Miracle-dz/UniFAR-FTUs huggingface dataset:
Barriers of Local Organizations for Providing Free Food Support in the Priority Neighborhood, Toronto: What We Need Further

Introduction: The provincial and federal governments provided emergency funding to local community organizations for free food support to people affected by COVID-19. The study's goals were to explore the barriers of the people and the organizations while receiving and providing free food support and what further was needed to give the people food support in the priority neighborhood. Methods: Nine representatives of the local community organizations and 15 volunteers participated in the community conversation or the extensive group discussion. They shared their experiences about barriers and what we further needed to provide food support for the priority neighborhood like Taylor Massey. Two note-takers obtained data from the community conversation. This is a qualitative study. It used a thematic approach to analyze the data and interpretation. Results: Information lacking about free food, language barriers, the social stigma associated with free food support, and fear of COVID-19 were the barriers of community people to receiving food support. On the other hand, the community organizations lacked information about who needed real food support, lacked enough transportation support to distribute the food, enough empty spaces to store food, and enough numbers of committed and trained volunteers to help with the food distribution. Also, the community organizations had limited funding to meet people's food needs. The local community organizations needed an assessment to understand the available resources so that local organizations could use the resources for an effective food program. Furthermore, the organizations required coordination and extended food funding to affected families. In addition, the local organizations needed to work with community gardens, community kitchens, and food banks to support and meet the community's demands. Conclusion: People and local organizations had information and communication, social, structural and systemic, and financial barriers while receiving and providing food in the priority neighborhood. The study indicated what were needed to make the food support program effective.
Avant-garde Approach to Life: Reviewing the Current Applications of 3D Bioprinting

Introduction: The promise of bioprinting tissue constructs that could potentially serve the same function in the human body as native tissues has taken the world of regenerative medicine by storm. The current review describes system-wide clinical applications of three-dimensional (3D) bioprinting and aims to address ethical and social considerations, while also discussing the scope of this technology in the near future. Discussion: 3D bioprinting is believed to present new approaches to conventional treatment, offering the advantage of customization and on-time availability. It facilitates simultaneous deposition of appropriate bioinks and biomaterials onto scaffolds which can then be employed to develop tissue fabricates that can potentially mimic native tissues in both structure and functionality. It has been extensively employed to regenerate viable tissue constructs of skin, bone, cartilage, vasculature, myocardial tissue and heart valves, nervous tissue, lung and tracheal tissue, liver, pancreatic, and corneal tissue. Conclusion: To obviate the current restrictions associated with this technology, it is imperative to understand where we currently stand in terms of current clinical applications of 3D bioprinting. This technology is anticipated to contribute significantly to the fields of tissue engineering and regenerative medicine (TERM), where it can be employed to fabricate functional tissues that can simulate their counterparts in the human body. Relevance: The increasing disparity between organ demand and supply as well as the shortcomings associated with antiquated approaches to treatment call for utilizing 3D bioprinting to develop viable tissue constructs.
Sugarcane Root Transcriptome Analysis Revealed the Role of Plant Hormones in the Colonization of an Endophytic Diazotroph

Some sugarcane germplasms can absorb higher amounts of nitrogen via atmospheric nitrogen fixation through the bacterial diazotrophs. Most endophytic diazotrophs usually penetrate through the root, colonize inside the plant, and fix the nitrogen. To assess the plant’s bacterial association during root colonization, strain GXS16 was tagged with a plasmid-bear green fluorescent protein (GFP) gene. The results demonstrated that the strain can colonize roots all the way to the maturation zone. The strain GXS16 showed maximum nitrogenase enzyme activity at pH 8 and 30°C, and nitrogenase activity is less affected by different carbon sources. Further, strain GXS16 colonization response was investigated through plant hormones analysis and RNAseq. The results showed that the bacterial colonization gradually increased with time, and the H2O2 and malondialdehyde (MDA) content significantly increased at 1 day after inoculation. There were no substantial changes noticed in proline content, and the ethylene content was detected initially, but it decreased with time. The abscisic acid (ABA) content showed significant increases of 91.9, 43.9, and 18.7%, but conversely, the gibberellin (GA3) content decreased by 12.9, 28.5, and 45.2% at 1, 3, and 5 days after inoculation, respectively. The GXS16 inoculation significantly increased the activities of catalase (CAT), superoxide dismutase (SOD), polyphenol oxidase (PPO), ascorbate peroxidase (APX), and glutathione reductase (GR) at different timepoint. In contrast, the peroxisome (POD) activity had no changes detected during the treatment. In the case of RNAseq analysis, 2437, 6678, and 4568 differentially expressed genes (DEGs) were identified from 1, 3, and 5 days inoculated root samples, and 601 DEGs were shared in all samples. The number or the expression diversity of DEGs related to ethylene was much higher than that of ABA or GA, which indicated the critical role of ethylene in regulating the sugarcane roots response to GXS16 inoculation.
Resilience of Environmental and Social Stocks under Stress: Lessons from the COVID‐19 Pandemic

Abstract This paper examines whether environmental and social (ES) activities affect the resiliency of firms during the COVID‐19 crisis. We study a sample of 330 firms operating in five developed countries: Canada, France, Japan, the UK and the US. Our analysis shows that US firms with a high ES ranking experienced a significantly lower stock price range volatility during the Covid stock market rundown of February‐March 2020. Such findings also hold for Japanese firms but only later on after the introduction of government support. In terms of returns, compared to their peers with a low ES ranking, Japanese and UK stock prices with a high ES ranking suffered more during and after the market rundown. For other countries, we do not find significant differences in stock price behavior based on ES ratings. Our findings suggest that engaging with ES activities is not associated with a better or worse performance during crisis times, which has important implications for investors and managers.
Parametric Curves Metamodelling Based on Data Clustering, Data Alignment, POD-Based Modes Extraction and PGD-Based Nonlinear Regressions

In the context of parametric surrogates, several nontrivial issues arise when a whole curve shall be predicted from given input features. For instance, different sampling or ending points lead to non-aligned curves. This also happens when the curves exhibit a common pattern characterized by critical points at shifted locations (e.g., in mechanics, the elastic-plastic transition or the rupture point for a material). In such cases, classical interpolation methods fail in giving physics-consistent results and appropriate pre-processing steps are required. Moreover, when bifurcations occur into the parametric space, to enhance the accuracy of the surrogate, a coupling with clustering and classification algorithms is needed. In this work we present several methodologies to overcome these issues. We also exploit such surrogates to quantify and propagate uncertainty, furnishing parametric stastistical bounds for the predicted curves. The procedures are exemplified over two problems in Computational Mechanics.
Image Classification on Hurricane Damage Prediction via Deep Learning Methods

Artificial Intelligence is widely applied around us in daily life. Its powerful property can also help us, the human beings in environmental issues, analyse the traces left by nature and forecasting. Therefore, image classification has been widely used in different fields, becoming a part of our lives, especially in medical and diagnostic fields. As the weather condition changes dramatically throughout these years, we the human beings, need to do more on natural protection and rescue. Hence, this paper discusses different machine learning models on image classification for hurricane damage prediction problem and their performances. The image we use in this research is obtained via satellite by “Geo-satellite sensor” and “Geo Bigdata”, and the models we apply include AlexNet, LeNet-5, along with CNNs and other supervised machine learning techniques. The best accuracy these models can achieve is up to 94.50% in the training set. This study improves the models' performance on hurricane image classification through parameters, the Neural Network architecture construction, and optimizers. The value of the study is to realize the importance and values of the Neural Network to provide support for post-hurricane actions.
Tourist arrivals and overnight stays along the Croatian Adriatic Coast: Changes in persistence and seasonality from the COVID-19 disruption

This study examines the changes in the persistence and seasonality inherent in the Croatian tourism sector in light of the onset of the COVID-19 pandemic. First, we differentiate between the changes in the persistence and seasonal behavior with respect to domestic and foreign tourist arrivals and overnight stays. Second, with nearly 90% of the Croatian tourism sector tied to the seven counties along the Adriatic coast we investigate the differential regional impact on persistence and seasonal behavior. Our results indicate the disruption was much more prominent for foreign tourist arrivals and overnight stays relative to domestic tourist arrivals and overnight stays with respect to the increased persistence associated with the onset of the pandemic along with the seasonal autoregressive component reduced considerably.
Projects in the Business Ecosystem: The Case of Short Sea Shipping and Logistics

This article develops a conceptual framework to analyze the governance of projects within a business ecosystem. The framework is applied to the case of a vessel delivery project in the short sea shipping business ecosystem, which is a cargo and logistics infrastructure service at sea. We develop a model that identifies contentious lock-ins among the workflows, and show how they can be resolved by governance that can increase performance of the sea logistics infrastructure. The model shows the interdependence of the short sea shipping business ecosystem and the vessel project, and it shows how performance is enhanced by their integration.
COPD_A_261919 2103..2113

1Modus Outcomes, Lyon, France; 2Novartis Pharma AG, Basel, Switzerland Purpose: The objective of this study was to examine the psychometric properties of the St. George’s Respiratory Questionnaire (SGRQ) in patients with chronic obstructive pulmonary disease (COPD) using Rasch measurement theory (RMT) analyses. Materials and Methods: RMT analysis was conducted on the baseline SGRQ data from five multi-national, Phase III randomized trials investigating a fixed-dose combination of a long-acting β2-agonist and a long-acting muscarinic antagonist in COPD patients. Analysis was performed for the SGRQ “Symptoms” and “Activity” domains. An exploratory analysis was also conducted using the different specific symptoms as defined in the reconceptualization of the SGRQ “Symptoms” domain. Differential item functioning (DIF) analysis was performed for geographical regions on the “Activity” domain, in order to explore crosscultural validity of the SGRQ. Results: Overall, the SGRQ “Activity” domain showed good measurement property, but two items (“Sitting or lying still making feel breathless” and “Playing sports or game making feel breathless”) showed very high fit residuals. The SGRQ “Symptoms” domain demonstrated good targeting; however, two items showed disordered thresholds (“Coughed” and “Brought up phlegm”). In an exploratory RMT analysis, measures for “Cough and Sputum”, “Breathing difficulties” or “Wheezing attacks” showed unsatisfactory measurement properties with poor reliability (person separation index = 0.35, 0.66 and 0.16, respectively) and targeting issues. The examination of cross-cultural performances of the SGRQ “Activity” items showed a great variability in the responses to these items in different global regions. Conclusion: Our results indicated that SGRQ may not be an appropriate instrument to measure symptom severity or activity limitations in patients with COPD. Hence, there is a need to develop other relevant PRO instruments that can be used in conjunction with SGRQ to provide a holistic assessment of the health status of COPD patients in clinical research.
Identification of Novel Tumor Antigens and the Immune Landscapes of Bladder Cancer Patients for mRNA Vaccine Development

Background mRNA vaccines are a novel technology that provide a potential strategy for cancer treatment. However, few studies exist that are focused on the application and development of mRNA vaccines in bladder cancer (BLCA). Therefore, this study filtered candidate antigens and specific mRNA-suitable populations in BLCA via comprehensive multi-omics analysis. Methods Clinical information, follow-up information, and gene expression profiles were obtained from the TCGA and GEO databases. Somatic mutation and DNA copy number variation of BLCA were visualized by cBioPortal. Significant survival genes were analyzed by GEPIA2. TIMER was used to evaluate the connection between candidate antigens and infiltration of antigen-presenting cells. Consensus clustering analysis was performed to identify immune subtypes using the ConsensusClusterPlus package. The Monocle package was used to visualize the immune landscapes of each BLCA patient. Weighted gene co-expression network analysis (WGCNA) was used to identify key genes for mRNA vaccines. Results AP2S1, P3H4, and RAC3 were identified as candidate tumor-specific antigens for BLCA. Three immune subtypes were classified based on immune-related gene expression profiles. Patients with the BCS2 subtype were characterized as immune “cold” and exhibited upregulation of immunogenic cell death modulators, whereas patients with BCS1 and BCS3 were immune “hot” and had upregulation of immune checkpoints. Interestingly, patients with the BCS2 subtype had a better prognosis than other subtypes. The immune landscapes of each patient were visualized and revealed the heterogeneity within the BCS1 subtype. Finally, 13 key immune genes were identified. Conclusions AP2S1, P3H4, and RAC3 were identified as candidate tumor-specific antigens, and patients with the BCS2 and BCS1A subtypes were identified as candidate populations for mRNA vaccines. In summary, this study provides novel insights and a theoretical basis for mRNA vaccine development in BLCA and other malignancies.
Electric field distribution in spark plugs insulators – modeling and computer simulation

Automotive spark plugs are essential ignition circuit components being of importance for combustion engine reliability and performance. The majority of spark plugs have ribbed ceramic insulator to ensure high resistance along the surface from the terminal to the metal shell to minimize leakage current and to provide flashover protection. The leakage current intensity depends on electric field distribution, physical insulator properties and such factors as humidity, insulator contamination or defects in insulation material. Furthermore, leakage current not infrequently interferes with discharge process causing misfire effect being harmful to exhaust manifold components, mainly to catalytic converters. This paper presents simulation results of electric field distribution in ceramic insulator, in silicone rubber boot and in space surrounding a spark plug. Assuming that a spark plug can be considered as an object having cylindrical symmetry the electric field distribution was calculated for a two-dimensional case in accordance with Laplace’s and Poisson’s equations. In this paper, the finite difference method (FDM) for the solution of the Laplace’s equation was applied. The FDM algorithm based on the Liebmann’s method was developed in the MATLAB environment. Presented simulation results can be helpful to automotive spark plugs and high-tension cables manufacturers interested in improvement of insulating properties.
The mitogenome of a medically important paper wasp, Polistes hebraeus (Hymenoptera, Vespidae)

Abstract Polistes hebraeus (Smith) is a common paper wasp species of subfamily Polistinae. The clinical potential allergic reaction after stings and valuable venom components of P. hebraeus make it a medical importance species. Here, the mitochondrial genome of P. hebraeus was analyzed. The mitogenome was 19,262 bp in length including 22 transfer RNAs (tRNA) and 2 ribosomal RNAs (rRNA). The protein coding gene (PCGs) number and order are consistent with existing records of Polistidae. The nucleotide composition is AT: 84.5% and CG: 15.5% which are largely similar with other complete Polistidae mitogenome (15.27 ∼ 19.16%). The available mitogenome resources of Polistidae species and P. hebraeus were used to construct the phylogenetic tree. It showed that the genus Polistes could clearly seperated from Parapolybia, and the relationship in Polistes was consistent with previous studies. This mitogenome resource can contribute to further phylogenetic and taxonomic study on paper wasp.
Relationship Between Psychosocial Stress and Blood Pressure: The National Heart, Lung, and Blood Institute Family Heart Study

Introduction Various domains of psychosocial stress have been significantly related to blood pressure. However, ambiguity is present in how these relationships are defined in the literature. Objective To add to the existing literature and examine the relationship between psychosocial stress (financial strain and job strain) and other cofactors on blood pressure. Methods This secondary analysis is designed to analyze the relationship between levels of job and financial stress and blood pressure outcomes among participants in the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study 2004–2008. The descriptive, cross-sectional design uses data from a subset of study participants, 350 White and 195 Black (n = 545), 338 female (62%), and all aged 18–56 years. Psychosocial stress was measured using the Singh Stress Scale. Resting systolic (SBP) and diastolic (DBP) blood pressure values obtained on a stress reactivity protocol day in the primary study, as well as calculated mean arterial pressure (MAP) were used for this analysis. Multivariate linear regression analyses were used to explore the relationship between psychosocial stress and blood pressure. Results In this young cohort, self-report of either financial strain or job strain was associated with lower blood pressure levels than those of participants who reported neither stressor. Differential sex and race effects appear to contribute to these results. Blood pressure levels were not significantly associated with self-report of both stressors. Conclusion Understanding the effects of various forms of stress on blood pressure may inform more precise HTN risk-factor screening and interventions to improve BP management.
Transmission parameters of coronavirus disease 2019 in South Asian countries

Objectives This study aimed to estimate the transmission parameters, effective reproduction number, epidemic peak, and future exposure of coronavirus disease 2019 (COVID-19) in South Asian countries. Methods A susceptible-exposed-infected-recovered-death (SEIRD) model programmed with MATLAB was developed for this purpose. Data were collected (till June 28, 2021) from the official webpage of World Health Organization, along with the Center for Systems Science and Engineering at Johns Hopkins University. The model was simulated to measure the primary transmission parameters. The reproduction number was measured using the next-generating matrix method. Results The primary transmission rate followed an exponential Gaussian process regression. India showed the highest transmission rate (0.037) and Bhutan the lowest (0.023). The simulated epidemic peaks matched the reported peaks, thereby validating the SEIRD model. The simulation was carried out up to December 31, 2020 using the reported data till June 9, 2020. Conclusion The information gathered in this research will be helpful for authorities to prevent the transmission of COVID-19 in the subsequent wave or in the future.
Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor's level in the identification of various lung diseases.