Miracle-dz/UniFAR-FTUs huggingface dataset:
BIA ? Tool for evaluating the success of reduce the body weight

T objective of our work in to unravel the relationships between melatonin and the Metabolic Syndrome (MS) with emphasis in the possible therapeutic role of the methoxyindole. Being the MS a global pandemia, with a rising prevalence close to 30%, we, as others, believe that it is not only related to an unadequate nutrition and sedentarism, but also, to an unbalance between the current life style and natural light-dark cycles. Prolonged exposures to light during night and sleep impairment negatively affect many vital processes, as shown by a decreased melatonin production, modified circadian rhythms and oxidative overload. As an accepted experimental model of MS, we use male Wistar rats fed with regular chow and 10% fructose drinking solution ad libitum. After 8 weeks, the MS phenotype is developed. The administration of melatonin (25 μg/mL drinking solution) given to rats simultaneously with fructose was employed as a paradigm to analyze its effect on different metabolic consequences of MS. Along with the progression markers of MS, we also studied oxidative damage on adipose tissue, as a main player in this scene. We evaluated lipid peroxidation by TBARs determination and the oxidative status by glutathion levels (GSSG/GSH). As compared to control, body weight and systolic blood pressure augmented significantly, and an impaired glucose tolerance was detected as shown by the increases in glycemia, insulinemia and leptinemia. Circulating triglyceride, cholesterol and LDL-c concentration also augmented significantly. Melatonin counteracted the changes in body weight and systolic blood pressure in rats. Melatonin also decreased glycemia, insulinemia and leptinemia and it was effective to counteract the changes in plasma LDL-c, triglyceride and cholesterol, augmented HDL-c levels and decreased plasma uric acid levels. The results underline the possible utility of melatonin in the treatment of MS.
Teamwork in Pediatric Resuscitation: Training Medical Students on High-Fidelity Simulation

Background Simulation training and teamwork for medical students are essential to improve performance in pediatric cardiopulmonary resuscitation. Purpose To evaluate if a specific approach to teamwork improves technical and nontechnical performance. Methods We performed quasiexperimental, prospective, pre- and postinterventional, and nonrandomized research with 65 students in the fourth year of their medicine course. This was a case–control study in which teams used a customized TeamSTEPPS protocol (n=34) or not (n=31) for cardiopulmonary arrest training in children using high-fidelity simulation. All participants answered a sociodemographic and satisfaction questionnaire and underwent theory and practice pre- and posttesting. The survey data were collected in 2019 and analyzed using χ2, Mann–Whitney, κ, and Wilcoxon tests. p<0.05 was considered significant. Results Intervention and control groups achieved better scores in theory posttesting (p<0.001 and p=0.049), but there was no difference between them in pre- (p=0.291) and posttesting (p=0.397). In the checklist of the practice test, all groups obtained their best outcomes in posttesting and the intervention group achieved higher scores (p<0.001). All groups increased the number of teamwork events and reduced the time span to perform resuscitation first steps (p<0.001) in posttesting. Conclusion The use of teamwork training based on a customized TeamSTEPPS protocol improved performance in team behavior and group technical achievement. The evaluation of the students about the training was positive.
The Trichoptera of Panama. XIX. Additions to and a review of the genus Leucotrichia (Trichoptera, Hydroptilidae) in Panama

﻿Abstract Prior to 2016, three species of caddisflies in the genus Leucotrichia (Trichoptera: Hydroptilidae) were known from Panama. Subsequently, one new species and four new country records were added to Panama’s fauna. Herein, four new species are described (Leucotrichiacortaderasp. nov., L.holzenthalisp. nov., L.lumasp. nov., L.ruiterisp. nov.) and two new country records added for Panama (L.botosaneanui Flint, 1996, L.hispida Thomson & Holzenthal, 2015). The resulting total of 14 species makes Panama the most species-rich country for this genus. Panama’s species assemblage is most similar to Costa Rica and Mexico. However, the similarities among faunas in all these countries is very low (< 35%). Thus, more new country records are possible with additional collecting. Recent collections (2015–2021) of new caddisfly species and country records in this genus were effected primarily by use of Malaise traps. Our collections also evidenced multiple species from the same collecting site, with seven species each found in both lowland and mid-altitude sites. Investigation of the distribution of Leucotrichia species with altitude reveals a preference by several species for higher altitude locations. Additional Malaise trap collections over extended time periods are needed to verify the validity of all observations and preliminary conclusions made to date.
Intra- and Inter-Day Reliability of the NIRS Portamon Device after Three Induced Muscle Ischemias

(1) Background: Near-infrared spectroscopy (NIRS) is an innovative and non-invasive technology used to investigate muscular oxygenation. The aim of this study is to assess the within- and between-session reliability of the NIRS Portamon (Artinis, Elst, Netherlands) device following three sets of induced muscle ischemia. (2) Methods: Depending on the experimental group (G1, G2 or G3), a cuff was inflated three times on the left upper arm to 50 mmHg (G1), systolic blood pressure (SBP) + 50 mmHg (G2) or 250 mmHg (G3). Maximum, minimum and reoxygenation rate values were assessed after each occlusion phase, using a Portamon device placed on the left brachioradialis. Reliability was assessed with intraclass correlation coefficient (ICC) value and ICC 95% confidence interval (CI-95%), coefficient of variation (CV) and standard error of measurement (SEM) (3) Results: Our results showed a good to excellent reliability for maximums and minimums within-session. However, the reoxygenation rate within sessions as well as measurements between sessions cannot predominantly show good reliability. (4) Conclusions: Multiple measurements of maximums and minimums within a single session appeared to be reliable which shows that only one measurement is necessary to assess these parameters. However, it is necessary to be cautious with a comparison of maximum, minimum and reoxygenation rate values between sessions.
Polarimetric Wideband Directional ChannelMeasurement and Analysis for Outdoor Small CellScenarios at 32 GHz and 39 GHz

Wideband millimeter-wave (mmWave) directional propagation measurements were conducted in the 32 GHz and 39 GHz bands in outdoor line-of-sight (LoS) small cell scenarios. The measurement provides spatial and temporal statistics that will be useful for small-cell outdoor wireless networks for future mmWave bands. Measurements were performed at two outdoor environments and repeated for all polarization combinations. Measurement results show little spread in the angular and delay domains for the LoS scenario. Moreover root-mean-squared (RMS) delay spread at different polarizations show small difference which can be due to specific scatterers in the channel.
Comparison of Climate Model Large Ensembles With Observations in the Arctic Using Simple Neural Networks

Evaluating historical simulations from global climate models (GCMs) remains an important exercise for better understanding future projections of climate change and variability in rapidly warming regions, such as the Arctic. As an alternative approach for comparing climate models and observations, we set up a machine learning classification task using a shallow artificial neural network (ANN). Specifically, we train an ANN on maps of annual mean near‐surface temperature in the Arctic from a multi‐model large ensemble archive in order to classify which GCM produced each temperature map. After training our ANN on data from the large ensembles, we input annual mean maps of Arctic temperature from observational reanalysis and sort the prediction output according to increasing values of the ANN's confidence for each GCM class. To attempt to understand how the ANN is classifying each temperature map with a GCM, we leverage a feature attribution method from explainable artificial intelligence. By comparing composites from the attribution method for every GCM classification, we find that the ANN is learning regional temperature patterns in the Arctic that are unique to each GCM relative to the multi‐model mean ensemble. In agreement with recent studies, we show that ANNs can be useful tools for extracting regional climate signals in GCMs and observations.
A bidirectional examination of mental health symptoms and perceptions of leader support: Which comes first?

ABSTRACT Leader support for psychological health (LSPH) has been identified as an important factor in the prediction of mental health symptoms among warfighters. Although research has examined the relationship between LSPH and mental health symptoms, the extent to which this relationship is bidirectional has been underexplored. Consequently, the present study examined the longitudinal relationships between perceived LSPH and mental health symptoms (depression and PTSD) among military personnel over a 5-month period. We found that perceived LSPH at Time 1 (T1) was associated with fewer mental health symptoms at Time 2 (T2); however, mental health symptoms at T1 were also associated with lower perceptions of LSPH at T2. The results differed slightly based on the type of symptoms experienced, but the relationships between perceived LSPH and symptoms did not vary based on whether soldiers had been exposed to combat. However, it is important to note that the overall sample had low combat experience. Despite this, these findings may suggest that the assumption that leader support can enhance soldier mental health may fail to consider that the symptoms themselves may also affect how leaders are perceived. Therefore, organizations such as the military should consider both directions to optimally understand the relationship between leaders and subordinate mental health.
stringi: Fast and Portable Character String Processing in R

Effective processing of character strings is required at various stages of data analysis pipelines: from data cleansing and preparation, through information extraction, to report generation. Pattern searching, string collation and sorting, normalization, transliteration, and formatting are ubiquitous in text mining, natural language processing, and bioinformatics. This paper discusses and demonstrates how and why stringi, a mature R package for fast and portable handling of string data based on ICU (International Components for Unicode), should be included in each statistician’s or data scientist’s repertoire to complement their numerical computing and data wrangling skills.
Taking stock and moving forward: A textual statistics approach to synthesizing four decades of job insecurity research

We collected the abstracts of manuscripts examining job insecurity published between 1984 and 2019 and carried out a textual analysis to investigate the defining clusters, their development over time, and whether there was evidence of siloed knowledge. Results suggested that job insecurity research seems to be fragmented into disciplinary foci (organizational psychology, public health, economics, and sociology). Further analyses on the organizational psychology corpus, revealed 25 topics with distinct temporal trajectories: some were increasing (analytical advances and differentiation between cognitive and affective job insecurity) while other were decreasing (scale development). The remaining abstracts revealed 15 topics with more stable trajectories. Based on these results, we identified five areas for future organizational research on job insecurity: the changing labor market, the need to better understand the experiences of marginalized workers and non-work outcomes of job insecurity, the added-value of qualitative research, and the need to critically evaluate our assumptions as researchers. Plain Language Summary Since the paper by Greenhalgh and Rosenblatt, research on job insecurity has burgeoned. Taking an interdisciplinary perspective, we collected the abstracts of all peer-reviewed manuscripts examining job insecurity published between 1984 and 2019 and carried out a textual analysis using the Latent Dirichlet Allocation and the Reinert method to investigate (a) the defining clusters of job insecurity research, (b) the development of such clusters over time, and (c) whether there was any evidence of siloed knowledge. Results suggested that indeed job insecurity research seems to be fragmented into four main disciplinary foci (organizational psychology, public health, economics, and sociology) with relatively little cross-fertilization. We conducted further analyses of the abstracts stemming from organizational research on job insecurity, revealing 25 topics with distinct temporal trajectories (e.g., “hot” topics including the increasing use of advanced analytic techniques and differentiation between cognitive and affective job insecurity) and “cold” topics including the development of job insecurity measures). The remaining abstracts revealed 15 topics with more stable research interests over time (e.g., a continued reliance on appraisal theories). Based on these results, we identified five areas for future organizational research on job insecurity based on: the changing labor market, the need to better understand the experiences of marginalized workers and non-work outcomes of job insecurity, the added-value of qualitative research, and finally the need to critically evaluate our assumptions as researchers.
The God of Big (And Good) Things: Religious Priming, Event Properties, and Supernatural Explanations

ABSTRACT Humans have a pervasive tendency to make causal attributions when attempting to explain life events. While causality is often attributed to naturalistic forces, such as the agency of individuals or situational factors, many individuals – particularly those who hold religious or spiritual beliefs – invoke supernatural explanations that ascribe causation to invisible forces or agents such as God(s), fate, or karma. The present research sought to clarify conflicting findings regarding the types of events that tend to elicit supernatural explanations, and to investigate whether reminders of religion exerts a causal effect on these tendencies using priming methodologies. Over two studies (N = 119; N = 121), we observed a consistently greater tendency to invoke supernatural explanations of various kinds when event outcomes were serious and positive. A significant influence of religious priming was observed only for one type of supernatural explanation in Study 1, while inconsistent interactions between valence and seriousness were also observed. These findings are consistent with the notions of “God-of-the-gaps” and God-serving attributional biases, although evidence for a causal influence of reminders of religion on these processes remains inconclusive.
Statistical Analysis of Medium‐Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation

Medium‐scale traveling ionospheric disturbances (MSTIDs) are observed as parallelly arrayed wavelike perturbations of Total Electron Content (TEC) in ionospheric F region leading to satellite navigation error and communication signal scintillation. The observation method for MSTIDs, detrended TEC (dTEC) map, summarizes the perturbation component of TEC having the merits of full‐time and two‐dimensional. However, previous automatic processing methods for dTEC map cannot discriminate MSTIDs from other irregular ionospheric perturbations intelligently. With the development of artificial intelligence in recent years, deep learning approach is expecting to clarify the controversy of MSTID external dependence (season and solar/geomagnetic activity) under debating for decades. Therefore, this research proposes a real‐time processing algorithm for dTEC maps based on Mask Region‐Convolutional Neural Network (R‐CNN) model of deep learning instance segmentation to detect wavelike perturbations intelligently with an accuracy of about 80% and a processing speed of about 8 fps. Then isolated perturbations are eliminated and only MSTID waveforms are chosen to obtain statistical characteristics of MSTIDs. With this algorithm, we analyzed up to 1,209,600 dTEC maps from 1997 to 2019 over Japan automatically and established a database of hourly averaged MSTID characteristics. This research introduces the partial correlation coefficient for the first time to clarify the solar/geomagnetic activity dependence of MSTID characteristics which is independent with each other.
How Does the Language Control of L1 and L2 Writers Develop Over Time in First-Year Composition?

Most U.S. colleges and universities expect students to improve their writing ability by taking first-year composition (FYC) courses. In such courses, non-native English (L2) writers with diverse language backgrounds study alongside their native English (L1) speaking peers. However, it is not clear how different these populations are in terms of their language development over time, leaving questions unanswered about whether L2 writers develop more or less than L1 writers in an FYC curriculum. To investigate, we compared 75 L1 and L2 students’ written accuracy, fluency, and lexical and syntactic complexity over the semester of an FYC course. Data showed that L2 students had significantly higher rates of language error and less fluent and lexically complex writing compared to L1 writers. Moreover, L2 student writing became less grammatically accurate over 14 weeks despite showing greater fluency and syntactic complexity. These results suggest a need for plurilingual pedagogies in FYC that embrace diversity and inclusion while also providing L2 writers with instruction on socially powerful and dominant linguistic forms.
Regional convergence in Bangladesh using night lights

ABSTRACT We analyse economic convergence across 64 districts of Bangladesh using newly harmonized satellite night light data over 1992–2018. The growth in night lights – taken as a proxy for regional economic activity – reveals overwhelming evidence of absolute convergence. Regional differences in night light (or income) growth have been shrinking at an annual convergence rate of 4.57%, corresponding to a half-life of 15 years. Net migration plays a relatively prominent role in the regional convergence process.
Economic policy uncertainty and industry portfolio returns in the United States

ABSTRACT This paper examines whether returns on 49 different industry portfolios in the United States (US) expose significantly to the US economic policy uncertainty (EPU) even after controlling for the market and firm-specific risk factors. We find that the US EPU can load significantly against the returns of 15 industry portfolios which include stocks from heavy manufacturing and export dependent industries. However, further asset pricing tests show that the US EPU cannot improve the ability of a benchmark model to capture average industry returns. Additionally, the impact of EPU is time-dependent and significant only during specific periods which are different for each industry. Using a portfolio analysis, we also test whether EPU can forecast future returns. Results imply that the return-EPU relationship is almost flat.
Switching the decay rate of an emitter inside a cavity in the time domain

We have theoretically studied the deterministic switching in the time domain of the spontaneous emission of two-level emitters in an optical microcavity. We consider a system in the weak coupling regime, for which the refractive index of the cavity material is modified on a time scale shorter than the emitted lifetime, through the injection of free charge carriers. The equation of motion is introduced for the excited population of two-level emitters coupled to a time-dependent local density of optical states. We then derived the emitted intensity as a function of time for both continuous wave and pulsed excitation of the embedded emitters. We demonstrate that a short temporal increase of the radiative decay rate drastically increases the emission intensity during the switch time for both continuous wave excitation and pulsed excitation. For pulsed excitation, the resulting spontaneous emission shows a distribution of photon arrival times with a deterministic burst of photons during the switch event, that strongly deviates from the standard exponential law. Finally, we derive a figure of merit that quantifies the total switching action.
Investigation of Thermal and Mechanical Properties of Aramid Fiber Reinforced Thermoplastic Polyurethane Elastomer Composites

Along with the emergence of thermoplastic polyurethane materials with its elastomeric behavior, there has been an improvement in the production of composites with functional properties by adding additives as well as advantages such as high production rates and diversity. In this study, thermal and mechanical behaviors of thermoplastic polyurethane (TPU) elastomer reinforced with aramid fiber (Kevlar) fabrics with different layers were investigated. Particular emphasis has been placed on the optimization of parameters such as good distribution and impregnation of TPU elastomers used as reinforcements on Kevlar interfaces. Composite samples prepared in 1, 2 and 3 layers of Kevlar fabric by using hot pressing method which were heated at 180 °C under the pressure of 10 MPa for 20 minutes to obtain the final product. Optical, modulus of elasticity, tensile strength, % elongation and thermal conductivity values of TPU reinforced Kevlar composite materials with different layer ranges are compared. It is concluded that the high mechanical strength and the high thermal conductivity values are associated with the amount of thermoplastic polyurethane materials between the kevlar sheets in few layered composite structures. It is also seen that the mechanical strength strongly depends on pore volume, adhesion on fiber-matrix interfaces and matrix concentration on aramid fiber reinforced thermoplastic polyurethane composites.
Antimicrobial Resistance Patterns of Bacterial and Fungal Isolates in COVID-19

Background: The pattern of bacterial infection in coronavirus disease 2019 (COVID-19) patients differ worldwide. Objectives: This study aimed to determine the patterns of bacterial infections and the antibiotic resistance profile by VITEK 2 (bioMérieux, France) in the culture of blood samples from hospitalized patients with COVID-19. Methods: This retrospective descriptive cross-sectional was conducted on a total of 25 patients with critical COVID-19 admitted to Imam Reza Hospital in Mashhad, Iran, during the first three COVID-19 peaks (2019 - 2020). Results: Among Gram-positive bacteria, two strains isolated from Staphylococcus aureus were methicillin-resistant S. aureus at a concentration of > 2 μg/mL. Enterococcus was vancomycin-resistant Enterococcus at a concentration of higher than 4 μg/mL (the minimum inhibitory concentration [MIC] ≥ 32). Among Gram-negative bacteria, three strains of Acinetobacter baumannii complex were extensively drug-resistant. Conclusions: There is evidence of the remarkable increase of various antibiotics’ MIC during the COVID-19 pandemic, which highlights the impact of the use of steroids on the risk of developing antimicrobial resistance during the COVID-19 pandemic.