martial arts deserves to be further investigated in STEAM kinesthetic learning scenarios, which can also consider the students' affective state.

The failure of drug repurposing for COVID-19 as an effect of excessive hypothesis testing and weak mechanistic evidence

The current strategy of searching for an effective treatment for COVID-19 relies mainly on repurposing existing therapies developed to target other diseases. Conflicting results have emerged in regard to the efficacy of several tested compounds but later results were negative. The number of conducted and ongoing trials and the urgent need for a treatment pose the risk that false-positive results will be incorrectly interpreted as evidence for treatments' efficacy and a ground for drug approval. Our purpose is twofold. First, we show that the number of drug-repurposing trials can explain the false-positive results. Second, we assess the evidence for treatments' efficacy from the perspective of evidential pluralism and argue that considering mechanistic evidence is particularly needed in cases when the evidence from clinical trials is conflicting or of low quality. Our analysis is an application of the program of Evidence Based Medicine Plus (EBM+) to the drug repurposing trials for COVID. Our study shows that if decision-makers applied EBM+, authorizing the use of ineffective treatments would be less likely. We analyze the example of trials assessing the efficacy of hydroxychloroquine as a treatment for COVID-19 and mechanistic evidence in favor of and against its therapeutic power to draw a lesson for decision-makers and drug agencies on how excessive hypothesis testing can lead to spurious findings and how studying negative mechanistic evidence can be helpful in discriminating genuine from spurious results.

Exploring Local Detail Perception for Scene Sketch Semantic Segmentation

In this paper, we aim to explore the fine-grained perception ability of deep models for the newly proposed scene sketch semantic segmentation task. Scene sketches are abstract drawings containing multiple related objects. It plays a vital role in daily communication and human-computer interaction. The study has only recently started due to a main obstacle of the absence of large-scale datasets. The currently available dataset SketchyScene is composed of clip art-style edge maps, which lacks abstractness and diversity. To drive further research, we contribute two new large-scale datasets based on real hand-drawn object sketches. A general automatic scene sketch synthesis process is developed to assist with new dataset composition. Furthermore, we propose to enhancing local detail perception in deep models to realize accurate stroke-oriented scene sketch segmentation. Due to the inherent differences between hand-drawn sketches and natural images, extreme low-level local features of strokes are incorporated to improve detail discrimination. Stroke masks are also integrated into model training to guide the learning attention. Extensive experiments are conducted on three large-scale scene sketch datasets. Our method achieves state-of-the-art performance under four evaluation metrics and yields meaningful interpretability via visual analytics.

Phylogenetic placement of Turkish populations of Ixodes ricinus and Ixodes inopinatus

Studies on phylogeography and population structure of Ixodes ricinus have been carried out in Europe for decades, but the number of specimens from the Middle East included in these analyses is relatively small, despite the wide distribution of the species in this area. This study aimed to clarify the phylogenetic positions of I. ricinus from Turkey as well as to investigate the presence of Ixodes inopinatus in Anatolia. For this purpose, one mitochondrial (mt 16S rDNA) and one nuclear gene (defensin) were used to generate molecular data from I. ricinus samples, which were collected from 17 locations across the species' distributional range in Turkey. Bayesian inference was used to investigate phylogenetic relationships. Globally, the mt 16S rDNA lineages correspond to the lineages revealed by defensin; I. ricinus and I. inopinatus sequences clustered separately. However, a discordant genetic pattern was observed between the phylogenetic position of turkish I. ricinus revealed by nuclear versus mitochondrial genes. All Turkish haplotypes of mt 16SrDNA clustered with I. ricinus samples from Europe, which might be the result of extensive gene flow between populations of Europe and the Middle East. On the other hand, a sample from Thrace Region grouped within I. inopinatus clade. Thus, the occurrence of I. inopinatus in Turkey was demonstrated for the first time using molecular data. Moreover, four individuals were found to be heterozygous for the defensin. The potential evolutionary processes that underlie this observed discrepancy between the phylogenetic trees of two genes have been discussed.

White Light Emission Through Downconversion of Terbium and Europium Doped CeF3 Nanophosphors

CeF3 nanophosphors have been extensively investigated in recent years for lighting and numerous bio-applications. Downconversion emissions in CeF3:Eu(3+)/Tb(3+) phosphors were studied with the objective of attaining a white light emitting composition, by means of a simple co-precipitation method. The material was characterized by X-ray Diffraction (XRD), Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FT-IR) and Photoluminescence (PL). Uniformly distributed nanoparticles were obtained with an average particle size range of 8-10 nm. Various studies were undertook utilizing different doping concentrations and respective fluorescence studies were carried out to optimize dopant concentrations while achieving maximum luminescence intensity. From PL results, it was observed that the efficient energy transfers from the donor to the acceptor ions. Different concentrations of Tb(3+), Eu(3+) were doped in order to achieve a white light emitting phosphor for UV-based Light Emitting Diodes (LEDs). The nanoparticles showed characteristic emission of respective dopants (Eu(3+), Tb(3+)) when excited at the 4f → 5d transition of Ce(3+). The chromaticity coordinates for CeF3 doped with Eu(3+) and Tb(3+) were calculated and an emission very close to white light was observed.

A Linear Encoding Approach to Index Assignment in Lossy Source-Channel Coding

We present a general scheme for the lossy transmission of a source with arbitrary statistics through a noisy channel under the mean-square error fidelity criterion. Our approach is based on transform coding, scalar quantization of the transform coefficients and linear encoding of the quantization indices. Entropy coding and channel coding are merged into a single linear encoding function, such that the "catastrophic" behavior of conventional entropy coding is avoided and the full power of modern coding techniques and iterative "Belief-Propagation" decoding can be exploited. We show that this approach is asymptotically optimal in the limit of large block length, for arbitrary source statistics and binary-input output-symmetric channel. In the practical regime of finite block length and low decoding complexity, we show, through the explicit construction of codes for the lossy transmission of digital images over a binary symmetric channel, that our approach yields significant improvements with respect to previously proposed channel-optimized quantization schemes and also with respect to the conventional concatenation of state-of-the art image coding with state-of-the art channel coding. Although our constructive example focuses on a special case, the approach is general and can be applied to other classes of sources of practical interest.

Methylation patterns within 5'-UTR of DAT1 gene as a function of allelic 3'-UTR variants and their maternal or paternal origin: May these affect the psychopathological phenotypes in children? An explorative study

Psychopathological symptoms such as depression/anxiety vs attention or aggression problems, in children, have been associated to altered expression of the DAT1/SLC6A3 gene. Inheriting specific 9- or 10-repeat VNTR alleles could modify the pattern of methylation in the CpGs islands at the 5'-UTR of the DAT1 gene. Through accurate recruitment at primary schools, we ended up with four subgroups of children: 9/9 and 10/10 homozygous; 9/10 heterozygous born from 9/10 mothers and 10/10 fathers (called heM); 9/10 heterozygous born from 10/10 mothers and 9/10 fathers (called heF). (Epi)genetical changes were found to be in relation to internalizing and externalizing symptoms: compared to other genotypes, our 9/9 children exhibited mainly internalizing symptoms, while 10/10 genotype was previously associated with ADHD severity. We found that 10/10 children bear 5'-UTR motifs showing a CpGs 1-2-3-5 unity with anticorrelated CpG 6, while 9/9 children showed rather a demethylated CpG 1 linked to demethylated CpG 6. We found two different patterns between heMs and heFs: a feature of heM children is in CpGs 1-3 methylated pattern with CpGs 2, 5 and 6 demethylated together, supporting a "split" unitary destiny. Within the heF children, the status for CpGs 3 + 6 remained opposite, yet pattern of (de)methylation was not well defined. The prevailing one between inherited parental alleles may somewhat influence the motif destiny of heterozigous children. Present work aimed to identify novel epigenetic biomarkers, to be exploited as fairly indicators of children's psychopathological vulnerability.

Global-Local Transformer for Brain Age Estimation

Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0-97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: https://github.com/shengfly/global-local-transformer.

Pseudomonas fitomaticsae sp. nov., isolated at Marimurtra Botanical Garden in Blanes, Catalonia, Spain

In the framework of the research project called fitomatics, we have isolated and characterized a bacterial plant-endophyte from the rhizomes of Iris germanica, hereafter referred to as strain FIT81T. The bacterium is Gram negative, rod-shaped with lophotrichous flagella, and catalase- and oxidase-positive. The optimal growth temperature of strain FIT81T is 28 °C, although it can grow within a temperature range of 4-32 °C. The pH growth tolerance ranges between pH 5 and 10, and it tolerates 4% (w/v) NaCl. A 16S rRNA phylogenetic analysis positioned strain FIT81T within the genus Pseudomonas, and multilocus sequence analysis revealed that Pseudomonas gozinkensis IzPS32dT, Pseudomonas glycinae MS586T, Pseudomonas allokribbensis IzPS23T, 'Pseudomonas kribbensis' 46-2 and Pseudomonas koreensis PS9-14T are the top five most closely related species, which were selected for further genome-to-genome comparisons, as well as for physiological and chemotaxonomic characterization. The genome size of strain FIT81T is 6 492 796 base-pairs long, with 60.6 mol% of G+C content. Average nucleotide identity and digital DNA-DNA hybridization analyses yielded values of 93.6 and 56.1%, respectively, when the FIT81T genome was compared to that of the closest type strain P. gozinkensis IzPS32dT. Taken together, the obtained genomic, physiologic and chemotaxonomic data indicate that strain FIT81T is different from its closest relative species, which lead us to suggest that it is a novel species to be included in the list of type strains with the name Pseudomonas fitomaticsae sp. nov. (FIT81T=CECT 30374T=DSM 112699T).

Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks

Fully convolutional networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same relevance for the classes being predicted. In order to learn the inter-channel relationships and recalibrate the channels to suppress the less relevant ones, squeeze and excitation blocks were proposed in the context of image classification with convolutional neural networks. However, this is not well adapted for segmentation with fully convolutional networks since they segment several objects simultaneously, hence a feature map may contain relevant information only in some locations. In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with fully convolutional networks- the SegSE block. Feature maps are recalibrated by considering the cross-channel information together with spatial relevance. The experimental results indicate that recombination and recalibration improve the results of a competitive baseline, and generalize across three different problems: brain tumor segmentation, stroke penumbra estimation, and ischemic stroke lesion outcome prediction. The obtained results are competitive or outperform the state of the art in the three applications.

Long-term effectiveness and safety of omalizumab in pediatric and adult patients with moderate-to-severe inadequately controlled allergic asthma

Omalizumab is recommended as an add-on therapy in patients aged ≥6 years with inadequately controlled, moderate-to-severe persistent allergic asthma. The efficacy and safety of omalizumab treatment in allergic asthma clinical trials and its effectiveness in the real world have been reported in numerous studies. In this review, we examine clinical evidence in pediatric and adult patients with allergic asthma who received omalizumab treatment for at least 2 years, to assess its effectiveness, durability, and trajectory of response over time as well as safety. We performed a literature search from inception until March 2022 in PubMed using the keywords "omalizumab" and "allergic asthma" to retrieve articles examining the effects of omalizumab in patients with allergic asthma, aged ≥6 years. Only articles that evaluated the effectiveness of omalizumab for at least 2 years were included. Data from case reports were excluded. Our review confirmed the long-term effectiveness and safety of omalizumab, demonstrating reduced rate of exacerbations, improved lung function, asthma control, and quality of life, decreased health care resource utilization, and use of corticosteroids (oral/inhaled) with a favorable safety and tolerability profile for up to 9 years in adult patients with moderate-to-severe allergic asthma. Similar results were also observed in the pediatric population with up to 7.5 years of omalizumab treatment. This review highlights and confirms the sustained clinical benefits of omalizumab over long periods of treatment in pediatric and adult populations with allergic asthma.

Towards Fast and Optimal Grouping of Regular Expressions via DFA Size Estimation

Regular Expression (RegEx) matching, as a core operation in many network and security applications, is typically performed on Deterministic Finite Automata (DFA) to process packets at wire speed; however, DFA size is often exponential in the number of RegExes. RegEx grouping is the practical way to address DFA state explosion. Prior RegEx grouping algorithms are extremely slow and memory intensive. In this paper, we first propose DFAestimator, an algorithm that can quickly estimate DFA size for a given RegEx set without building the actual DFA. Second, we propose RegexGrouper, a RegEx grouping algorithm based on DFA size estimation. In terms of speed and memory consumption, our work is orders of magnitude more efficient than prior art because DFA size estimation is much faster and memory efficient than DFA construction. In terms of the resulting size sum of DFAs, our work is significantly more effective than prior art because we use a much finer grained quantification of the degree of interaction between two RegExes. For example, to divide the RegEx set of the L7-filter system into 7 groups, prior art uses 279.3 minutes and the resulting 7 DFAs have a total of 29047 states, whereas RegexGrouper uses 3.2 minutes and the resulting 7 DFAs have a total of 15578 states.

Timing Analysis of Tasks on Runtime Reconfigurable Processors

Real-time embedded systems need to be analyzable for timing guarantees. Despite significant scientific advances, however, timing analysis lags years behind current microarchitectures with out-of-order scheduling pipelines, several hardware threads, and multiple (shared) cache layers. To satisfy the increasing performance demands, analyzable performance features are required. We propose a novel timing analysis approach to introduce runtime reconfigurable instruction set processors as one way to escape the scarcity of analyzable performance while preserving the flexibility of the system. We introduce extensions to the state-of-the-art Integer linear programming (ILP)-based program path analysis for computing precise worst case time bounds in the presence of the widely used technique to continue processor execution during reconfiguration by emulating not yet reconfigured custom instructions (CIs) in software. We identify and safely bound a timing anomaly of runtime reconfiguration, where executing faster than worst case time during reconfiguration extends the execution time of the whole program. Stalling the processor during reconfiguration (easier to analyze but not state-of-the-art for reconfigurable processors) is not required in our approach. Finally, we show the precision of our analysis on a complex multimedia application with multiple reconfigurable CIs for several hardware parameters and give advice on how to deal with reconfiguration delay under timing guarantees.

Intergenerational Programming Increases Solid Food Consumption for Adult Day Center Attendees

We examined whether participation in intergenerational programming would impact daily food and liquid intake for adult day service center (ADSC) participants, many of whom are at risk for malnutrition and dehydration. Data came from 75 ADSC participants who, on average, attended the center for 472.32 days between 2007 and 2018. We analyzed daily data using multilevel modeling, nesting attending days within ADSC participants. On days when participants joined intergenerational programming, they consumed significantly more solid food (β = 1.54, SD = .37, p < .001), but no different liquid (β = -.16, SD = .09, p = .06), than their own average across all days they attended the ADSC. Intergenerational programming may be an effective way to support ADSCs participants' nutrition. Future research is needed to determine the longer-term health benefits of daily increases in food consumption and to explore why intergenerational programming may differentially impact eating and drinking.

Phytoremediation of nitrogen and phosphorus pollutants from glass industry effluent by using water hyacinth (Eichhornia crassipes (Mart.) Solms): Application of RSM and ANN techniques for experimental optimization

The present study aimed to assess the efficiency of the water hyacinth (Eichhornia crassipes (Mart.) Solms) plant for the reduction of nitrogen and phosphorus pollutants from glass industry effluent (GIE) as batch mode phytoremediation experiments. For this, response surface methodology (RSM) and artificial neural networks (ANN) methods were adopted to evidence the optimization and prediction performances of E. crassipes for total Kjeldahl's nitrogen (TKN) and total phosphorus (TP) removal. The control parameters, i.e., GIE concentration (0, 50, and 100%) and plant density (1, 3, and 5 numbers) were used to optimize the best reduction conditions of TKN and TP. A quadratic model of RSM and feed-forward backpropagation algorithm-based logistic model (input layer: 2 neurons, hidden layer: 10 neurons, and output layer: 1 neuron) of ANN showed good fitness results for experimental optimization. Optimization results showed that maximum reduction of TKN (93.86%) and TP (87.43%) was achieved by using 60% of GIE concentration and nearly five plants. However, coefficient of determination (R2) values showed that ANN models (TKN: 0.9980; TP: 0.9899) were superior in terms of prediction performance as compared to RSM (TKN: 0.9888; TP: 0.9868). Therefore, the findings of this study concluded that E. crassipes can be effectively used to remediate nitrogen and phosphorus loads of GIE and minimize environmental hazards caused by its unsafe disposal.

An accelerated correlation filter tracker

Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features. Despite the outstanding performance achieved by the above combination, existing advanced trackers suffer from the burden of high computational complexity of the deep feature extraction and online model learning. We propose an accelerated ADMM optimisation method obtained by adding a momentum to the optimisation sequence iterates, and by relaxing the impact of the error between DCF parameters and their norm. The proposed optimisation method is applied to an innovative formulation of the DCF design, which seeks the most discriminative spatially regularised feature channels. A further speed up is achieved by an adaptive initialisation of the filter optimisation process. The significantly increased convergence of the DCF filter is demonstrated by establishing the optimisation process equivalence with a continuous dynamical system for which the convergence properties can readily be derived. The experimental results obtained on several well-known benchmarking datasets demonstrate the efficiency and robustness of the proposed ACFT method, with a tracking accuracy comparable to the start-of-the-art trackers. (C) 2020 Elsevier Ltd. All rights reserved.

Rüdin's 1916 Monograph: On the Inheritance and Primary Origin of Dementia Praecox

In 1916, Ernst Rüdin published the first modern family study in the history of psychiatric genetics, the major goal of which was to test whether the pattern of risk in the siblings of dementia praecox (DP) probands followed Mendelian expectations. He utilized systematic ascertainment of probands and multisourced diagnostic assessments of probands and relatives, applying the narrow Kraepelinian concept of DP. In a novel step, he collaborated closely with a statistical geneticist-Wilhelm Weinberg-and applied his sibling, proband, and age correction methods. In his key sample-701 sibships when neither parent had DP-the morbid risk for DP in siblings was 4.48%, much lower than 25% expected for a recessive disorder. Risk for DP was increased by alcoholism or other mental disorders in parents. Other non-DP psychoses were common in both siblings and parents of DP probands. Rüdin discussed several alternative genetic models for DP including a 2-locus recessive, incomplete penetrance, and an oligogenic model. The high rates of other psychoses and psychopathic personalities in relatives might arise, he suggested, because these disorders shared genetic risks with DP. Rüdin established that DP, when carefully studied, ran in families, did not have a simple Mendelian genetic transmission pattern, and appeared likely to be genetically related to other non-DP psychotic disorders and perhaps some kinds of psychopathic personalities. This study, the most important in Rüdin's career, should be viewed in the context of his later extensive support of and collaboration with Nazi eugenic policies.

Niche width predicts extinction from climate change and vulnerability of tropical species

Climate change may be a major threat to global biodiversity, especially to tropical species. Yet, why tropical species are more vulnerable to climate change remains unclear. Tropical species are thought to have narrower physiological tolerances to temperature, and they have already experienced a higher estimated frequency of climate-related local extinctions. These two patterns suggest that tropical species are more vulnerable to climate change because they have narrower thermal niche widths. However, no studies have tested whether species with narrower climatic niche widths for temperature have experienced more local extinctions, and if these narrower niche widths can explain the higher frequency of tropical local extinctions. Here, we test these ideas using resurvey data from 538 plant and animal species from 10 studies. We found that mean niche widths among species and the extent of climate change (increase in maximum annual temperatures) together explained most variation (>75%) in the frequency of local extinction among studies. Surprisingly, neither latitude nor occurrence in the tropics alone significantly predicted local extinction among studies, but latitude and niche widths were strongly inversely related. Niche width also significantly predicted local extinction among species, as well as among and (sometimes) within studies. Overall, niche width may offer a relatively simple and accessible predictor of the vulnerability of populations to climate change. Intriguingly, niche width has the best predictive power to explain extinction from global warming when it incorporates coldest yearly temperatures.

Semaglutide for the treatment of overweight and obesity: A review

Obesity is a chronic, relapsing disease associated with multiple complications and a substantial morbidity, mortality and health care burden. Pharmacological treatments for obesity provide a valuable adjunct to lifestyle intervention, which often achieves only limited weight loss that is difficult to maintain. The Semaglutide Treatment Effect in People with obesity (STEP) clinical trial programme is evaluating once-weekly subcutaneous semaglutide 2.4 mg (a glucagon-like peptide-1 analogue) in people with overweight or obesity. Across STEP 1, 3, 4 and 8, semaglutide 2.4 mg was associated with mean weight losses of 14.9%-17.4% in individuals with overweight or obesity without type 2 diabetes from baseline to week 68; 69%-79% of participants achieved ≥10% weight loss with semaglutide 2.4 mg (vs. 12%-27% with placebo) and 51%-64% achieved ≥15% weight loss (vs. 5%-13% with placebo). In STEP 5, mean weight loss was -15.2% with semaglutide 2.4 mg versus -2.6% with placebo from baseline to week 104. In STEP 2 (individuals with overweight or obesity, and type 2 diabetes), mean weight loss was -9.6% with semaglutide 2.4 mg versus -3.4% with placebo from baseline to week 68. Improvements in cardiometabolic risk factors, including high blood pressure, atherogenic lipids and benefits on physical function and quality of life were seen with semaglutide 2.4 mg. The safety profile of semaglutide 2.4 mg was consistent across trials, primarily gastrointestinal adverse events. The magnitude of weight loss reported in the STEP trials offers the potential for clinically relevant improvement for individuals with obesity-related diseases.

An investigation of the impact of the Global Gag Rule on women's sexual and reproductive health outcomes in Uganda: a difference-in-differences analysis

In 2017, the Trump administration reinstated the Global Gag Rule (GGR), making non-U.S. non-governmental organisations ineligible for US government global health assistance if they provide access to or information about abortion. Little is known about the impact of the Trump administration's GGR on women's outcomes. Data for this analysis come from a panel of women surveyed in 2018 and 2019 in Uganda (n = 2755). We also used data from meetings with key stakeholders to create a detailed measure of exposure to the GGR within Uganda, classifying districts as more or less exposed to the GGR. Multivariable regression models were used to assess changes in contraceptive use, all births, unplanned births, and abortion from before to during implementation of the GGR. Difference-in-differences (DID) estimates were determined by calculating predicted probabilities from interaction terms for exposure/survey round. Descriptive analyses showed long-acting reversible contraceptive use increased more rapidly among women in less exposed districts after GGR implementation. DID estimates for contraceptive use were small. We observed a DID estimate of 3.5 (95% CI -0.9, 7.9) for all births and 2.9 (95% CI -0.2, 6.0) for unplanned births for women in more exposed districts during the period the policy was in effect. Our results suggest that the GGR may have attenuated Uganda's recent progress in improving SRHR outcomes, with women in less exposed districts continuing to benefit from this progress, while previously increasing trends for women in more exposed districts levelled off. Although the GGR was rescinded in January 2021, the impact of these disruptions may be felt for years to come.

Delayed access of low body weight-selected chicks to food at hatch is associated with up-regulated pancreatic glucagon and glucose transporter gene expression

Chickens selected for low (LWS) and high (HWS) juvenile body weight (BW) for 55 generations differ in BW by 10-fold at selection age. High (HWR) and low (LWR) body weight-relaxed lines have been random-bred since the 46th generation. Our objective was to evaluate the developmental and nutritional regulation of pancreatic mRNA abundance of pancreatic and duodenal homeobox 1 (PDX1), preproinsulin (PPI), preproglucagon (PPG), and glucose transporter 2 (GLUT2). At day of hatch (DOH) and days 1, 3, 7, and 15 (D1, 3, 7 and 15, respectively), pancreas was collected and real time PCR was performed in Experiment 1. In Experiment 2, HWS and LWS were fed or delayed access to food for 72 h post-hatch, and pancreas collected at D15. There was an interaction of line and age for GLUT2 (P=0.001), PPI (P<0.0001), PPG (P=0.034), and PDX1 (P<0.0001). Expression was greater in chicks from LWR and LWS than HWR and HWS. There was an interaction of line and nutrition on PPG (P<0.0001) and GLUT2 (P=0.001) mRNA, where expression was similar among chicks that were fed but greater in LWS than HWS when chicks were delayed access to food. Thus, the first two weeks is important for maturation of pancreatic endocrine function. Long-term selection for BW is associated with differences in pancreas development, and delaying access to food at hatch may have persisting effects on glucose regulatory function.

Sustainable Development Assessment of Cultural and Creative Industries in Casino Cities: A Case Study of Macao

In Macao, the government established the Cultural and Creative Industry Promotion Office and Cultural Industries Committee in 2010, which nominated eight to-be-developed cultural and creative industries (CCIs): design, visual arts, performing arts, clothing, pop music, film and video, animation, and publishing. However, because each CCI has its unique pattern and environmental resources are very limited in Macao, an industrial chain analysis for these eight industries should be conducted prior to policy implementation. Therefore, this study organized an industrial feasibility analysis for these eight CCIs. The methodologies included in-depth interviews, a literature analysis, and knowledge-discovery in databases. On the other hand, this study adopted the concept of creative industries, "the relationship between production and reproduction", and "the three-circle hypothetical interactive consumption" model for positioning these eight CCIs to choose existing industries in Macao, such as the exhibition, gambling, and cultural tourism industries, that are likely to promote CCIs. Next, the orientations of these CCIs are determined. Finally, it is suggested that the performing arts, design, and visual arts industries should be prioritized currently, and the heritage management and digital media industries are advised as to-be-developed ones. In contrast, the clothing, pop music, film and video, animation, and publishing industries are not so beneficial for Macao's development.

Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval

Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination, and lighting conditions. The accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper, a local gradient hexa pattern is proposed that identifies the relationship among the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. The proposed descriptor effectively transforms these relationships into binary micropatterns discriminating inter-class facial images with optimal precision. The recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors, namely, local derivative pattern, local tetra pattern, multiblock local binary pattern, and local vector pattern over the most challenging and benchmark facial image databases, i.e., Cropped Extended Yale B, CMU-PIE, color-FERET, LFW, and Ghallager database. The proposed descriptor has better recognition as well as retrieval rates compared with state-of-the-art descriptors.

Conditional motion in-betweening

Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given start and target poses while preserving the naturalness of the motion, such as periodic footstep motion while walking. Although state-of-the-art MIB methods are capable of producing plausible mo-tions given sparse key-poses, they often lack the controllability to generate motions satisfying the se-mantic contexts required in practical applications. We focus on the method that can handle pose or se-mantic conditioned MIB tasks using a unified model. We also present a motion augmentation method to improve the quality of pose-conditioned motion generation via defining a distribution over smooth tra-jectories. Our proposed method outperforms the existing state-of-the-art MIB method in pose prediction errors while providing additional controllability. Our code and results are available on our project web page: https://jihoonerd.github.io/Conditional- Motion- In- Betweening . (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Applying the food multimix concept for sustainable and nutritious diets

Despite a rich and diverse ecosystem, and biodiversity, worldwide, more than 2 billion people suffer from micronutrient malnutrition or hidden hunger. Of major concern are a degradation of our ecosystems and agricultural systems which are thought to be unsustainable thereby posing a challenge for the future food and nutrition security. Despite these challenges, nutrition security and ensuring well balanced diets depend on sound knowledge and appropriate food choices in a complex world of plenty and want. We have previously reported on how the food multimix (FMM) concept, a food-based and dietary diversification approach can be applied to meet energy and micronutrient needs of vulnerable groups through an empirical process. Our objective in this paper is to examine how the concept can be applied to improve nutrition in a sustainable way in otherwise poor and hard-to-reach communities. We have reviewed over 100 FMM food recipes formulated from combinations of commonly consumed traditional candidate food ingredients; on average five per recipe, and packaged as per 100 g powders from different countries including Ghana, Kenya, Botswana, Zimbabawe and Southern Africa, India, Mexico, Malaysia and the UK; and for different age groups and conditions such as older infants and young children, pregnant women, HIV patients, diabetes and for nutrition rehabilitation. Candidate foods were examined for their nutrient strengths and nutrient content and nutrient density of recipes per 100 g were compared with reference nutrient intakes for the different population groups. We report on the nutrient profiles from our analysis of the pooled and age-matched data as well as sensory analysis and conclude that locally produced FMM foods can complement local diets and contribute significantly to meet nutrient needs among vulnerable groups in food-insecure environments.

Unabridged adjacent modulation for clothing parsing

Clothing parsing has made tremendous progress in the domain of computer vision recently. Most state-ofthe-art methods are based on the encoder-decoder architecture. However, the existing methods mainly neglect problems of feature uncalibration within blocks and semantics dilution between blocks. In this work, we propose an unabridged adjacent modulation network (UAM-Net) to aggregate multi-level features for clothing parsing. We first build an unabridged channel attention (UCA) mechanism on feature maps within each block for feature recalibration. We further design a top-down adjacent modulation (TAM) for decoder blocks. By deploying TAM, high-level semantic information and visual contexts can be gradually transferred into lower-level layers without loss. The joint implementation of UCA and TAM ensures that the encoder has an enhanced feature representation ability, and the low-level features of the decoders contain abundant semantic contexts. Quantitative and qualitative experimental results on two challenging benchmarks (i.e., colorful fashion parsing and the modified fashion clothing) declare that our proposed UAM-Net can achieve competitive high-accurate performance with the state-of-the-art methods. The source codes are available at: https://github.com/ctzuo/UAM-Net .(c) 2022 Elsevier Ltd. All rights reserved.

Breathing pattern and its evaluation by muscle dynamometer md03

The purpose of the study was to determine the proportion of engagements of individual breathing sectors during 1-min physically active breathing at rest in 163 healthy, physically active participants (students of Physical Education and Sport). The research analyzed breathing movements through the muscle dynamometer MD03 (Hitron, Plzeň, Czech Republic). The proportion of engagements of the individual breathing sector in the group analyzed was determined based on measurement results. The lower breathing sector was engaged at 29.2%, the middle breathing sector at 31.0%, and the upper breathing sector at 39.8%. The largest observed difference between the involvement of individual breath sectors was 10.6% between the lower and upper breathing sectors. The muscle dynamometer MD03 may be instrumental for practising both localized breathing and full breath.

Gradient-based global features for seam carving

We propose a gradient-based global feature and its application to seam carving. We focus on areas, rather than points and lines, to be assigned as important elements for expressing the rough location of salient objects in an image. The proposed feature is calculated with a low computational load based on gray-scale intensity. The superior performance of the proposed gradient-based global feature, as compared to state-of-the-art salient features for seam carving, is demonstrated experimentally.

Optimization of cervical cage and analysis of its base material: A finite element study

Nowadays, cervical disorders are common due to human lifestyles. Accordingly, the cage design should be optimized as an essential issue. For an optimal design, an objective function is utilized to calculate the proper geometrical parameters. Additionally, the base material of the cage plays a key role in its functionality and final cost. Novel materials are currently introduced with more compatibility with the bone in terms of mechanical and chemical properties. In this study, a cervical cage was modeled based on PEEK material with three types of tooth designs on its surface. The cervical cage is assumed to be implanted between C6 and C7 vertebrae. The geometric parameters of the cage were optimized to minimize the mass by determining allowable stress and subsidence. The effect of complete cortical removal was investigated as a surgical mistake. Finally, a new composition of PEEK/titanium was introduced as the base material of the cage. Ansys 18.2 was used for FEA. The cage with a straight tooth was chosen due to its lower stress and subsidence compared with other designs. Furthermore, the optimized structures of all three tooth designs were determined. The mass and volume of the optimal cages were reduced by 41.47% and 41.52% respectively. Besides, complete cortical resection should not be carried out during fusion surgery, since it may lead to higher subsidence. The composition of PEEK/titanium was chosen as an appropriate base material due to its better performance compared with PEEK or titanium alone.

An efficient and low power one-lambda crosstalk avoidance code design for network on chips

Crosstalk faults occurring in wires of Networks on Chip (NoCs) can seriously threaten the reliability of data transfer. One efficient way to tackle crosstalk faults is numeral-based Crosstalk Avoidance Codes (CACs). Numeral-based CACs reduce crosstalk faults by preventing specific transition patterns to occur. One-Lambda Codes (OLCs) are the most efficient types of CACs. However, the codec of OLCs imposes overheads including power consumption, critical path and area occupation to the routers of NoCs. To find overhead-efficient OLCs, this paper proposes an Algorithm for Generating OLC Numeral systems (AGON). AGON provides a tradeoff for designers in selecting overhead-efficient OLCs. Using AGON, an efficient numeral-based OLC called Subtraction based-Numeral (Sub-Num) is proposed that benefits the Numeral system that can omit OLC-induced transition patterns completely. In addition, the mapping algorithm of Sub-Num can reduce the overheads of codec more efficiently than the other state-of-the art OLCs. Evaluation results using SPICE and VHDL simulations show that Sub-Num reduces power consumption and average delay of wires by 10% and 9%, and also overheads of codecs including dynamic power consumption, critical path and area occupation by 52%, 51% and 21%, respectively as compared to the state-of-the-art numeral-based OLC.

The Clinical Implications of the Academic Performance of the Siblings of Individuals With Autism Spectrum Disorder

We all know that autism spectrum disorder (ASD) can affect academic performance. Many children with autism face different challenges at school. However, less attention is paid to the siblings of autistic children, who are at a high risk of ASD or the broad autism phenotype (BAP). Recent data also shows that many siblings of ASD children suffer from neurodevelopmental disorders, mental health problems as well as poor academic performance. This review will look at the possible etiologies of the poor school performance of autistic children's siblings, with an emphasis on the challenges they face. We will also highlight the clinical implications of these findings, and the possible solutions that can help this vulnerable group.

Deep Gait Recognition: A Survey

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.

The development of the amnion in mice and other amniotes

The amnion is an extraembryonic tissue that evolutionarily allowed embryos of all amniotes to develop in a transient and local aquatic environment. Despite the importance of this tissue, very little is known about its formation and its molecular characteristics. In this review, we have compared the basic organization of the extraembryonic membranes in amniotes and describe the two types of amniogenesis, folding and cavitation. We then zoom in on the atypical development of the amnion in mice that occurs via the formation of a single posterior amniochorionic fold. Moreover, we consolidate lineage tracing data to better understand the spatial and temporal origin of the progenitors of amniotic ectoderm, and visualize the behaviour of their descendants in the extraembryonic-embryonic junctional region. This analysis provides new insight on amnion development and expansion. Finally, using an online-available dataset of single-cell transcriptomics during the gastrulation period in mice, we provide bioinformatic analysis of the molecular signature of amniotic ectoderm and amniotic mesoderm. The amnion is a tissue with unique biomechanical properties that deserves to be better understood. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.

Prediction Error Expansion (PEE) based Reversible polygon mesh watermarking scheme for regional tamper localization

This paper proposes a block based semi-fragile reversible authentication scheme that achieves regional localization for different classes of geometry and topology based mesh attacks. First of all, the model bounding volume is spatially partitioned into sub-volumes using Octree data structure and for each sub-volume, the embeddable units are computed. Each embeddable unit is comprises of three vertices and one bit can be embedded to an embeddable unit using a Prediction Error Histogram (PEH) shifting strategy. In PEH shifting, few of the embeddable units are expandable and few are shiftable. To compute PEH, Vertex Normal Value Ordering (VNVO) is performed and the maximum prediction error value is expanded. During PEH generation, to achieve sharper histograms and to get minimum numbers of shiftable units, adaptive bin-width selection step is also added. Verification of each block is performed by computing CRC-8 using vertex information from the block and embedding to the expandable units of the corresponding block itself. The proposed method could generate a sharper histogram than the state of the art methods and the proposed embedding function incurred very low distortion to mesh surface. It also outperforms the prior arts by achieving regional taper localization for both geometrical as well as topological attacks. The results analysis justifies the superiority of the proposed work than state of the art methods.

The challenges of growing orchids from seeds for conservation: An assessment of asymbiotic techniques

Lewis Knudson first successfully germinated orchid seeds asymbiotically on artificial medium in 1922. While many orchid species have since been grown asymbiotically, the tremendous variation in how species respond to artificial medium and growth conditions ex situ has also become apparent in the past century. In this study, we reviewed published journal articles on asymbiotic orchid seed germination to provide a summary of techniques used and to evaluate if these differ between terrestrial and epiphytic species, to identify areas where additional research is needed, and to evaluate whether asymbiotic germination could be used more often in ex situ conservation. We found articles reporting successful asymbiotic germination of 270 species and 20 cultivars across Orchidaceae. Researchers often used different techniques with epiphytic versus terrestrial species, but species-specific responses to growth media and conditions were common, indicating that individualized protocols will be necessary for most species. The widespread success in generating seedlings on artificial media suggests that asymbiotic techniques should be another tool for the conservation of rare orchid species. Further advances are needed in understanding how to introduce mycorrhizae to axenically grown orchids and to maximize the viability of seedlings reintroduced into natural habitats to fully utilize these methods for conservation.

Robust online video super-resolution using an efficient alternating projections scheme

Video super-resolution reconstruction (SRR) algorithms attempt to reconstruct high-resolution (HR) video sequences from low-resolution observations. Although recent progress in video SRR has significantly improved the quality of the reconstructed HR sequences, it remains challenging to design SRR algorithms that achieve good quality and robustness at a small computational complexity, being thus suitable for online applications. In this paper, we propose a new adaptive video SRR algorithm that achieves state-of-the-art performance at a very small computational cost. Using a nonlinear cost function constructed considering characteristics of typical innovation outliers in natural image sequences and an edge-preserving regularization strategy, we achieve state-of-the-art reconstructed image quality and robustness. This cost function is optimized using a specific alternating projections strategy over non-convex sets that is able to converge in a very few iterations. An accurate and very efficient approximation for the projection operations is also obtained using tools from multidimensional multirate signal processing. This solves the slow convergence issue of stochastic gradient-based methods while keeping a small computational complexity. Simulation results with both synthetic and real image sequences show that the performance of the proposed algorithm is similar or better than state-of-the-art SRR algorithms, while requiring only a small fraction of their computational cost. (C) 2020 Elsevier B.V. All rights reserved.

Spatiotemporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

In autonomous vehicles (AVs), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them less suitable for deployment on AV edge hardware. To address these limitations, we propose SG2VEC, a spatiotemporal scene-graph embedding methodology that uses the graph neural network (GNN) and long short-term memory (LSTM) layers to predict future collisions via visual scene perception. We demonstrate that SG2VEC predicts collisions 8.11% more accurately and 39.07% earlier than the state-of-the-art method on synthesized data sets, and 29.47% more accurately on a challenging real-world collision data set. We also show that SG2VEC is better than the state of the art at transferring knowledge from synthetic data sets to real-world driving data sets. Finally, we demonstrate that SG2VEC performs inference 9.3x faster with an 88.0% smaller model, 32.4% less power, and 92.8% less energy than the state-of-the-art method on the industrystandard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.

COVID-19, climate change, and the finite pool of worry in 2019 to 2021 Twitter discussions

Climate change mitigation has been one of the world's most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past 2 y. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 to 2021. Consistent with the "finite pool of worry" hypothesis both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of "climate change" tweets. Climate change discussion on Twitter decreased, despite 1) a larger Twitter daily active usage in 2020 and 2021, 2) greater coverage of climate change in the traditional media in 2021, 3) a larger number of North Atlantic Ocean hurricanes, and 4) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of climate change tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.

FeMV is a cathepsin-dependent unique morbillivirus infecting the kidneys of domestic cats

Feline morbillivirus (FeMV) is a recently discovered pathogen of domestic cats and has been classified as a morbillivirus in the Paramyxovirus family. We determined the complete sequence of FeMVUS5 directly from an FeMV-positive urine sample without virus isolation or cell passage. Sequence analysis of the viral genome revealed potential divergence from characteristics of archetypal morbilliviruses. First, the virus lacks the canonical polybasic furin cleavage signal in the fusion (F) glycoprotein. Second, conserved amino acids in the hemagglutinin (H) glycoprotein used by all other morbilliviruses for binding and/or fusion activation with the cellular receptor CD150 (signaling lymphocyte activation molecule [SLAM]/F1) are absent. We show that, despite this sequence divergence, FeMV H glycoprotein uses feline CD150 as a receptor and cannot use human CD150. We demonstrate that the protease responsible for cleaving the FeMV F glycoprotein is a cathepsin, making FeMV a unique morbillivirus and more similar to the closely related zoonotic Nipah and Hendra viruses. We developed a reverse genetics system for FeMVUS5 and generated recombinant viruses expressing Venus fluorescent protein from an additional transcription unit located either between the phospho-protein (P) and matrix (M) genes or the H and large (L) genes of the genome. We used these recombinant FeMVs to establish a natural infection and demonstrate that FeMV causes an acute morbillivirus-like disease in the cat. Virus was shed in the urine and detectable in the kidneys at later time points. This opens the door for long-term studies to address the postulated role of this morbillivirus in the development of chronic kidney disease.

Fast and Reversibly Humidity-Responsive Fluorescence Based on AIEgen Proton Transfer

The construction of humidity-responsive fluorescent materials with reversibility, specificity, and sensitivity is of great importance for the development of information encryption, fluorescence patterning, and sensors. Nevertheless, to date, the application of these materials has been limited by their slow response rate and nonspecificity. Herein, a humidity-responsive fluorescence system was designed and assembled to achieve a rapid, reversible, and specific moisture response. The system comprised tetra-(4-pyridylphenyl)ethylene (TPE-4Py) as a fluorescent proton acceptor with an aggregation-induced emission (AIE) effect and poly(acrylic acid) (PAA) as a proton donor with an efficient moisture-capturing ability. The fluorescence color and intensity rapidly changed with increasing relative humidity (RH) because of TPE-4Py protonation, and TPE-4Py deprotonation resulted in recovery of the original fluorescence color in low-humidity environments. The proton transfer between the pyridyl group in TPE-4Py and the carboxyl group in PAA was reversible and chemically stable, and the humidity-responsive fluorescence system showed a high response/recovery speed, an obvious color change, good reversibility, and an outstanding specific moisture response. Because of these advantages, diverse applications of this humidity-responsive fluorescence system in transient fluorescent patterning and the encryption of information were also developed and demonstrated.

Staying connected: smartphone acceptance and use level differences of older adults in China

In recent years, an increasing number of studies have addressed the older adults' Information and Communication Technology acceptance, the majority of which concentrate on the use of computers and the internet. As smartphone use becomes further integrated into older adults' daily lives, it is important to investigate how perceptions about and use of smartphones intersect. This study (1) proposes an extended Technology Acceptance Model and tests the relationships between Perceived Usefulness, Perceived Ease of Use, Attitude, Behavioural Intention, Self-efficacy, Technology Anxiety, and Social Support in older adults' smartphone use by confirmatory factor analysis (CFA); and (2) analyses the specific differences between primary, medium, and advanced use level groups in each construct by Q-cluster and ANOVAs. We conduct a community-based survey with a sample of 1,006 older adults in East China. The data demonstrate that the extended model offered a good explanation of smartphone acceptance among the older adults, and the groups belong to different use levels show significant difference in all constructs. The findings indicate that digital divide is objectively inevitable in smartphone use, but the older adults are extremely diverse groups that do not uniformly conform to technology averse stereotypes.

Damping-Based Droop Control Strategy Allowing an Increased Penetration of Renewable Energy Resources in Low-Voltage Grids

The increased penetration of renewable distributed energy sources increases the challenges for distribution systems operators to keep the voltage variations within the prescribed limits. One of the voltage problems is caused by single-phase systems leading to voltage unbalance. Another problem is overvoltages caused by local injection of distributed energy sources. An effective solution for this problem is by active power curtailment. In this paper, a control strategy is proposed that mitigates voltage unbalance by using active power and it is equipped with a linear active power curtailment solution. The effect of the proposed control strategy and three state-of-the-art control strategies on the voltage profile and the curtailed power are studied. It can be concluded that the proposed control strategy has a beneficial effect on the voltage profile along a feeder and leads to less power curtailment compared to the state-of-the-art solutions. The proposed control strategy thus allows an increased penetration of distributed energy sources in the low-voltage grid.

Robust Stereo Data Cost With a Learning Strategy

The performance of stereo matching algorithms strongly depends on the quality of the stereo data/matching cost. Most state-of-the-art data costs require expert knowledge for the design of a transformation function, such as census for handling gray-level changes monotonically, adaptive normalized cross correlation for handling Lambertian cases, guided filtering for preserving edge information, and local density encoding for handling illumination differences. However, it is difficult to design a complex transformation function to handle unknown factors that often occur in driving conditions such as snow, rain, and sun. Therefore, this paper has investigated the deep learning strategy to develop a novel stereo matching cost model without using much expert knowledge. Experimental results show that the proposed deep learning model obtains better results than the state-of-the-art stereo matching cost as judged by the standard KITTI benchmark, Middlebury, and HCI datasets.

Susceptibility of TNFAIP8, TNFAIP8L1, and TNFAIP2 Gene Polymorphisms on Cancer Risk: A Comprehensive Review and Meta-Analysis of Case-Control Studies

Objectives: The TNFAIP8 gene family and TNFAIP2 gene are inextricably linked to an elevated risk of cancer development. This systemic review and meta-analysis seeks to establish the relationship between TNFAIP8 (rs11064, rs1045241, rs1045242, and rs3813308), TNFAIP8L1 (rs1060555), and TNFAIP2 (rs710100 and rs8126) polymorphisms with the risk of cancer. Methods and Materials: A systematic search of multiple databases from January 2022 to April 2022 was used to identify relevant studies. Odds ratios (ORs) with corresponding 95% CI and p-value were calculated to assess the association. Bonferroni correction was performed to correct p-values. Trial sequential analysis (TSA) and in-silico messenger RNA expression were also performed. Review Manager 5.4 software was used for performing this meta-analysis. Results: This study comprised 6909 cancer patients and 7087 healthy participants from 14 studies. Four genetic models of rs11064 (codominant 2 [COD2]: OR = 2.30, p = 7.83 × 10-5; codominant 3 [COD3]: OR = 2.10, p = .0006; recessive model [RM]: OR = 2.24, p = .0001; AC: OR = 1.47, p = .037), two genetic models of rs1045241 (codominant 1 [COD1]: OR = 1.27, p = .009; overdominant model [ODM]: OR = 1.24, p = .018), four genetic models of rs1045242 (COD1: OR = 1.52, p = .005; dominant model (DM): OR = 1.56, p = .002; OD: OR = 1.48, p = .008; AC: OR = 1.48, p = .002), and three genetic models of rs8126 (COD2: OR = 1.41, p = .0005; COD3: OR = 1.44, p = .0002; RM: OR = 1.43, p = .0001) were statistically linked to cancer risk. Only one genetic model of rs1060555 polymorphism showed a significant protective association with cancer (COD2: OR = 0.80, p = .048). The outcomes of TSA also validated the findings of the meta-analysis. Conclusion: This study summarizes that rs11064, rs1045241, and rs1045242 polymorphisms of TNFAIP8 gene and rs8126 polymorphism of TNFAIP2 gene are significantly linked with the risk of cancer development. This meta-analysis was registered at INPLASY (registration number: INPLASY202270073).

Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style sources such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology. Our code is available at https://github.com/rikiyay/style-transfer-for-digital-pathology.

Neuronal Ganglioside and Glycosphingolipid (GSL) Metabolism and Disease : Cascades of Secondary Metabolic Errors Can Generate Complex Pathologies (in LSDs)

Glycosphingolipids (GSLs) are a diverse group of membrane components occurring mainly on the surfaces of mammalian cells. They and their metabolites have a role in intercellular communication, serving as versatile biochemical signals (Kaltner et al, Biochem J 476(18):2623-2655, 2019) and in many cellular pathways. Anionic GSLs, the sialic acid containing gangliosides (GGs), are essential constituents of neuronal cell surfaces, whereas anionic sulfatides are key components of myelin and myelin forming oligodendrocytes. The stepwise biosynthetic pathways of GSLs occur at and lead along the membranes of organellar surfaces of the secretory pathway. After formation of the hydrophobic ceramide membrane anchor of GSLs at the ER, membrane-spanning glycosyltransferases (GTs) of the Golgi and Trans-Golgi network generate cell type-specific GSL patterns for cellular surfaces. GSLs of the cellular plasma membrane can reach intra-lysosomal, i.e. luminal, vesicles (ILVs) by endocytic pathways for degradation. Soluble glycoproteins, the glycosidases, lipid binding and transfer proteins and acid ceramidase are needed for the lysosomal catabolism of GSLs at ILV-membrane surfaces. Inherited mutations triggering a functional loss of glycosylated lysosomal hydrolases and lipid binding proteins involved in GSL degradation cause a primary lysosomal accumulation of their non-degradable GSL substrates in lysosomal storage diseases (LSDs). Lipid binding proteins, the SAPs, and the various lipids of the ILV-membranes regulate GSL catabolism, but also primary storage compounds such as sphingomyelin (SM), cholesterol (Chol.), or chondroitin sulfate can effectively inhibit catabolic lysosomal pathways of GSLs. This causes cascades of metabolic errors, accumulating secondary lysosomal GSL- and GG- storage that can trigger a complex pathology (Breiden and Sandhoff, Int J Mol Sci 21(7):2566, 2020).

An Efficient Solution to Non-Minimal Case Essential Matrix Estimation

Finding relative pose between two calibrated images is a fundamental task in computer vision. Given five point correspondences, the classical five-point methods can be used to calculate the essential matrix efficiently. For the case of N (N > 5) inlier point correspondences, which is called N-point problem, existing methods are either inefficient or prone to local minima. In this paper, we propose a certifiably globally optimal and efficient solver for the N-point problem. First we formulate the problem as a quadratically constrained quadratic program (QCQP). Then a certifiably globally optimal solution to this problem is obtained by semidefinite relaxation. This allows us to obtain certifiably globally optimal solutions to the original non-convex QCQPs in polynomial time. The theoretical guarantees of the semidefinite relaxation are also provided, including tightness and local stability. To deal with outliers, we propose a robust N-point method using M-estimators. Though global optimality cannot be guaranteed for the overall robust framework, the proposed robust N-point method can achieve good performance when the outlier ratio is not high. Extensive experiments on synthetic and real-world datasets demonstrated that our N-point method is 2 similar to 3 orders of magnitude faster than state-of-the-art methods. Moreover, our robust N-point method outperforms state-of-the-art methods in terms of robustness and accuracy.

Investigating the impact of problem-oriented sustainability education on students' identity: A comparative study of planning and liberal arts students

This study uses a grounded theory lite approach to investigate the changes in identity of planning and liberal arts students who studied sustainability in a problem-oriented environment. It was found that although the students expressed a moral identity in relation to the environment, that was not translated into shifting beliefs and behaviours. The authors conceptualised an identity dissonance between aspirational moral identities and implicit socialized western middle-class identities and identified an array of coping mechanisms that enabled students to maintain these conflicting identities. Where the planning students primarily utilized threat reduction, bargaining, and hope for technological salvation, the liberal arts students tended towards shifting blame, fatalism, and limited engagement. The differences between the groups were explained in terms of disciplinary orientation and the differences in the pedagogical approach. In response, the authors recommended a more comprehensive, hands-on environmental educational approach geared towards building environmental identities. (c) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.

Fuzzing: State of the Art

As one of the most popular software testing techniques, fuzzing can find a variety of weaknesses in a program, such as software bugs and vulnerabilities, by generating numerous test inputs. Due to its effectiveness, fuzzing is regarded as a valuable bug hunting method. In this paper, we present an overview of fuzzing that concentrates on its general process, as well as classifications, followed by detailed discussion of the key obstacles and some state-of-the-art technologies which aim to overcome or mitigate these obstacles. We further investigate and classify several widely used fuzzing tools. Our primary goal is to equip the stakeholder with a better understanding of fuzzing and the potential solutions for improving fuzzing methods in the spectrum of software testing and security. To inspire future research, we also predict some future directions with regard to fuzzing.

From cave geomorphology to Palaeolithic human behaviour: speleogenesis, palaeoenvironmental changes and archaeological insight in the Atxurra-Armina cave (northern Iberian Peninsula)

A detailed geomorphological study was performed in the Atxurra-Armina cave system (northern Iberian Peninsula) to decode landscape evolution, palaeoenvironmental changes and human use of a cave within an Inner Archaeological Context. The results show an average incision rate of the river of <0.083 mm a(-1)for at least the last 419 ka, with interruptions due to sedimentary inputs. Moreover, allostratigraphic units comprising fluviokarstic deposits at the base and flowstone formation at the top have been shown to be climatically controlled, formed either during glacial-interglacial cycles or during interstadial cycles. Finally, when the cave was used by humans in the Late Magdalenian, the lower entrance was closed, and they must therefore have entered the cave through the upper entrance. To reach the sectors selected to decorate the panels, they probably travelled from the upper cave level, as the current crawlway was wider than today, according to our U/Th dating. Once these visitors reached the panels, the floor in the main gallery would have been around 15 cm lower than at present. However, the morphology of the conduit was similar; this has significant implications for understanding and interpreting the human use of the cave during the Palaeolithic.

Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. On the other hand, reducing the radiation dose leads to increased noise and artifacts, which adversely degrades the scan's interpretability. In recent times, the deep learning-based technique has emerged as a promising method for low dose CT(LDCT) denoising. However, some common bottleneck still exists, which hinders deep learning-based techniques from furnishing the best performance. In this study, we attempted to mitigate these problems with three novel accretions. First, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Our proposed module assisted in boosting the denoising by a significant margin. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising. The loss mentioned above also assisted to alleviate the laborious effort required while training CT denoising network using image patches. Lastly, we propose a novel discriminator function for CT denoising tasks. The conventional vanilla discriminator tends to overlook the fine structural details and focus on the global agreement. Our proposed discriminator leverage self-attention and pixel-wise GANs for restoring the diagnostic quality of LDCT images. Our method validated on a publicly available dataset of the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge performed remarkably better than the existing state of the art method. The corresponding source code is available at: https://github.com/reach2sbera/ldct_nonlocal

Cattle without herdsmen: Animal and human beings in the prehistoric rock art of the Western Sahara

Because of the scarcity and discontinuity of the archaeological excavations in the Western Sahara, rock-art appears to be one of the most relevant sources of information in order to discuss the subsistence, technology and ideology of the neolithic communities. However, in contrast to what we would expect from the evident pastoral scenes depicted on the Central Saharan rock art, the Western Saharan art of Neolithic age is dominated by wild or isolated animals. The pastoral scenes always are more uncertain to describe than the hunting scenes, which are also marginal but easier to interpret. In the current state of the research, the nature of the Western Saharan Neolithic, the putative age of the images, and the type of information expected from rock art should be questioned. (C) 2015 Elsevier Ltd and INQUA. All rights reserved.

Artificial aging induced changes in biochar,s properties and Cd2+ adsorption behaviors

Fresh biochar has been widely applied to the remediation of heavy metals in soil by its property of adsorption, but the changes in its physicochemical properties and in situ adsorption performance over time cannot be ignored. In this study, the sorption of Cd2+ by corn straw biochars (CB) and municipal sludge biochars (SB) produced at 350 °C and 650 °C before and after H2O2 oxidation, and dry-wet and freeze-thaw aging were investigated using batch sorption experiments. The changes of physicochemical properties of biochar before and after aging were analyzed by various characterization methods. Based on these results, the impact of aging on the Cd2+ adsorption behavior could be clarified, which showed that CB650 was able to display the highest adsorption capacity in fresh biochars. Aging treatments reduced the ash content and pH value of CB, and significantly diminished the adsorption performance of Cd2+. These changes indicated that precipitation was a critical factor in the adsorption of Cd2+ on CB. The adsorption capacity of SB was enhanced after H2O2 oxidation, but weakened after dry-wet and freeze-thaw aging. This was closely related to the increase or decrease in the content of oxygen-containing functional groups, which in turn enhanced or inhibited its ability to compound with heavy metals. These results are of great significance for evaluating its long-term application prospects in the natural environment.

Manipulating Oxygen Vacancies by K+ Doping and Controlling Mn2+ Deposition to Boost Energy Storage in β-MnO2

Aqueous zinc-ion batteries (ZIBs) have gained wide attention for their low cost, high safety, and environmental friendliness in recent years. β-MnO2, a potential cathode material for ZIBs, has been restricted by its small channels for efficient charge storage. Herein, β-MnO2 nanorods with oxygen vacancies are fabricated by a K+-doping strategy to improve the performance of ZIBs. The assembled batteries exhibit a capacity of 468 mAh g-1, a power density of 2605 W kg-1, and an energy density of 179 Wh kg-1, which outperforms most reported ZIBs. Such a performance is owing to the synergistic combination of the oxygen vacancies in β-MnO2 and concurrent deposition of ε-MnO2 from Mn2+ in the electrolyte. Furthermore, superior cycling stability with negligible capacity decay in these batteries is demonstrated over 1000 cycles at a high current of 2 A g-1. This study reveals the importance of oxygen vacancies and Mn2+ deposition effect in understanding the mechanism of charge storage in MnO2-based ZIBs.

Radar target tracking via robust linear filtering

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.

RESPIRE plus plus : Robust Indoor Sensor Placement Optimization Under Distance Uncertainty

Sensor placement in wireless sensor networks (WSN) aims to maximize coverage while minimizing total deployment cost. However, existing coverage-only approaches do not consider the robustness of the entire system where sensors may break down or malfunction. In this paper, we first propose a robustness-aware sensor placement approach by constructing a multi-objective optimization model. Our experiments demonstrate that this method increases the robustness of a WSN by up to 50%, with 201% higher probability of monitoring the entire environment as compared to the state-of-the-art coverage-only approach. The paper further improves the proposed method by introducing a robust optimization based sensor placement approach which considers the distance uncertainty between a sensor and a target. We show that this improved model increases the probability of target detection by up to 77% compared to state-of-the-art coverage-only approach.

In vitro antimicrobial effect of essential tea tree oil( Melaleuca alternifolia), thymol, and carvacrol on microorganisms isolated from cases of bovine clinical mastitis

Both Gram-negative and Gram-positive bacteria have recently developed antibiotic resistance to treatments for bovine mastitis, creating a serious concern for public and animal health. The objective of this study was to analyse in vitro microbicidal activity of tea tree oil, thymol and carvacrol (composed of oregano and thyme essential oils) on bacteria isolated from clinical mastitis. Field isolates and ATCC strains of the Staphylococcus spp, Streptococcus spp, Escherichia coli, Klebsiella pneumoniae, and Candida albicans genera were analysed. The agar diffusion technique was used to test bactericidal susceptibility and plate microdilution was utilized to determine the minimum inhibitory, bactericidal, and fractional inhibitory concentrations. Thymol alone and the combinations of thymol-carvacrol and thymol-TTO obtained the highest inhibition diameters for Gram-negative bacteria, while for Gram-positive bacteria and C. albicans, thymol and the combination thymol-carvacrol obtained the highest indices. TTO, thymol, and carvacrol had MIC values of 1.56-25 mg/ml, 0.05-0.4 mg/ml, and 0.02-0.2 mg/ml, respectively. CMB results for the Gram-negative and gram-positive groups were 0.39-0.78 mg/ml, and for C. albicans, 0.78-1.56 mg/ml. Results for the fractional inhibitory concentrations show that the TTO+thymol and thymol+carvacrol combinations had additive activity against groups of Gram-negative bacteria and C. albicans. These natural components, evaluated individually and in combinations, have an effectiveness above 70%.

From Childhood Maltreatment to Allostatic Load in Adulthood: The Role of Social Support

Although previous research has documented that social support acts as a protective factor for individuals exposed to trauma, most research relies on assessments of social support at one point in time. The present study used data from a prospective cohort design study to examine the stability of social support from childhood through middle adulthood in individuals with documented histories of childhood abuse and neglect and matched controls (aged 0-11) and assessed the impact of social support on allostatic load, a composite measure of physiological stress response assessed through blood tests and physical measurements, in middle adulthood. Maltreated children are more likely to have unstable social support across the life span, compared to matched controls. Social support across the life span partially mediated the relationship between child maltreatment and allostatic load in adulthood, although there were differences by race and sex. These findings have implications for interventions to prevent the negative consequences of child maltreatment.

The evolution of the reference monetary value of the man.sievert at electricite de France

The reference monetary value of the man.sievert is a pragmatic decision-aiding technique to "take into account economic and societal factors" associated with the optimisation principle and to help decide whether a protection option is "reasonable" or not. EDF has adopted a system of reference monetary values in 1992, updated it in 2002, and was considering a new update. In 2019 and 2020, a designated EDF-CEPN working group investigated the elements (through survey, literature and feedback analysis) that might support a change and in which direction. A simplified system, based on one single reference monetary value of the man.sievert, has been proposed. The value takes into account the most recent recommendations from ICRP and French State administration and uses the state-of-the-art methodology in calculating the Value of Statistical Life and has been adjusted with an aversion risk factor considering the EDF radiation protection policy. The new reference value is 4,500 euro/man.mSv. An upper value of 7,000 euro/man.mSv can be used if the project presents radiation protection benefits (positive externalities) in addition to a reduction in collective dose. The Radiation Protection Manager makes the decision on which value should be selected, and the Radiation Protection Service, in collaboration with the other services, integrates the value in the optimization analysis, bearing in mind that the output will guide the decision (and not determine it) bringing also objectivity and transparency.

Accurate modeling of replication rates in genome-wide association studies by accounting for Winner's Curse and study-specific heterogeneity

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in genome-wide association studies has been well-studied in the context of Winner's Curse, which is the inflation of effect size estimates for significant variants due to statistical chance. However, Winner's Curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes genome-wide association studies and replication studies to jointly model Winner's Curse and study-specific heterogeneity due to confounding factors. We apply this framework to 100 genome-wide association studies from the Human Genome-Wide Association Studies Catalog and observe that there is a large range in the level of estimated confounding. We demonstrate how this framework can be used to distinguish when studies fail to replicate due to statistical noise and when they fail due to confounding.

Food security for survivors of intimate partner violence: Understanding the role of food in survivor well-being

Intimate partner violence (IPV) and food security are two leading public health issues that disproportionately impact women in the United States. Despite this connection, the relationship between IPV and food security has been relatively unexplored. While food security is a known factor in increased well-being, it is not often explicitly included in care for survivors. As part of a larger study on survivors of domestic violence who are receiving services from a domestic violence and sexual assault agency in the Southern United States, we analysed participant responses (n = 26) to various scales (i.e. depression, anxiety, PTSD, disability, well-being, hope, food security) to determine the relationships between mental health and food-related variables. Importantly, findings from our study show that survivors experience low food security at higher rates (53.8%) than the U.S. national average (11.5%). Additionally, the proportion of survivors in our sample who are receiving some form of food aid and remain food insecure is high (26.9%), leading to questions about the adequacy of food aid. Finally, our results underpin the relationship between food security and mental health for survivors, as low food security is positively correlated with depression, PTSD, disability, trouble concentrating, lack of hope and decreased well-being. These findings have implications for how we evaluate food security and the role it plays in well-being for survivors.

Elastic Fibers/Fabrics for Wearables and Bioelectronics

Wearables and bioelectronics rely on breathable interface devices with bioaffinity, biocompatibility, and smart functionality for interactions between beings and things and the surrounding environment. Elastic fibers/fabrics with mechanical adaptivity to various deformations and complex substrates, are promising to act as fillers, carriers, substrates, dressings, and scaffolds in the construction of biointerfaces for the human body, skins, organs, and plants, realizing functions such as energy exchange, sensing, perception, augmented virtuality, health monitoring, disease diagnosis, and intervention therapy. This review summarizes and highlights the latest breakthroughs of elastic fibers/fabrics for wearables and bioelectronics, aiming to offer insights into elasticity mechanisms, production methods, and electrical components integration strategies with fibers/fabrics, presenting a profile of elastic fibers/fabrics for energy management, sensors, e-skins, thermal management, personal protection, wound healing, biosensing, and drug delivery. The trans-disciplinary application of elastic fibers/fabrics from wearables to biomedicine provides important inspiration for technology transplantation and function integration to adapt different application systems. As a discussion platform, here the main challenges and possible solutions in the field are proposed, hopefully can provide guidance for promoting the development of elastic e-textiles in consideration of the trade-off between mechanical/electrical performance, industrial-scale production, diverse environmental adaptivity, and multiscenario on-spot applications.

In vitro and in vivo antiviral activity of nucleoside analogue cHPMPC against African swine fever virus replication

African swine fever virus (ASFV) causes a haemorrhagic disease affecting wild boar and domestic pigs which can result in morbidity and fatality rates of up to 100%. ASFV is a large double-stranded DNA virus which replicates predominantly in the cell cytoplasm and codes for its replication and transcription machinery. No vaccine is widely available and control depends on early detection, culling of infected herds and adherence to biosecurity measures. In this study the small molecule nucleoside analogue, cyclic cidofovir (cHPMPC), was evaluated for its ability to inhibit replication of four different ASFV genotypes in primary porcine macrophages. Time of addition studies demonstrated that cHPMPC effectively inhibits ASFV replication and late gene expression when added pre-infection or early post-infection but not when added at late times, suggesting the drug target may be the virus DNA polymerase, or the RNA polymerase involved in late transcription. Oral administration of cHPMPC delayed onset of clinical signs and significantly reduced viral titres in blood and tissues of treated pigs. These results indicate that cHPMPC is a promising compound for further development to control ASFV outbreaks.

Delayed Treatment for People Living with HIV in China, 2004-2016: An Analysis of An Observational Cohort

Early universal access to antiretroviral treatment (ART) is critical in the control of the HIV epidemic. However, prompt initiation of ART remains problematic in China. This study analyzed the late testing and lag time between HIV diagnosis and initiation of ART from 2004 to 2016 and identified the risk factors for delayed initiation of ART. Data from 16,957 people living with HIV were abstracted from a hospital electronic health record database and a case report database for AIDS prevention and control in Yunnan province. Reasons for delayed initiation of ART were categorized into late testing, defined as CD4 count of < 350 cells/mu L at baseline HIV diagnosis, and delayed access, defined as a lag time of > 1 month between the diagnosis and initiation of ART. Binary logistic regression models were used to identify risk factors for late testing and delayed access. The CD4 counts at diagnosis increased from 201 +/- 147 cells/mu L (mean +/- SD) in 2004 to 324 +/- 238 cells/mu L in 2016 (p = 0.024). The CD4 count was higher for persons < 45 years, unmarried, and men who have sex with men (MSM) (356, 357, and 409 cells/mu L, respectively) compared to their peers in 2016 (p < 0.05). The lag time from diagnosis to initiation of ART was significantly reduced from 59.2 months in 2004 to 0.9 months in 2016 (p < 0.05). The shorter lag time over the years was consistent when analysis was stratified by sex, age, marital status, and transmission routes, even though the lag time for people using drugs was longest in 2016 (> 2 months versus 0.82 and 0.72 month of heterosexuals and MSM, respectively). Compared to their peers, married persons (AOR = 0.63, 95%CI: 0.57, 0.69) were less likely to have delayed access to ART, and drugs-using patients (AOR = 3.58, 95%CI: 2.95,4.33) were more likely to have delayed access to ART. Late testing rather than delayed access to ART after a diagnosis remains problematic in China, although improvements have been seen for both parameters from 2004 to 2016. Our data highlight the importance of continued efforts to promote early diagnosis of HIV to prevent transmission, morbidity, and early mortality in HIV infection.

Comprehensive Analysis of Deep Learning-Based Vehicle Detection in Aerial Images

Vehicle detection in aerial images is a crucial image processing step for many applications such as screening of large areas as used for surveillance, reconnaissance, or rescue tasks. In recent years, several deep learning-based frameworks have been proposed for object detection. However, these detectors were developed for data sets that considerably differ from aerial images. In this paper, we systematically investigate the potential of fast R-CNN and faster R-CNN for aerial images, which achieve top performing results on common detection benchmark data sets. Therefore, the applicability of eight state-of-the-art object proposal methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations to account for the characteristics of the aerial images are provided. To overcome the shortcomings of the original approach in the case of handling small instances, we further propose our own networks that clearly outperform state-of-the-art methods for vehicle detection in aerial images. Furthermore, we analyze the impact of the different adaptations with respect to various ground sampling distances to provide a guideline for detecting small objects in aerial images. All experiments are performed on two publicly available data sets to account for differing characteristics such as varying object sizes, number of objects per image, and varying backgrounds.

Maternal Education Potentially Moderates the MAOA uVNTR Effects on Externalizing Behavior in Black South African Children

Interactions between the MAOA uVNTR and rearing environment are suggested to influence the developmental manifestations of childhood internalizing and externalizing behavior. However, few studies in the MAOA literature have included continental African children, or focused on non-clinical samples. We explored the main and interactive effects of the MAOA uVNTR (high and low activity alleles) in Black South African male (n = 478) and female (n = 540) children who were part of the longitudinal Birth to Twenty Plus cohort. Historical data on birth weight, gestational age at delivery, socioeconomic status, and maternal education were combined with genotypic information and analyzed using regression modeling. We found no significant main effects for the MAOA uVNTR on childhood behavior in either sex. A significant interaction (p = .04) was identified between MAOA and maternal education, suggesting that externalizing behavior in boys carrying a low activity MAOA allele varied in direct proportion to the education levels of their mothers. However, the model fit failed to reach significance, possibly due to our inclusion of only non-clinical pre-pubertal males. No significant interactions were detected for female children. Our findings lend tentative credibility to the Environmental Sensitivity metaframework, which suggests that MAOA is an important plasticity factor in childhood development.