Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of serum vitamin D concentration with the final course of hospitalization in patients with COVID-19

Background Vitamin D deficiency is a substantial public health problem. The present study evaluated the association between vitamin D concentration and hospitalization and mortality risk in patients with coronavirus disease 19 (COVID-19). Methods This study used the COronavirus in LOwer Silesia (COLOS) dataset collected between February 2020 and June 2021. The medical records of 474 patients with confirmed severe acute respiratory syndrome 2 (SARS-CoV-2) infection, and whose vitamin D concentration was measured, were analyzed. Results We determined a significant difference in vitamin D concentration between discharged patients and those who died during hospitalization (p = 0.0096). We also found an effect of vitamin D concentration on the risk of death in patients hospitalized due to COVID-19. As vitamin D concentration increased, the odds ratio (OR) for death slightly decreased (OR = 0.978; 95% confidence interval [CI] = 0.540-0.669). The vitamin D concentration cutoff point was 15.40 ng/ml. In addition, patients with COVID-19 and serum 25-hydroxyvitamin D (25(OH)D) concentrations < 30 ng/ml had a lower survival rate than those with serum 25(OH)D ≥ 30 ng/ml (log-rank test p = 0.0018). Moreover, a Cox regression model showed that patients with an estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m2 and higher vitamin D concentrations had a 2.8% reduced risk of mortality (hazard ratio HR = 0.972; CI = 0.95-0,99; p = 0.0097). Conclusions The results indicate an association between 25(OH)D levels in patients with COVID-19 and the final course of hospitalization and risk of death.
Status of exploitation of tuna, mackerel and seerfish in Andaman and Nicobar Islands

Annual fish landing of Andaman and Nicobar Islands during 1989-'99 and the contribution of scombroids viz., tuna, mackerel and seerfishes are described. During the period the exploitation of tuna ranged between 321 to 3,8231, the average contribution being about 5% of the total fish landings. Among the tuna species, yellowfin tuna Thunnus albacares is the major contributor followed by skipjack tuna Katsuwonus pelamis and bigeye tuna Thunns obesus all of which are best caught during March to August. The mackerfel fishery is supported by Rastrelliger brachysoma followed by R.kanagurta. The total mackerel landings ranged between 806 to 1,9551, the major fishing season being from March to June and September to October and is best caught in the gill nets and boat seines. Seerfishes are dominantly represented by two genera and three species vjz. Scomberomorus commerson, S. guttatus and Acanthocybium solandri. Their major fishing season is from March to August and November to Febmary, being generally caught by gill nets, troll lines and hand lines. During the period of 1989 -'99, the total exploitation of seerfishes ranged between 293 to 1,172 t and showed a gradual increase in their production.
On the synthesis and characterization of (bio)ferroelectrically active PVDF-BCP composites

Abstract In this manuscript, an innovative and bioactive polyvinylidene fluoride-biphasic calcium phosphate (PVDF-BCP) ferroelectric composite was synthesized and characterized. FTIR investigations reveal that our synthesis method promotes a deep change in the PVDF structure, leading to the emergency of the ferroelectric polar β-PVDF phase in the PVDF-BCP composite. Microstructural analyzes performed in samples immersed in simulated body fluid reveal an elevated growth rate of synthetic osseous tissue (apatite, ∼15 μm in 7 days) on the composite surface, attesting its enhanced bioactivity. The observation of ferroelectric hysteresis loops attests the polar character of the obtained PVDF-BCP composites.
Determination of Polar Pesticides Based on a Modified QuPPe with Liquid Chromatography Coupled to Tandem Mass Spectrometry

An analytical approach for determining polar pesticides using a Hypercarb column that is based on a modified quick polar pesticide (QuPPe) extraction process combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) was examined. Maleic hydrazide, glyphosate, glufosinate N-acetyl, glufosinate ammonium, fosetyl Al, ethephon, chlormequat chloride, aminomethyl phosphonic acid N-acetyl, aminomethyl phosphonic acid, cyanuric acid, ethylene thiourea, phosphonic acid, propylene thiourea, and acidified methanol solution were used to extract tomato, wheat, olive, sunflower, and herbal tea samples. The amount of solvent, extraction period, and mobile phases used in the experiment were all changed; the analysis included various stationary phases. The method was validated in five matrices spiked at 0.01 and 0.05 mg/kg in accordance with the EU guidance document SANTE/11312/2021 method performance criteria, using six replicates for each concentration for one individual. The limit of detection and limit of quantification (LOQ) values were determined and found to range from 1.82 to 2.44 and 6.07 to 8.13 mg/kg. For all spike levels studied, the approximate recoveries for the pesticides ranged from 85 to 118%, with RSD values of less than 20%. Plant-origin foods from diverse field experiments were effectively processed using the validated approach. This newly developed analytical process can meet the stringent requirements for plant-origin food analysis.
Backchannels Responses as Conversational Strategies in the Interaction of Indonesian Speakers in Interview Setting

In interpersonal communication, the listeners' role is as necessary as the speaker's. Backchannel is considered a universal behavior; it often occurs in the conversation to send a signal from the listener without claiming the floor. Linguists have identified backchannel responses in many languages and cultural differences regarding the backchannel's type, frequency, and placement. This study investigates the use of backchannel in Indonesian conversation by native speakers in interview settings. This mixed-method study combines a quantitative and qualitative study that examines the function and frequency of ten dyadic Indonesian native speakers in an interview setting. The sampling subject participant is 20 Indonesian native speakers with each gender; we selected ten people whose first language is the Indonesian language Each interview session took 20 to 30 minutes, totalling 4 hours, and 20 minutes of dialogue for the whole corpus. Based on the study findings, Indonesian native speakers often use nodding (each gender has the same frequency of around 42%) as a non-verbal backchannel. The current analysis suggests that nodding and facial expressions such as smiles, and laughter consider one of the listener responses in Indonesian conversation. Indonesian speakers' verbal backchannel does occur, but nodding occurs most frequently as a sign of the backchannel. The non-verbal behavior of nodding gives listening signals without bothering the speaker's utterance. The current study adds to our understanding of the listener's role in Indonesian conversation. These results contribute to intercultural understanding in the era of globalization.
The Maternal Microbiome and Gestational Diabetes Mellitus: Cause and Effect

Gestational diabetes mellitus (GDM) is a growing public health concern that affects many pregnancies globally. The condition is associated with adverse maternal and neonatal outcomes including gestational hypertension, preeclampsia, placental abruption, preterm birth, stillbirth, and fetal growth restriction. In the long-term, mothers and children have an increased risk of developing metabolic diseases such as type 2 diabetes and cardiovascular disease. Accumulating evidence suggest that alterations in the maternal microbiome may play a role in the pathogenesis of GDM and adverse pregnancy outcomes. This review describes changes in the maternal microbiome during the physiological adaptations of pregnancy, GDM and adverse maternal and neonatal outcomes. Findings from this review highlight the importance of understanding the link between the maternal microbiome and GDM. Furthermore, new therapeutic approaches to prevent or better manage GDM are discussed. Further research and clinical trials are necessary to fully realize the therapeutic potential of the maternal microbiome and translate these findings into clinical practice.
Prevalence of voice handicap among nurses in intensive care units due to occupational noise during pandemic

Background Healthcare workers have been identified as being at risk of occupational voice disorders. Among them, nurses working in intensive care units (ICUs) are particularly vulnerable due to the risk factors that are associated with their exposure to high levels of noise. Thus, this study aimed to determine the prevalence of voice disorders among ICU nurses. Methods A questionnaire was administered to 100 ICU nurses from four hospitals in China. The questionnaire assessed vocal-related symptoms, perceived voice handicap, frequently heard noise sources, and the quality of communications. Results Results indicate that the most frequently reported voice symptoms were ‘voice tiredness’ and ‘voiceless’. Nurses working more than 50 h per week experienced voice symptoms more frequently than nurses working for 40–50 h per week. The median value of the perceived voice handicap score (VHI-30) was 23, indicating mild voice handicap, while 24% of the nurses reported severe voice handicap. Longer working hours and working at patient wards were significantly associated with higher VHI-30 scores. The nurses also reported that the quality of verbal communication with patients and colleagues and voice problems worsened during the COVID-19 pandemic. Conclusion More than 20% of nurses reported severe voice handicap, however, voice handicap among ICU nurses did not appear universally to all nurses. Further research is necessary to identify the risk factors associated with voice disorders and the mechanism behind such heterogeneity among ICU nurses.
A random walk method for the simulation of macrodispersion in a stratified aquifer

A numerical method is proposed for solute transport in two-dimensional horizontal groundwater flow in a stratified aquifer. The convective component of the solute flux is calculated by a Runge-Kutta integration of the motion equations of individual particles. Vertical variations of hydraulic conductivity are taken into account. The dispersive component of the solute flux is simulated by a random walk. The method can be used for uniform as well as for non-uniform flow. For uniform flow an analytical analysis is given by Gelhar. Numerical results for the uniform case are in good agreement with Gelhar's analytical solution. Several examples are given for different flow problems, including flow towards a pumping well. Une méthode de déplacement aléatoire pour simuler la dispersion macroscopique dans une nappe aquifère stratifée RESUME Une méthode numérique est proposée pour le transfert de corps en solution dans un écoulement bidimensionnel horizontal de l'eau souterraine dans une nappe aquifère stratifée. La composante convective du débit de surface des corps en solution est calculée par intégration numérique (Runge-Kutta) des équations de mouvement des particules individuelles. La variation verticale de perméabilité est prise en compte. La composante dispersive du débit de surface de soluté est simulée par un déplacement aléatoire. La méthode peut être utilisée pour un écoulement uniforme et non-uniforme. Pour le cas d'un écoulement uniforme une analyse a été donnée par Gelhar. Les résultats numériques pour le cas d'écoulement uniforme sont en concordance avec la solution analytique de Gelhar. Plusieurs exemples sont données pour des problèmes différents d'écoulement, y compris l'écoulement vers un puits de pompage.
English in the Caribbean: References

What is English in the Caribbean? This is by no means as obvious as it may appear at first sight, and after discussing a few examples of variation to set the scene, the present introductory chapter will therefore present some general considerations relating to this question; the Dictionary of Caribbean English Usage (Allsopp ) will provide a frame of reference for this discussion, and a central concept in the study of language variation in the Caribbean, the Creole continuum, will be introduced. The chapter will then zoom in on the specific concerns of the present study, standards and style in English in the Caribbean, providing an overview of existing research and highlighting central issues that need to be addressed. On that basis the research questions for the present study can then be formulated.
Do macrophages play a role in the adverse effects of endocrine disrupting chemicals (EDCs) on testicular functions?

During the past decades, several endocrine disrupting chemicals (EDCs) have been confirmed to affect male reproductive function and fertility in animal studies. EDCs are suspected to exert similar effects in humans, based on strong associations between levels of antiandrogenic EDCs in pregnant women and adverse reproductive effects in infants. Testicular macrophages (tMΦ) play a vital role in modulating immunological privilege and maintaining normal testicular homeostasis as well as fetal development. Although tMΦ were not historically studied in the context of endocrine disruption, they have emerged as potential targets to consider due to their critical role in regulating cells such as spermatogonial stem cells (SSCs) and Leydig cells. Few studies have examined the impact of EDCs on the ability of testicular cells to communicate and regulate each other’s functions. In this review, we recapitulate what is known about tMΦ functions and interactions with other cell types in the testis that support spermatogenesis and steroidogenesis. We also surveyed the literature for reports on the effects of the EDCs genistein and DEHP on tMΦ, SSCs, Sertoli and Leydig cells. Our goal is to explore the possibility that EDC disruption of tMΦ interactions with other cell types may play a role in their adverse effects on testicular developmental programming and functions. This approach will highlight gaps of knowledge, which, once resolved, should improve the risk assessment of EDC exposure and the development of safeguards to protect male reproductive functions.
Automated Detection of Retinopathy of Prematurity Using Quantum Machine Learning and Deep Learning Techniques

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease that affects premature infants and causes permanent blindness if left untreated. Automated retinal diagnosis from the Retinal fundus images aid in the early detection of many pathological conditions. The low-level statistical features used in literatures have not provided the complete ROP-specific profile, and hence it has to be replaced by high-level features. The proposed system involves extracting Scale Invariant Feature Transform (SIFT) - Speeded Up Robust Features (SURF) combined high-level features from the SegNet segmented retinal vessels and classified using the Quantum Support Vector Machine (QSVM) classifier. This study aims (i) to segment retinal vessels from the acquired fundus images using SegNet and extract their features using the SURF and SIFT Feature Extraction method, (ii) to classify the Normal and ROP retinal vessels using four classical machine learning classifiers such as Support Vector Machine (SVM), Reduced Error Pruning (REP) tree, K-Star, and LogitBoost and Quantum SVM classifier, (iii) to develop a novel transformer-based Swin-T ROP model to classify ROP from normal Neonatal fundus images, (iv) to compare the performance characteristics of the proposed QSVM model with the Resnet50, DarkNet19, and classical machine learning classifiers. The study is conducted using 200 fundus images, including 100 normal and 100 ROP-positive neonatal retinal images. The machine learning classifiers such as SVM, REP Tree, K-Star, and Logit Boost Classifiers attained accuracy of 86.7%, 75%, 74%, and 76.5%, respectively, in classifying ROP from normal retinal images. The deep learning networks such as ResNet50 and DarkNet19 classified ROP from normal fundus images with an accuracy of 92.87% and 89%, respectively. The Quantum machine learning classifier outperforms the classical machine learning classifiers, Pre-trained Convolutional Neural Networks (CNN) and SwinT-ROP in terms of classification accuracy (95.5%), sensitivity (93%), and specificity (98%). The proposed system accurately diagnoses ROP from the neonatal fundus images and could be used in point-of-care diagnosis to access diagnostic expertise in underserved regions.
The effects of anisotropic pressure on plasma displacement and its deviation from flux surfaces

A significant impact of pressure anisotropy on the plasma displacement associated with ideal Magnetohydrodynamic (MHD) stability is found. A heliotron plasma, such as a large helical device plasma, is analyzed. Simulations are performed using the equilibrium solver Anisotropic Neumann Inverse Moments Equilibrium Code and the ideal MHD stability code TERPSICHORE. Both codes provide a treatment of the pressure anisotropy by the bi-Maxwellian model. The ratio of hot particle pressure to total pressure / has been scanned over. Other simulation parameters have been chosen such that the simulations represent an experimentally relevant condition with an external, off-axis heating scheme. The radial location of the peak of the plasma displacement of the n = 1, m = 2 mode number has been compared to the radial location of the ι = 0.5 resonant surface. This comparison shows that this difference in location increases monotonically for increasing / . These results provide insight on the effect of external heating schemes on MHD stability.
The Impact of U.S. Monetary Policy on Foreign Firms

This paper uses cross-country firm-level data to explore the impact of U.S. monetary policy shocks on firms’ sales, investment, and employment. We estimate a significant impact of U.S. monetary policy on the average foreign firm, while controlling for other macroeconomic and financial variables like the VIX and exchange rate fluctuations that accompany U.S. monetary policy changes. We then estimate the role of international trade exposure and financial constraints in transmitting monetary policy shocks to firms, allowing for a better identification of the importance of external demand effects and the financial channel. We first exploit cross-country-sector-level data on intermediate and final goods to show that greater global production linkages amplify the impact of U.S. monetary policy at the firm level. We then show that the impact varies along the firm-level distribution of proxies for firms’ financial constraints (e.g., size and net worth), with the impact being significantly attenuated for less constrained firms.
Identification of Influential Climate Indicators, Prediction of Long-term Streamflow and Great Salt Lake Elevation Using Machine Learning Approach

Sea surface temperature (SST) has significant influence in the hydrological cycle which eventually affects the discharge in streams. SST is an atmospheric circulation indicator which provides the predictive information about the hydrologic variability in regions around the world. Choosing the right location of sea surface temperature for the prediction of streamflow at a specific location of the gage is crucial. The use of the correct location of SST for the selected stream gage appropriately captures the effect of oceanic-atmospheric interaction, which eventually improves the predictive ability of the model. The strength of the effect of SST changes spatially, thus, the influential locations of SST for different locations of the stream gages will be different. This chapter aims on identifying appropriate locations of sea surface temperature at selected stream gage in the state of Utah that spatially covers the state from South to North, and use them for longterm streamflow prediction. Analysis shows the influence of Pacific Ocean SST to be stronger than that of Atlantic Ocean SST in the state of Utah. Using appropriate location of SST, an accurate and reliable long-term streamflow can be predicted, which may play an important role on water resources planning and management in the river basin scale. This information provides how much water will be available in the next season so that the 10 water managers, stakeholders and farmers can plan accordingly. Predicting future water availability accurately and reliably is a key step for successful water resource management in arid regions. A data-driven model derived from statistical learning theory is used in this chapter. This model relates input/output without trying to understand the underlying physical process. The model used in this chapter is developed in the form of a Multivariate Relevance Vector Machine (MVRVM). Using the best identified SST locations, along with local climatic condition and the current state of basin, both monthly mean discharge and volume of water passing the gage for the next six months are predicted. Monthly mean discharge is usually predicted best by the North Pacific SST for Northern and Central Utah, while the Tropical Pacific SST develops the best model for Southern Utah. For volumetric prediction, the North Pacific SST develops the best model prediction in most of the selected stream gages in the state of Utah. Each model is demonstrated to be robust by the results of a bootstrap analysis. 2.
Exploring the Effects of Game-based Learning

Abstract :Advancements in technologies are helping the education system to improve and explore a new dimension of learning. Learning beyond class hours is an important issue. Traditional textbooks and learning materials (online as well as offline) may help to overcome this challenge for theory-based papers. On contrary, experimenting in a lab beyond the class hour is challenging, more specifically in schools. The effect of the COVID pandemic also forced the researchers to explore new ways of learning, particularly in lab work. In this work, an interactive virtual Lab was developed for school students. They can virtually perform Ohm's Law experiments using a mobile phone or computer. The proposed lab was designed using Unity Engine with C#. In order to check the acceptance of the proposed applications, we conducted a usability evaluation. It was observed that students learned more effectively using our proposed application. Keywords : Simulation, 3D Model, Virtual Reality, Usability Testing.