Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Primary School Physical Education Facilities and Infrastructure in the Regional Office of Education in Patuk Sub-District, Gunungkidul District

The purpose of this study was to evaluate the facilities and infrastructure used in learning Physical Education Sports and Health in Elementary Schools in the Kapanewon Patuk Regional Office of Education, Gunung Kidul using the CIPP model which includes aspects of Context, Input, Process, and product. The research subjects consisted of principals and teachers. Data collection methods included documentation, observation, and interviews. Qualitative and quantitative data were analyzed using descriptive techniques. The results showed that the condition and feasibility of facilities and infrastructure for game sports are still not good with an interval value of 49%, while the interval value for gymnastics and athletics are 31% and 26% respectively, with the overall interval value of the three types of sports is 35%. Fulfillment of standardization is also still not good with a value of 27%. However, the implementation of Physical Education, Sports, and Health learning is good with a score of 67% and the learning outcomes of Physical Education, Sports, and Health reached 75%. Therefore, although there are still shortcomings, the creativity and innovation of teachers in teaching have made the learning process of Physical Education, Sports, and Health run well and the fulfillment of the minimum completeness criteria for Physical Education, Sports, and Health subjects is fulfilled. The suggestion given is that there is a need for improvement and improvement in facilities and infrastructure, fulfillment of standards stipulated in regulations, use of appropriate learning methods and techniques, and increasing student involvement in sports activities to improve the quality of learning Physical Education, Sports, and Health in Elementary Schools in the Kapanewon Patuk Regional Office of Education, Gunungkidul Regency. In addition, special improvements need to be made to improve students' learning achievements in sports that are still poor through additional training or intensive activities.
Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A “non-invasive” method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4–1 μm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.
PECULIARITIES OF THE COURSE OF THE DISEASE IN WOMEN WITH COVID-19

One of the most crucial topics at the moment is the viral infection caused by the SARS-CoV-2 coronavirus, which has acquired not only medical, but also, of course, social significance. The COVID-19 pandemic has challenged healthcare systems around the world.Pregnant women are the most vulnerable category of people, along with the elderly. Due to low immune reactivity, they are more susceptible to severe complications than others, which makes the problem of pregnancy in patients with COVID-19 a pressing issue.A successful pregnancy requires changes in the immune system of the pregnant woman to tolerate a genetically foreign fetus. These changes in the immune system, as well as in the cardiac, pulmonary, and other systems, can lead to increased susceptibility or increased morbidity and mortality due to infection during pregnancy. Considering the physiological adaptations associated with pregnancy, we found that the high metabolic demand to maintain normal intrauterine development increases the burden of oxidative stress during pregnancy. Intracellular redox changes associated with acute phase reactions at the maternal-fetal interface may be enhanced during pregnancy. Notably, mother-to-fetal transmission of SARS-CoV-2 has not been detected in most COVID-19 pregnancies. This relative absence of vertical transmission may be due to the presence of lactoferrin in the placenta, amniotic fluid, and breast secretions. However, the cytokine storm induced during COVID-19-associated pregnancies can cause severe inflammatory damage to the fetus and, if left uncontrolled, can subsequently lead to autism spectrum disorders and brain developmental abnormalities in newborns. Given this serious health threat to a child's growth and development, preventing COVID-19 during pregnancy should be a priority.We aimed to study risk factors and assess the course of pregnancy in women with COVID-19.The study was carried out within the framework of the research work "Clinical and pathogenetic substantiation of differentiated treatment of combined pathology of internal organs" (№0122U002209).
Number of FLOPs of Training DNNs for Learning Precoding

Deep neural networks (DNNs) have been widely used for learning precoding policy to achieve good system performance with low computational complexity. However, existing DNNs for learning precoding cannot be generalized to the number of users. Consequently, the DNNs have to be retrained frequently, which incurs unaffordable computational cost for training, especially when the number of antennas is large. As a metric of time complexity, the number of floating-point operations (FLOPs) has been analyzed for inference in literature. Yet the time complexity of training DNNs for learning wireless policies has only been evaluated in terms of running time. To gain useful insight into developing deep learning methods with low-cost training, in this paper we derive the numbers of FLOPs of training three DNNs used for learning precoding, which are circular convolution neural network (CCNN), linear convolution neural network with fully-connected layers (F-LCNN), and fully-connected neural network (FNN). We then compare them with those of inference. Analytical results show that the ratio of the approximate numbers of FLOPs for training to those for inference depends on the numbers of epochs and training samples. Simulation results show that the time complexity of training scales with the number of antennas faster than that of inference, and the complexity of training the F-LCNN and FNN scales faster than that of training the CCNN.
Respiratory Rate Estimation Based on Detected Mask Area in Thermal Images

The popularity of non-contact methods of measuring vital signs, particularly respiratory rate, has increased during the SARS-COV-2 pandemic. Breathing parameters can be estimated by analysis of temperature changes observed in thermal images of nostrils or mouth regions. However, wearing virus-protection face masks prevents direct detection of such face regions. In this work, we propose to use an automatic mask detection approach to select pixels within a mask region as a source of respiration information allowing efficient estimation of respiratory signals. We performed experiments with two important types of virus protection masks, i.e., FFP2 (N95) and surgical masks, for subjects while sitting, slowly walking from a short distance toward a camera, and slowly walking with moderate head movements. Experiments conducted with the adapted YOLO model have shown that detection of the mask area on the face allows for higher SNR values and reduces error in respiratory rate estimation in all analyzed scenarios. The Mean Absolute Error for respiratory rate estimation was below 1 bpm for sitting subjects for all types of masks. The error for walking subjects was 1.21 bpm for an FFP2 mask and about 2.1 bpm for a surgical mask. In the presence of head movements, while walking, the MAE was below 1.39 bpm and less than 1 bpm when only one outlier was removed.
Social attention to the scientific publications on mHealth research during Covid-19 outbreak: An altmetric study

Objective. This study aimed to assess the social attention received by the top 100 highly cited scientific publications focusing on mHealth (mobile health) research during the COVID-19 outbreak. Design/Methodology/Approach. This study employed altmetric tools to assess the social attention received by mHealth research publications. The study collected bibliographical data of the top 100 highly cited articles published between 2019 and 2022 from the Scopus database. Altmetric data was collected from the Dimensions.ai database and analyzed using MS Excel, Tableau, and SPSS software. Results/Discussion. The study found that mHealth research has received significant social attention from various social media, mass media and reference manager platforms. However, it needs to be dissemination and promotion to reach wider audiences. Twitter was the leading channel for disseminating research highlights on mHealth, and articles have a significant number of readers on the Mendeley platform. However, correlation analysis revealed a weak positive correlation between citation and AAS of mHealth research publications. Conclusions. The study contributes to understanding the societal impact of mHealth research during the COVID-19 outbreak and emphasizes the role of Altmetric tools in assessing social attention in scientific publications. The study concludes by suggesting future research directions in the field.
Return mapping scheme for the Hoek-Brown model with tension cut-off

The present paper revisits a stress update procedure for the Hoek-Brown plasticity model enhanced by a Rankine type of tension cut-off failure criterion. Limiting tensile stresses not only supports the behavior of weak rock masses with a very low tensile strength but in combination with the original Hoek-Brown model improves robustness of the stress update procedure, i.e. the stress return mapping algorithm. Herein, the primary focus is on the stress return from a space of inadmissible trial stresses for which neither the Hoek-Brown yield surface nor its derivative can be evaluated. All potential scenarios are thoroughly discussed both in the framework of single- and multi-surface plasticity. The presented procedures were implemented and verified with the help of the Geo5 FEM software.
PROBIOTICS BACTERIA AS QUORUM SENSING DEGRADER CONTROL Aeromonas hydrophila PATHOGENICITY IN CULTURED RED HYBRID TILAPIA

Quorum sensing (QS) is the interaction of bacteria cell-to-cell to regulate various bacterial functions, including bacterial virulence. It has been suggested that QS interruption is an anti-infective strategy to combat diseases in aquaculture. This research was conducted to isolate the potential probiotics bacteria as QS degrader from the fish gut and investigate its efficiency in reducing Aeromonas hydrophila pathogenicity by in vitro and in vivo assay. In this study, three isolates from the fish gut were able to degrade N-acyl homoserine lactone (AHL), one of the QS signals of A. hydrophila. Based on 16S rDNA sequence analysis, isolate CPi12 was identified as Klebsiella sp. , CBa5 and CBa7 were identified as Enterobacter tabaci. These isolates were co-cultured with A.hydrophila for five days. Results revealed that all isolates could decrease the AHL production of A. hydrophila but did not affect the growth of the pathogen. In vivo assay results showed that isolate CPi12 reduced the pathogenicity of A.hydrophila against tilapia with a significantly higher survival rate (p<0.05) of fish in the CPi12-fed group than the control group. Meanwhile, isolate CBa7 was significant (p<0.05) for growth performance, including specific growth rate (SGR), weight gain (WG), feed conversion ratio (FCR), mean weight gain (MWG), and average daily growth (ADG) among whole treatment and control group. These results displayed that the probiotics bacteria as QS degrader isolated from the fish gut could control the pathogenicity of Aeromonas hydrophila. It has been proposed that QS degrader bacteria might be an alternative solution for disease control.
Fen ve Teknoloji Eğitiminde Portfolyo ile Zenginleştirilmiş Öğrenme Ortamı ve Etkileri

Bu araştırmada fen ve teknoloji eğitiminde portfolyo ile zenginleştirilmiş öğrenme ortamının etkilerinin tespit edilmesi amaçlanmaktadır. Bunun için çalışma grubunun görüşleri ve araştırma sürecinde ortaya koydukları çalışmalar değerlendirilmiştir. Araştırmaya Gazi Üniversitesi Gazi Eğitim Fakültesi Fen Bilgisi Eğitiminde öğrenim görmekte olan 44 (33, Bayan ve 11, Erkek) öğretmen adayı katılmıştır. Belirlenen bazı kimya konularında uygulamalar sırasında fen ve teknoloji öğretmen adayları tarafından ürün dosyaları (portfolyo) hazırlanmıştır. Süreç içerisinde çalışma grubunun ürün dosyaları değerlendirilmiş ve geri dönütler verilmiştir. Çalışma grubu dönem sonunda ilgili konuları içeren ürün dosyalarını tamamlamışlardır. Uygulamaların sonunda 9 öğrenci ile görüşme yapılmıştır. Bunun için yarı yapılandırılmış iki sorudan oluşan bir görüşme protokolü hazırlanmıştır. Sonuç olarak süreçteki kazanımlar göz ününde bulundurulduğunda portfolyo ile zenginleştirilmiş öğrenme ortamının fen ve teknoloji eğitiminde bazı konularda etkili sonuçlar verdiği belirlenmiştir. Görüşme sonuçları da portfolyo ile zenginleştirilen öğrenme ortamının önemli bir süreç olduğunu ortaya koymaktadır. Nitekim öğretmen adayları bu süreçte çok şey kazandıklarını, konuları derinlemesine ve çok yönlü araştırmalarının kendilerine çok şey öğrettiğini vurgulamışlardır. Ayrıca süreçte öğrendiklerinin ise kalıcı olduğunu ifade etmişlerdir.
Similar Hierarchical Representation of Speech and Other Complex Sounds In the Brain and Deep Residual Networks: An MEG Study

Listeners recognize a vast number of complex sounds, but vocal sounds, speech and song, are essential for communication. Recently, deep neural networks (DNNs) have achieved human-level accuracy in sound classification, but do they illuminate similar properties with biological brains? In this study, we compared DNNs to primary and secondary auditory cortex to understand the hierarchy of sound representations in the brain. Ten subjects listened to speech and other naturalistic sounds while their magnetoencephalography (MEG) signals were recorded. Widely-used DNNs were trained to classify the same sounds. Brain activity localized to secondary auditory areas decoded speech significantly more accurately than other non-human sounds. Secondary auditory selectivity best matched later, and more complex layers of DNNs. Our results are compatible with special coding for speech in the brain and suggest comparable hierarchical principles of DNNs and neural processing of sounds.
Store-Operated Ca2+ Entry as a Putative Target of Flecainide for the Treatment of Arrhythmogenic Cardiomyopathy

Arrhythmogenic cardiomyopathy (ACM) is a genetic disorder that may lead patients to sudden cell death through the occurrence of ventricular arrhythmias. ACM is characterised by the progressive substitution of cardiomyocytes with fibrofatty scar tissue that predisposes the heart to life-threatening arrhythmic events. Cardiac mesenchymal stromal cells (C-MSCs) contribute to the ACM by differentiating into fibroblasts and adipocytes, thereby supporting aberrant remodelling of the cardiac structure. Flecainide is an Ic antiarrhythmic drug that can be administered in combination with β-adrenergic blockers to treat ACM due to its ability to target both Nav1.5 and type 2 ryanodine receptors (RyR2). However, a recent study showed that flecainide may also prevent fibro-adipogenic differentiation by inhibiting store-operated Ca2+ entry (SOCE) and thereby suppressing spontaneous Ca2+ oscillations in C-MSCs isolated from human ACM patients (ACM C-hMSCs). Herein, we briefly survey ACM pathogenesis and therapies and then recapitulate the main molecular mechanisms targeted by flecainide to mitigate arrhythmic events, including Nav1.5 and RyR2. Subsequently, we describe the role of spontaneous Ca2+ oscillations in determining MSC fate. Next, we discuss recent work showing that spontaneous Ca2+ oscillations in ACM C-hMSCs are accelerated to stimulate their fibro-adipogenic differentiation. Finally, we describe the evidence that flecainide suppresses spontaneous Ca2+ oscillations and fibro-adipogenic differentiation in ACM C-hMSCs by inhibiting constitutive SOCE.
What Do Butterflies Tell Us about an Intermediate Disturbance in a Dry Tropical Forest Context?

Montes de María is the best-preserved tropical dry forest fragment in the Colombian Caribbean, making it an ideal location for studying the impacts of human disturbance on local ecosystems. In this study, we analyzed the community structure of diurnal butterflies in both forested and disturbed areas using 16 circular plots to identify relationships between alpha and beta diversity, and the geographic distance between disturbed areas and native forests, using a range of metrics, including range–abundance and rarefaction curves, nonlinear models, and the Bray–Curtis dissimilarity index. The results revealed three distinct species assemblages associated with forests, intermediate disturbed areas (IDAs), and disturbed areas (DAs). Nonlinear models show that IDAs are more diverse than forests and DAs. However, forests have more beta diversity in plots than IDAs and DAs. Indicator species for each butterfly assemblage were also identified. Thus, although new butterfly species assemblages emerge from a new human landscape, it is clear that species that only occur within dry forest fragments are lost when forest fragments disappear. Overall, these findings have important implications for conservation efforts and understanding how human disturbance affects biodiversity in tropical ecosystems.
Perceptions of social media users to government regulations and measures during COVID-19 pandemic in Jordan: a qualitative study

ABSTRACT During the COVID-19 pandemic, social media played an important role in disseminating information and procedures for dealing with this pandemic, however, there is a lack of studies investigating the perceptions of social media users concerning the regulations and measures issued by authorities to encounter and cope with the COVID-19 pandemic, which was exceptional at that time. Therefore, this study purposed to explore the perceptions of social media users about the regulations and instructions of the authorities’ measures in times of the COVID-19 pandemic in Jordan. A qualitative method including thematic analysis and content analysis was adopted to evaluate social media posts, which involved the text and images found in these posts during the period from March 2020 to June 2020. Three social media websites were included in the study, namely Facebook, Snapchat, and Twitter. Five themes were extracted from 317 posts, including COVID-19 is a bubble economy, COVID-19 is a political game, health-related information about COVID-19 and safety precautions, COVID-19 regulations influenced personal restrictions and freedom confiscation, and COVID-19 is a financial burden. Thus, this study may provide recommendations for policy-makers to develop campaigns and activities based on people’s perspectives and design the appropriate health and economic promotions and interventions.
Predictors of mortality in critically ill patients with COVID-19 and diabetes

The COVID-19 pandemic has challenged the entire world, and patients with diabetes mellitus (DM) have been particularly affected. We aimed to evaluate predictors of mortality during the first 30 days of hospitalization in critically ill patients with COVID-19 and comorbid DM. This prospective study included 110 critically ill patients admitted with COVID-19 infection. Thirty-two (29%) patients had a previous diagnosis of DM. Clinical variables, laboratory tests, and vascular biomarkers, such as VCAM-1, syndecan-1, ICAM-1, angiopoietin-1, and angiopoeitin-2, were evaluated after intensive care unit (ICU) admission. A comparison was made between patients with and without DM. No difference in mortality was observed between the groups (48.7 vs 46.9%, P=0.861). In the multivariate Cox regression analysis, VCAM-1 levels at ICU admission (HR: 1 [1-1.001], P<0.006) were associated with death in patients with DM. Among patients with DM, advanced age (HR 1.063 [1.031-1.096], P<0.001), increased Ang-2/Ang-1 ratio (HR: 4.515 [1.803-11.308] P=0.001), and need for dialysis (HR: 3.489 [1.409-8.642], P=0.007) were independent predictors of death. Higher levels of VCAM-1 in patients with DM was better at predicting death of patients with severe COVID-19 and comorbid DM, and their cut-off values were useful for stratifying patients with a worse prognosis. Vascular biomarkers VCAM-1 and Ang-2/Ang-1 ratio were predictors of death in patients with severe COVID-19 and comorbid DM and those without DM. Additionally, kidney injury was associated with an increased risk of death.
Prevalence of impaired awareness of hypoglycaemia in adults with Type 1 diabetes

Aims Impaired awareness of hypoglycaemia (IAH) is thought to affect approximately 25% of people with Type 1 diabetes. While this estimate was based on retrospective information from patients in several small studies performed several years ago, validated methods of assessment have not been used in a large hospital clinic‐based population to ascertain the prevalence in the present era.
Spatial and Spatio-temporal Bayesian Models with R - INLA

with R-INLA Marta Blangiardo and Michela Cameletti 2015, 308pp. Wiley–Blackwell http://www.wiley. com/wiley-blackwell Bayesian statistical approaches have revolutionised our ability to extract parameters from complex spatial and temporal datasets. Nevertheless, conventional MCMC methods become computationally intractable for very large datasets, which is where Integrated Nested Laplace Approximation (INLA) offers particular efficiency advantages. The R-INLA package makes these techniques available to anyone with basic competency in R. No statistical test should be applied without a full awareness of its procedures and assumptions, on which this book provides a thorough grounding. The mathematical content is advanced, but for those willing to put in the effort it will open up new ways of approaching big data.