Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole-genome single nucleotide polymorphism analysis for typing the pandemic pathogen Fusarium graminearum sensu stricto

Recent improvements in microbiology and molecular epidemiology were largely stimulated by whole- genome sequencing (WGS), which provides an unprecedented resolution in discriminating highly related genetic backgrounds. WGS is becoming the method of choice in epidemiology of fungal diseases, but its application is still in a pioneer stage, mainly due to the limited number of available genomes. Fungal pathogens often belong to complexes composed of numerous cryptic species. Detecting cryptic diversity is fundamental to understand the dynamics and the evolutionary relationships underlying disease outbreaks. In this study, we explore the value of whole-genome SNP analyses in identification of the pandemic pathogen Fusarium graminearum sensu stricto (F.g.). This species is responsible for cereal diseases and negatively impacts grain production worldwide. The fungus belongs to the monophyletic fungal complex referred to as F. graminearum species complex including at least sixteen cryptic species, a few among them may be involved in cereal diseases in certain agricultural areas. We analyzed WGS data from a collection of 99 F.g. strains and 33 strains representing all known cryptic species belonging to the FGSC complex. As a first step, we performed a phylogenomic analysis to reveal species-specific clustering. A RAxML maximum likelihood tree grouped all analyzed strains of F.g. into a single clade, supporting the clustering-based identification approach. Although, phylogenetic reconstructions are essential in detecting cryptic species, a phylogenomic tree does not fulfill the criteria for rapid and cost-effective approach for identification of fungi, due to the time-consuming nature of the analysis. As an alternative, analysis of WGS information by mapping sequence data from individual strains against reference genomes may provide useful markers for the rapid identification of fungi. We provide a robust framework for typing F.g. through the web-based PhaME workflow available at EDGE bioinformatics. The method was validated through multiple comparisons of assembly genomes to F.g. reference strain PH-1. We showed that the difference between intra- and interspecies variability was at least two times higher than intraspecific variation facilitating successful typing of F.g. This is the first study which employs WGS data for typing plant pathogenic fusaria.
Study of mechanical deformations on tough skinned vegetables during mechanical peeling process

Peeling is an essential phase of post harvesting and processing industry; however undesirable processing losses are unavoidable and always have been the main concern of food processing sector. There are three methods of peeling fruits and vegetables including mechanical, chemical and thermal, depending on the class and type of fruit. By comparison, the mechanical methods are the most preferred; mechanical peeling methods do not create any harmful effects on the tissue and they keep edible portions of produce fresh. The main disadvantage of mechanical peeling is the rate of material loss and deformations. Obviously reducing material losses and increasing the quality of the process has a direct effect on the whole efficiency of food processing industry, this needs more study on technological aspects of these operations. In order to enhance the effectiveness of food industrial practices it is essential to have a clear understanding of material properties and behaviour of tissues under industrial processes. This paper presents the scheme of research that seeks to examine tissue damage of tough skinned vegetables under mechanical peeling process by developing a novel FE model of the process using explicit dynamic finite element analysis approach. A computer model of mechanical peeling process will be developed in this study to stimulate the energy consumption and stress strain interactions of cutter and tissue. The available Finite Element softwares and methods will be applied to establish the model. Improving the knowledge of interactions and involves variables in food operation particularly in peeling process is the main objectives of the proposed study. Understanding of these interrelationships will help researchers and designer of food processing equipments to develop new and more efficient technologies. Presented work intends to review available literature and previous works has been done in this area of research and identify current gap in modelling and simulation of food processes.
Comparison of Five Oxidative Stress Biomarkers in Vegans and Omnivores from Germany and Finland

When the amount of reactive oxygen species produced by human metabolism cannot be balanced by antioxidants, this phenomenon is commonly referred to as oxidative stress. It is hypothesised that diets with high amounts of plant food products may have a beneficial impact on oxidative stress status. However, few studies have examined whether a vegan diet is associated with lower oxidative stress compared to an omnivorous diet. The present cross-sectional study aimed to compare the levels of five oxidative stress biomarkers in vegans and omnivores. Data of 36 vegans and 36 omnivores from Germany and of 21 vegans and 18 omnivores from Finland were analysed. HPLC coupled with mass spectrometry or fluorescence detection and ELISA methods were used to measure the oxidative stress biomarkers malondialdehyde (MDA), protein carbonyls and 3-nitrotyrosine in plasma and 8-hydroxy-2′-deoxyguanosine (8-OHdG) and 8-iso-prostaglandin F2α (8-iso-PGF2α) in 24 h urine. Analyses of variance and covariance, considering potential confounders, were used. Vegans and omnivores showed no differences in MDA and protein carbonyl concentrations. In Finnish but not in German vegans, the concentrations of 3-nitrotyrosine were lower compared to those in omnivores (p = 0.047). In Germany, vegans showed lower excretion levels of 8-iso-PGF2α than omnivores (p = 0.002) and with a trend also of 8-OHdG (p = 0.05). The sensitivity analysis suggests lower 8-iso-PGF2α excretion levels in women compared to men, independently of the dietary group. The present study contributes to expanding our knowledge of the relationship between diet and oxidative stress and showed that 3-nitrotyrosine, 8-OHdG and 8-iso-PGF2α tended to be lower in vegans. Furthermore, studies are recommended to validate the present findings.
Cardiac Investigation Trends Among Primary Care Physicians: A Paradigm Shift

Objective While numerous recent guidelines support coronary computed tomography angiography (CTA) as a first-line test for stable chest pain, it remains underutilized by primary care physicians (PCPs). We aimed to evaluate cardiac investigation ordering practices following education sessions, as well as the total number of downstream tests and time to diagnosis for patients presenting with stable chest pain. Methods A retrospective chart review was completed for eligible patients assessed at the Women's College Hospital Family Practice Health Centre between 2017 and 2019 following the education sessions. The outcome measures were first-choice cardiac investigation, additional downstream testing, time from presentation to first investigation, and time to final diagnosis. Results 419 patients were included in the final analysis (74.70% female; mean age 61 ± 11 years). Coronary CTA requests by PCPs increased between 2017 and 2019 (18 vs 72 tests; P < .0001). When coronary CTA was the first-line test, patients were less likely to receive additional downstream testing when compared to those receiving other first-line investigations (P < .0001). Coronary CTA was associated with longer time to diagnosis than stress echocardiography (47 ± 45 vs 27 ± 36 days; P = .0068) due to limited availability of coronary CTA appointment times. There was no significant difference in time to final diagnosis among the cardiac imaging modalities observed in the cohort (P = .0623). Conclusion Utilization of coronary CTA as the first-line test for stable chest pain increased following our education sessions targeting PCPs. Coronary CTA was associated with less downstream testing compared to other non-invasive cardiac investigations. Visual Abstract
In Silico Study of Allosteric Communication Networks in GPCR Signaling Bias

Signaling bias is a promising characteristic of G protein-coupled receptors (GPCRs) as it provides the opportunity to develop more efficacious and safer drugs. This is because biased ligands can avoid the activation of pathways linked to side effects whilst still producing the desired therapeutic effect. In this respect, a deeper understanding of receptor dynamics and implicated allosteric communication networks in signaling bias can accelerate the research on novel biased drug candidates. In this review, we aim to provide an overview of computational methods and techniques for studying allosteric communication and signaling bias in GPCRs. This includes (i) the detection of allosteric communication networks and (ii) the application of network theory for extracting relevant information pipelines and highly communicated sites in GPCRs. We focus on the most recent research and highlight structural insights obtained based on the framework of allosteric communication networks and network theory for GPCR signaling bias.
On the Mutual Influence of Human and Artificial Life: an Experimental Investigation

Our modern world is teeming with non-biological agents, whose growing complexity brings them so close to living beings that they can be cataloged as artificial creatures , i.e., a form of Artificial Life (ALife). Ranging from disembodied intelligent agents to robots of conspicuous dimensions, all these artifacts are united by the fact that they are designed, built, and possibly trained by humans taking inspiration from natural elements. Hence, humans play a fundamental role in relation to ALife, both as creators and as final users, which calls attention to the need of studying the mutual influence of human and artificial life. Here we attempt an experimental investigation of the reciprocal effects of the human-ALife interaction. To this extent, we design an artificial world populated by life-like creatures, and resort to open-ended evolution to foster the creatures adaptation. We allow bidirectional communication between the system and humans, who can observe the artificial world and voluntarily choose to perform positive or negative actions towards the creatures populating it; those actions may have a short-or long-term impact on the artificial creatures. Our experimental results show that the creatures are capable of evolving under the influence of humans, even though the impact of the interaction remains uncertain. In addition, we find that ALife gives rise to disparate feelings in humans who interact with it, who are not always aware of the importance of their conduct.
A survey of visual analytics in urban area

Nowadays, the population has been overgrowing due to urbanization, yielding many severe problems in the urban area, including traffic congestion, unbalanced distribution of urban hotspots, air pollution and so on. Due to the uncertainty of the urban environment, it always needs to integrate experts' domain knowledge into solving these issues. In recent years, the visual analytics method has been widely used to assist domain experts in solving urban problems with its intuitiveness, interactivity and interpretability. In this survey, we first introduce the background of urban computing, present the motivation of visual analytics in the urban area and point out the characteristics of visual analytics methods. Second, we introduce the most frequently used urban data, analyse the main properties and provide an overview on how to use these data. Thereafter, we propose our taxonomy for visual analytics in the urban area and illustrate the taxonomy. The taxonomy provides four levels for visual analytics on urban data from a new perspective based on the four stages in data mining. Four levels from our taxonomy include: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Finally, we conclude this survey by discussing the limitations of the existing related works and the challenges to visual analytics in the urban area.
Target Recognition in Single-Channel SAR Images Based on the Complex-Valued Convolutional Neural Network With Data Augmentation

In recent years, with the rapid development of deep learning theory, real-valued convolutional neural networks (CNNs) have achieved significant success in the field of synthetic aperture radar (SAR) target recognition. However, different from natural images, the SAR images have complex information due to their special imaging mechanism. Traditional deep learning methods for SAR target recognition only employ the amplitude information and ignore the phase portion, which may sacrifice some useful information in the original complex SAR data. Moreover, the number of samples of SAR images is very limited. This is undoubtedly a huge challenge for the traditional real-valued CNNs which require numerous labeled data for training. Especially, since the single-channel SAR image contains only one channel, it has less available information than the multiple-channel SAR image. To deal with the above problems, a complex-valued CNN, for target recognition in single-channel SAR images is proposed in this article. The amplitude and phase information in the complex SAR data are fully utilized for target recognition. In addition, to alleviate the problem of small samples, this article also proposed a data augmentation method based on the complex SAR images. The experimental results based on the measured SAR data demonstrate that the proposed algorithm has better performance than the traditional real-valued CNNs.
Effects of Coix Seed Extract, Bifidobacterium BPL1, and Their Combination on the Glycolipid Metabolism in Obese Mice

Coix seed extract (CSE) and probiotics have been reported to regulate glycolipid metabolism via different modes of action. We tested the effects of CSE, Bifidobacterium BPL1, and their combination to determine their effects on glycolipid metabolism in obese mice. Male C57BL/6J mice were fed a high-fat diet for 8 weeks to establish an obesity model. Obese mice were selected and divided into four groups: the model control group and three intervention groups. After 10 weeks of continuous gavage intervention, the mice in the intervention groups exhibited lower body weight (lower about 2.31 g, vs. HFD mice 42.23 g) and epididymal (lower about 0.37 g, vs. HFD mice 2.5 g) and perirenal fat content (lower about 0.47 g, vs. HFD mice 0.884 g); decreased fasting blood glucose, total cholesterol, triglycerides, and VLDL; and increased HLDL, respiratory exchange ratio, energy expenditure, and amount of exercise performed. CSE, BPL1 and their combination can effectively control the weight gain in obese mice, reduce fat content, and regulate blood lipids and abnormal blood sugar. These results may be related to reduce the chronic inflammatory states, improve energy metabolism, exercise, relieve insulin sensitivity, and reduce lipid synthesis via the intervention of CSE, BPL1 and their combination. Compared with the single use of CSE alone, the combination of CSE + BPL1 can better exert the regulation function of intestinal flora, and change in the abundance of bacteria that could improve the level of inflammatory factors, such as increasing Bifidobacterium, reducing Lactococcus. Compared with the use of BPL1 alone, the combination of CSE and BPL1 can better regulate pancreatic islet and improve blood sugar. CSE may act directly on body tissues to exert anti-inflammatory effects. BPL1 and CSE + BPL1 may improve the structure and function of the intestinal flora, and reduce tissue inflammation.
Diversity of Colletotrichum Species Causing Apple Bitter Rot and Glomerella Leaf Spot in China

Bitter rot and Glomerella leaf spot (GLS) of apples, caused by Colletotrichum species, are major diseases of apples around the world. A total of 98 isolates were obtained from apple fruits with bitter rot, and 53 isolates were obtained from leaves with leaf spot in the primary apple production regions in China. These isolates were characterized morphologically, and five gene regions (ITS, ACT, GAPDH, CHS-1 and TUB2) were sequenced for each isolate. A phylogenetic analysis, combined with a comparison of the morphological, cultural and pathogenic characters, sorted bitter rot isolates into six species: C. alienum, C. fructicola, C. gloeosporioides sensu stricto, C. nymphaeae, C. siamense and one new species, C. orientalis Dandan Fu & G.Y. Sun. Among these, C. siamense was the predominant pathogen associated with bitter rot. Isolates from leaf spot were identified as two species, C. aenigma and C. fructicola. This is the first report of C. orientalis as an apple bitter rot pathogen worldwide, and the results provide important insights into the diversity of Colletotrichum species in China.
Component Repository with Grey Box Testing Support

Software components have played an important role in modern software and system development. The main contribution of software components is reuse which helps reduces development cost and time and increase productivity. In software industry, there is progressive increase in reusability of software components in order to increase productivity and to reduce time-to-market. In order to make reusable component available in future projects there must be a system to store software components so that they can be efficiently retrieved as per user requirements. Software repository is the storage medium used for storing, classifying, maintaining, browsing and retrieving components. Repository must be developed which supports essential features like efficient storage and retrieval. If a specific component is to be searched through keywords or phrase, then the potential keywords need to be stored in software repository. If in any case the keyword or the phrase framed is unable to give desired result, then the software repository is required to be enriched with more efficient retrieval technique. In this study grey testing tool has been implemented with an efficient storage structure which will provide both black box and white box testing support to the reusable components and will store all the test cases generated while performing grey testing of the component for future use.
Statistical Morphometry in Neuroanatomy

The scientific aim of computational neuroanatomy using magnetic resonance imaging (MRI) is to quantify interand intra-subject morphological variabilities. A unified statistical framework for analyzing temporally varying brain morphology is presented. Based on the mathematical framework of differential geometry, the deformation of the brain is modeled and key morphological descriptors such as length, area, volume dilatation and curvature change are computed. To increase the signal-to-noise ratio, Gaussian kernel smoothing is applied to 3D images. For 2D curved cortical surface, diffusion smoothing, which generalizes Gaussian kernel smoothing, has been developed. Afterwards, statistical inference is based on the excursion probability of random fields defined on manifolds. This method has been applied in localizing the regions of brain tissue growth and loss in a group of 28 normal children and adolescents. It is shown that children’s brains change dramatically in localized areas even after age 12.
Closed-form analytical solution for on-switching dynamics in a TaO memristor

For the first time, the model of a physical nano-scale memristor is integrated analytically. A closed-form expression for the time evolution of the device memristance during the turn-on process is mathematically derived. The complexity of the inverse imaginary error function-based analytical formula clearly reflects the high degree of nonlinearity in the nano-device switching kinetics, which may typically span several orders of magnitude and is critically dependent on input and initial condition. The excellent agreement between the analytical solution and numerical simulation results clearly demonstrates the correctness of the theoretical derivation. The introduction of this formula represents the first step towards a systematic approach to circuit design with memristors.
Internalization of Appearance Ideals and Not Religiosity Indirectly Impacts the Relationship Between Acculturation and Disordered Eating Risk in South and Southeast Asian Women Living in the United States

Objective Studies that examine disordered eating in samples of Asian individuals living in the United States frequently combine all individuals of Asian descent into a single group, which can obscure important differences between groups and their experiences of acculturation. The goal of the present study was to establish the relation of acculturation, internalization of appearance ideals, and religiosity as predicting body dissatisfaction and disordered eating in women of South and Southeast Asian (SSEA) descent. Method Women of SSEA descent (N = 112) aged 18–51 years (M = 23.10, SD = 6.4) completed a battery of questionnaires that inquire about these variables. A path analysis was conducted with acculturation serving as the independent (exogenous) variable, religiosity and internalization of the thin ideal as mediators, and body dissatisfaction and disordered eating as dependent (endogenous) variables. Results Direct paths from acculturation to both body dissatisfaction and disordered eating were not significant. Thin ideal internalization completely accounted for the path from acculturation to both endogenous variables; whereas, religiosity did not significantly account for any indirect effect. Discussion For SSEA women, internalization of appearance ideals is a potentially greater risk factor for disordered eating than acculturation or religiosity. As this was an atemporal mediation analysis, more work needs to be done exploring predictors of internalization in this population and how that may impact the development of disordered eating.
Analysis of depression in social media texts through the Patient Health Questionnaire-9 and natural language processing

Objective Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users’ depression based on their results. Methods First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whether a user’s sentence is related to depression, and the 0–9 sentence classifier, which further categorizes the user sentence based on the depression symptomology of the Patient Health Questionnaire-9. Then the depression classifier, which is a logistic regression model, was generated to classify the sentence writer’s depression. These trained sentence classifiers and the depression classifier were used to analyze the social media textual data of users and establish their depression. Results Our experimental results showed that the proposed depression classifier showed 68.3% average accuracy, which was better than the baseline depression classifier that used only the Y/N sentence classifier and had 53.3% average accuracy. Conclusions This study is significant in that it demonstrates the possibility of determining depression from only social media users’ textual data.