Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergency Stress, Hardiness, Coping Strategies and Burnout in Health Care and Emergency Response Workers During the COVID-19 Pandemic

Objective Stress is a growing problem in the general population, but most especially for workers responding to the COVID-19 crisis. The present study examines stress and Burnout in Health Care workers and Emergency Responders during the third COVID wave in Italy. In addition, we explore the value of psychological Hardiness and positive coping strategies as protective factors against the ill-effects of stress. Methods Over a 5-month period in 2021, surveys were administered across all Italian regions to several groups including Health Care workers (N = 220), Emergency Responders (firefighters, civil protection, ambulance personnel; N = 121), volunteer Italian Red Cross workers (N = 328), and a comparison group (N = 400) drawn from the General Population of Italy. Results Results showed that among the groups, Health Care workers had the highest levels of Emergency Stress, while the Red Cross volunteers had relatively lower stress levels. Hardiness and positive coping levels were highest among Red Cross workers, and lowest in the General Population sample. The biggest impact on Burnout was seen among health care workers, especially on Emotional Exhaustion and Depersonalization. Multiple regression results showed that Hardiness operates as a moderator of the effects of Emergency Stress on Emotional Exhaustion and Depersonalization aspects of Burnout, while problem focused coping and Stopping Negative Thoughts-Emotions also showed moderating effects. Conclusion These results suggest that Health Care workers and Emergency Responders would benefit from additional training in hardiness and positive coping skills.
Factors influencing dust reduction efficiency of spraying of oil-water mixtures in pig buildings

found in livestock buildings, i.e. 0.2 to 0.5 m s -1 . The droplet diameters should, therefore, be greater than 150 µm, corresponding to terminal velocity of 0.46 m s -1 . Farmers using the method often claim plugging of spray nozzles caused by coagulated oil. This is probably the most significant and unsolved technical problem.
Urinary Cystatin C Has Prognostic Value in Peripheral Artery Disease

Despite its association with adverse outcomes, peripheral artery disease (PAD) remains undertreated. Cystatin C is elevated in patients with renal disease and may be a marker of cardiovascular disease. We examined the prognostic ability of urinary Cystatin C (uCystatinC) in predicting adverse PAD-related events. In this prospective case-control study, urine samples were collected from patients with PAD (n = 121) and without PAD (n = 77). The cohort was followed for 2 years. uCystatinC was normalized to urinary creatinine (uCr) (uCystatinC/uCr; μg/g). The primary outcome was major adverse limb event (MALE; composite of vascular intervention (open or endovascular) or major limb amputation). The secondary outcome was worsening PAD status (drop in ABI ≥ 0.15). Multivariable Cox regression and Kaplan–Meier analyses were performed to assess the prognostic value of uCystatinC/uCr with regards to predicting MALE and worsening PAD status. Our analysis demonstrated that patients with PAD had significantly higher median [IQR] uCystatinC/uCr levels (24.9 μg/g [14.2–32.9] vs. 20.9 μg/g [11.1–27.8], p = 0.018). Worsening PAD status and MALE were observed in 39 (20%) and 34 (17%) patients, respectively. uCystatinC/uCr predicted worsening PAD status with a hazard ratio (HR) of 1.78 (95% CI 1.12–2.83, p = 0.015), which persisted after controlling for baseline demographic and clinical characteristics (adjusted HR 1.79 [95% CI 1.11–2.87], p = 0.017). Patients with high uCystatinC/uCr had a lower 2-year freedom from MALE (77% vs. 89%, p = 0.025) and worsening PAD status (63% vs. 87%, p = 0.001). Based on these data, higher uCystatinC/uCr levels are associated with adverse PAD-related events and have prognostic value in risk-stratifying individuals for further diagnostic vascular evaluation or aggressive medical management.
DSQNet: A Deformable Model-Based Supervised Learning Algorithm for Grasping Unknown Occluded Objects

Grasping previously unseen objects for the first time, in which only partially occluded views of the object are available, remains a difficult challenge. Despite their recent successes, deep learning-based end-to-end methods remain impractical when training data and resources are limited and multiple grippers are used. Two-step methods that first identify the object shape and structure using deformable shape templates, then plan and execute the grasp, are free from those limitations, but also have difficulty with partially occluded objects. In this paper, we propose a two-step method that merges a richer set of shape primitives, the deformable superquadrics, with a deep learning network, DSQNet, that is trained to identify complete object shapes from partial point cloud data. Grasps are then generated that take into account the kinematic and structural properties of the gripper while exploiting the closed-form equations available for deformable superquadrics. A seven-dof robotic arm equipped with a parallel jaw gripper is used to conduct experiments involving a collection of household objects, achieving average grasp success rates of 93% (compared to 86% for existing methods), with object recognition times that are ten times faster. Code is available at https://github.com/seungyeon-k/DSQNet-public Note to Practitioners—This paper provides a comprehensive two-step method for grasping previously unseen objects, in which only partially occluded views of the object may be available. End-to-end deep learning-based methods typically require large amounts of training data, in the form of images of the objects taken from different angles and with different levels of occlusion, and grasping experiments that record the success and failure of each attempt; if a new gripper is used, more often than not the training data must be recollected and a new set of experiments performed. Two-step methods that first identify the object structure and shape using deformable shape templates, then plan the grasp based on knowledge of the object shape, are currently a more practical solution, but also have difficulty when only occluded views of the object are available. Our newly proposed two-step method takes advantage of a more flexible set of shape primitives, and also uses a supervised deep learning network to identify the object from occluded views. Our experimental results indicate improved grasp success rates against the state-of-the-art, with recognition rates that are up to ten times faster. Our method shows high recognition and grasping performance so is well applicable on most of the general household objects, but it cannot be directly applied to more diverse public 3D datasets since it requires some human-annotated segmentation labels. In future research, we will develop our deep learning network to automatically learn segmentation without human-annotated labels, allowing it to recognize more complex and diverse object shapes.
Pycard and BC017158 Candidate Genes of Irm1 Locus Modulate Inflammasome Activation for IL-1β Production

We identified Pycard and BC017158 genes as putative effectors of the Quantitative Trait locus (QTL) that we mapped at distal chromosome 7 named Irm1 for Inflammatory response modulator 1, controlling acute inflammatory response (AIR) and the production of IL-1β, dependent on the activation of the NLRP3 inflammasome. We obtained the mapping through genome-wide linkage analysis of Single Nucleotide Polymorphisms (SNPs) in a cross between High (AIRmax) and Low (AIRmin) responder mouse lines that we produced by several generations of bidirectional selection for Acute Inflammatory Response. A highly significant linkage signal (LOD score peak of 72) for ex vivo IL-1β production limited a 4 Mbp interval to chromosome 7. Sequencing of the locus region revealed 14 SNPs between “High” and “Low” responders that narrowed the locus to a 420 Kb interval. Variants were detected in non-coding regions of Itgam, Rgs10 and BC017158 genes and at the first exon of Pycard gene, resulting in an E19K substitution in the protein ASC (apoptosis associated speck-like protein containing a CARD) an adaptor molecule in the inflammasome complex. Silencing of BC017158 inhibited IL1-β production by stimulated macrophages and the E19K ASC mutation carried by AIRmin mice impaired the ex vivo IL-1β response and the formation of ASC specks in stimulated cells. IL-1β and ASC specks play major roles in inflammatory reactions and in inflammation-related diseases. Our results delineate a novel genetic factor and a molecular mechanism affecting the acute inflammatory response.
Immunoinformatics Aided Design and In-Vivo Validation of a Cross-Reactive Peptide Based Multi-Epitope Vaccine Targeting Multiple Serotypes of Dengue Virus

Dengue virus (DENV) is an arboviral disease affecting more than 400 million people annually. Only a single vaccine formulation is available commercially and many others are still under clinical trials. Despite all the efforts in vaccine designing, the improvement in vaccine formulation against DENV is very much needed. In this study, we used a roboust immunoinformatics approach, targeting all the four serotypes of DENV to design a multi-epitope vaccine. A total of 13501 MHC II binding CD4+ epitope peptides were predicted from polyprotein sequences of four dengue virus serotypes. Among them, ten conserved epitope peptides that were interferon-inducing were selected and found to be conserved among all the four dengue serotypes. The vaccine was formulated using antigenic, non-toxic and conserved multi epitopes discovered in the in-silico study. Further, the molecular docking and molecular dynamics predicted stable interactions between predicted vaccine and immune receptor, TLR-5. Finally, one of the mapped epitope peptides was synthesized for the validation of antigenicity and antibody production ability where the in-vivo tests on rabbit model was conducted. Our in-vivo analysis clearly indicate that the imunogen designed in this study could stimulate the production of antibodies which further suggest that the vaccine designed possesses good immunogenicity.
Computational Modeling of Rupture Risk in Abdominal Aortic Aneurysms

An abdominal aortic aneurysm (AAA) is a frequently asymptomatic ballooning of the aorta, which in case of rupture is terminal for the patient. Experiments on mechanical properties of AAA wall, investigations on biological processes measured by PET images, as well as FE simulations to determine in vivo wall stress were combined to clarify the so far poorly understood mechanobiology during AAA development. Mechanics-based indices were developed, which provide a reliable AAA rupture risk prediction.
Genomic Analysis of Waterpipe Smoke-Induced Lung Tumor Autophagy and Plasticity

The role of autophagy in lung cancer cells exposed to waterpipe smoke (WPS) is not known. Because of the important role of autophagy in tumor resistance and progression, we investigated its relationship with WP smoking. We first showed that WPS activated autophagy, as reflected by LC3 processing, in lung cancer cell lines. The autophagy response in smokers with lung adenocarcinoma, as compared to non-smokers with lung adenocarcinoma, was investigated further using the TCGA lung adenocarcinoma bulk RNA-seq dataset with the available patient metadata on smoking status. The results, based on a machine learning classification model using Random Forest, indicate that smokers have an increase in autophagy-activating genes. Comparative analysis of lung adenocarcinoma molecular signatures in affected patients with a long-term active exposure to smoke compared to non-smoker patients indicates a higher tumor mutational burden, a higher CD8+ T-cell level and a lower dysfunction level in smokers. While the expression of the checkpoint genes tested—PD-1, PD-L1, PD-L2 and CTLA-4—remains unchanged between smokers and non-smokers, B7-1, B7-2, IDO1 and CD200R1 were found to be higher in non-smokers than smokers. Because multiple factors in the tumor microenvironment dictate the success of immunotherapy, in addition to the expression of immune checkpoint genes, our analysis explains why patients who are smokers with lung adenocarcinoma respond better to immunotherapy, even though there are no relative differences in immune checkpoint genes in the two groups. Therefore, targeting autophagy in lung adenocarcinoma patients, in combination with checkpoint inhibitor-targeted therapies or chemotherapy, should be considered in smoker patients with lung adenocarcinoma.
Chemical Compositions of Walnut (Juglans Spp.) Oil: Combined Effects of Genetic and Climatic Factors

Walnut oil is a high-value oil product. Investigation of the variation and the main climatic factors affecting the oil’s chemical composition is vital for breeding and oil quality improvement. Therefore, the fatty acid, micronutrients, and secondary metabolites compositions and contents in walnut oil were determined in three species: Juglans regia L. (common walnut), J. sigillata Dode (iron walnut), and their hybrids (Juglans sigillata Dode × J. regia L.), which were cultivated aat different sites. The major fatty acids were linoleic (51.39–63.12%), oleic (18.40–33.56%), and linolenic acid (6.52–11.69%). High variation in the contents of fatty acids, micronutrients, and secondary metabolites was found between both species and sites. Interestingly, myristic, margaric, and margaroleic acid were only detected in the hybrids’ walnut oil, yet α-tocopherol was only detected in common and iron walnut oil. Climatic factors significantly affected the composition and content of fatty acid, whereas δ-tocopherol was mostly dependent on the genetic factors. The average relative humidity explained the most variation in the fatty acids, micronutrients, and secondary metabolites, which showed a significant positive and negative effect on the monounsaturated fatty acids and polyunsaturated fatty acids, respectively. These findings contribute to the provision of better guidance in matching sites with walnut trees, and improvement of the nutritional value of walnut oil.
Adaptive Multidimensional Dual Attentive DCNN for Detecting Cardiac Morbidities Using Fused ECG-PPG Signals

Clinicians refer to standard physiological signals (ECG and PPG) to diagnose conduction disorders and coronary arterial malfunctions. The exact disease is identified from the results of confirmatory medical tests. This procedure creates a time lag in treating patients and causes difficulty in attending emergency cases. In this study, a novel method is proposed to detect and classify diseases related to electrical impulses and coronary arteries from other cardiac abnormalities efficiently. The ECG and PPG signals are collected simultaneously from 300 cardiovascular diseased patients, following a predecided inclusion and exclusion criteria. A fused signal is generated using an algorithm from the PPG and ECG signals. The deep neural network is constructed involving self and cross attention properties, multidimensional convolution, and skip connections. Reinforcement learning is utilized to induce an adaptive property in the model. The model’s ability in distinguishing the targeted diseases is achieved with an accuracy of 0.80, recall of 0.75, precision of 0.73, micro avg F-score of 0.78, and specificity of 0.85. Ablation studies are performed to get an efficient network. Comparison with the state-of-art methods and other established networks is made to denote the superiority of the proposed network. The fused signal is more efficient in comparison to the individual signals. Analysis of the ROC and PR curves produced an AUC and AP of 0.79 and 0.76, respectively. This technique could be used to identify the type of CVD at a preliminary stage of clinical diagnosis effectively involving less time, effort, and resources.
Piper species (Piperaceae) of Sri Lanka and the diversity of the economically important Piper nigrum (black pepper): An overview of what has been done and what is yet to be achieved

: Applying restriction site-associated DNA sequencing (RADseq) and target capture for Piper species from species-rich South America and India, Southeast Asia, and Africa will highlight the origin and evolution of Sri Lankan endemics, P. zeylanicum , P. walkerii , and P. trineuron . Looking into the genetic diversity of cultivated P. nigrum from different agroclimatic regions and available germplasm in Sri Lanka using RADseq will give an overview of the existing genetic diversity of black pepper, which is economically important and needs genetic improvement. Variation in flower composition (male, female or bisexual) across the spikes and their shape is of major interest to evolutionary and pollination biologists and plant systematists. The 3D shape models of flowers obtained by computed tomography of the wild species of Piper from Sri Lanka and cultivated P. nigrum will play an important role in revising the taxonomy and understanding the pollination biology of the genus.
Antioxidant Activity and Cytotoxicity of Aromatic Oligosulfides

Natural or synthetic antioxidants with biomimetic fragments protect the functional and structural integrity of biological molecules at a minimum concentration, and may be used as potential chemotherapeutic agents. This paper is devoted to in silico and in vitro evaluation of the antioxidant and cytotoxic properties of synthetic analogues of natural compounds—aromatic oligosulfides. The antiradical and SOD-protective activity of oligosulfides was demonstrated in the reaction with O2–• generated in enzymatic and non-enzymatic systems. It was found that phenol-containing disulfides significantly reduced the accumulation level of hydroperoxides and secondary carbonyl thiobarbituric acid reactive substances, which are primary products of oleic acid peroxidation. The antioxidant efficiency of bis(3,5-di-tert-butyl-4-hydroxyphenyl) disulfide increased over time due to the synergistic action of the 2,6-di-tert-butylphenol fragment and the disulfide linker. The highest cytotoxicity on the A-549 and HCT-116 cell lines was found for bis(3,4-dimethoxyphenyl) disulfide. Significant induction of apoptosis in HCT-116 cells in the presence of bis(3,4-dimethoxyphenyl) disulfide indicates the prospect of its use as an antitumor agent. The significant and moderate dependences revealed between various types of activities of the studied aromatic oligosulfides can be used in the development of a strategy for the synthesis and study of target-oriented compounds with predictable biological activity.
Outbreak of drug resistance Escherichia coli phylogenetic F group associated urinary tract infection

Background and Objectives: Urinary tract infections are one of the most commonly associated human infectious diseases caused by the bacteria Escherichia coli. Escherichia coli is described as having a large number of virulence genes that enable drug resistance, which is a cause for great concern. Monitoring of antimicrobial susceptibility is critical to determining the scope of the problem and selecting appropriate antimicrobial drugs. The current study aimed to identify the distribution of uropathogenic E. coli (UPEC) based on genetic profiles and to determine resistance patterns among isolates. Materials and Methods: This study employed biological correlations to study the patterns of antibiotic resistance and the distribution of phylogenetic groups of 118 isolates of E. coli and the relationship between them, which were isolated from three hospitals in Baghdad, Iraq. Results: The results of phylogenetic analysis showed that phylogroup F was the most common group among E. coli isolates (37.3%), followed by phylogroups C (20.3%), B2 (15.3%), E (14.4%), UP (4.2%), A and D (3.4%), and B1 (1.7%). The majority of antibiotic resistance patterns were related to penicillin groups (80.5%) and the least to the sulfonamide groups (67.0%). 51.7%, 42.4%, and 1.7% of isolates were Extensive Drug Resistance (XDR), Multi-Drug Resistance (MDR), and Pan Drug Resistance (PDR), respectively. Antibiotic resistance was most commonly detected in group F (35.6%). Conclusion: Our observations revealed that the dominant phylogroup F had the highest prevalence of multi-drug resistance and extensive drug resistance among E. coli isolates. The newly identified phylogroups C, E, and F account for about 72.0% of the E. coli isolates. Such investigations should be conducted in other localities as well, in order to gain a better understanding of the pattern of antibiotic resistance patterns and the frequency of distinct phylogenetic groups.
Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning

With the rapid development of society and economy, people's living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge. The purpose of this paper was to analyze the impact of bodybuilding exercise on physical fitness based on deep learning. It provides a reference for fitness enthusiasts to choose scientific and targeted exercise methods, and provides a theoretical basis for the promotion of bodybuilding and fitness. This paper first gives a general introduction to deep learning and adds image segmentation technology to design experiments for bodybuilding and fitness. The experiment was divided into groups A and B, and control group C. In this paper, recurrent neural network and gated recurrent neural network are introduced to compare and analyze the data, and the stability of data processing with different activation functions is compared. The data results show that under the scientific and reasonable arrangement of exercise conditions, bodybuilding and fitness exercises have a corresponding positive effect on the body shape and posture of the subjects. It is more practical to choose a combination of aerobic and anaerobic exercise. In this paper, based on the deep learning algorithm, compared with the recurrent neural network, the gated recurrent neural network is more suitable for processing sequence problems. In the experimental analysis part, this paper compares and analyzes the experimental results of the data under different activation functions, sigmoid function, and tanh function. It is found that the tanh activation function and the gated recurrent neural network are more stable for data processing. The highest AUC value of the traditional recurrent neural network differs by 0.78 from the highest AUC value of the gated recurrent neural network. The data analysis results are in line with the actual situation.