Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Branched Chain Amino Acid Supplement on Kidney Tissue of Exercising Rats

One of the supplements used in with exercise programs is branched chain amino acids (BCAAs) which preferred because of their effect on novation of muscle protein synthesis. However, BCAAs increase their amount in the blood in a short time due to their properties. In this case, the result can increase the workload of the kidneys. Based on the information, this study investigated the effects of resistance exercise and BCAA supplements on kidney tissue. A total of 24 Wistar Albino male rats were divided equally into 4 groups: Control, BCAA, Exercise and Exercise+BCAA. In six-week study, resistance swimming exercise was applied to exercise groups. BCAA groups were given BCAA supplements at doses of 2.5 mg/kg before exercise. End of the study, histological, immunochemical, and RT-PCR analyses were performed. As a result of the findings obtained, it was found that use of BCAA supplements together with exercise caused tubular necrosis (p=0.002). A significant increase was found in caspase 3 IHC staining findings in BCAA and Exercise+BCAA groups compared to Control group (p=0.011; p=0.02). Also, KIM-1 expression levels were higher in Exercise group compared to all other groups (p=0.004; p=0.003; p=0.008). As a result, BCAA consumption in combination with resistant exercise caused damage to kidney tissue.
Adaptation of Kirkpatrick’s Four-Level Model of Training Criteria to Evaluate Training Programmes for Head Teachers

Training programmes are evaluated to verify their effectiveness, assess their ability to achieve their goals and identify the areas that require improvement. Therefore, the target of evaluators is to develop an appropriate framework for evaluating training programmes. This study adapted Kirkpatrick’s four-level model of training criteria published in 1959 to evaluate training programmes for head teachers according to their own perceptions and those of their supervisors. The adapted model may help evaluators to conceptualise the assessment of learning outcomes of training programmes with metrics and instruments. The model also helps to determine the strengths and weaknesses of the training process. The adaptation includes concrete metrics and instruments for each of the four levels in the model: reaction criteria, learning criteria, behaviour criteria and results criteria. The adapted model was applied to evaluate 12 training programmes for female head teachers in Saudi Arabia. The study sample comprised 250 trainee head teachers and 12 supervisors. The results indicated that the adapted Kirkpatrick evaluation model was very effective in evaluating educational training for head teachers.
Aerosol Climatology Over South and Southeast Asia: Aerosol Types, Vertical Profile, and Source Fields

Aerosol climatology during typical haze dominating period over South and Southeast Asia was explored using several Earth‐Observing A‐Train satellite products retrieved in between 2010 and 2020. Comparatively high aerosol optical depth (AOD) with dominance of fine and UV‐absorbing aerosols are noted across the Indo‐Gangetic plain, South Asia (IGP; AOD: 0.58; UVAI: 0.74) against weak UV‐absorbing fine aerosols over Southeast Asia (SEA; AOD: 0.26; UVAI: 0.07). Among inland IGP sites, decadal mean AOD resembles in Lahore (0.72 ± 0.45), Delhi (0.81 ± 0.46), Kanpur (0.84 ± 0.42), and Varanasi (0.78 ± 0.45); all exhibiting bimodal AOD distribution with a first peak in early November followed by a second in early January. In contrast, except mainland site Chiang Mai, all SEA maritime cities resemble in having typical September‐October AOD peak, with the presence of fine and UV‐neutral aerosols. Urban hotspots across IGP and SEA (except Dhaka, Chiang Mai) denote a spatially consistent minor increasing trend in AOD (0.2–1.8 × 10−2 year−1) while increase in UVAI is more prominent over upper IGP. Dust aerosols dominate only in Karachi (46%) against strong UV‐absorbing smoke aerosols over rest of the IGP (71–91%), and UV‐neutral smoke aerosols across SEA (84–92%). Vertical stratification of aerosol types is noted across IGP as in lower atmosphere (<4 km) polluted dust/urban aerosols remain abundant, with gradual decrease in dust aerosols from upper to lower IGP and consequent increase in smoke aerosols. At upper atmosphere (>4 km), however, dust aerosols clearly dominate. Over SEA, smoke are the most abundant aerosols across the atmospheric column followed by polluted dust. No evidence of intracontinental transport of aerosols from IGP to Southeast Asia or vice versa is, however, noted.
Carcass and organ characteristics of finishing broilers fed diets containing probiotics (Saccharomyces cerevisiae)

A five-week study was conducted to determine the effect of feeding varying levels of S. cerevisiae on carcass and organ characteristics of finishing broilers. One hundred and twenty 4-weeks old broilers of cobb strain were randomly assigned to four treatments (T1 = 0.6 g Saccharomyces cerevisiae (SC) kg –1 diet; T2 = 0.8 g SC kg –1 diet; T3 = 1.0 g SC kg –1 diet and T4 = 0.0 g SC kg –1 diet) with 30 birds per treatment and replicated twice with 15 birds per replicate in a completely randomized design. Feed and water were provided ad libitum to the birds in a deep litter system. In the end, data on growth, carcass and organ indices generated from the study were analyzed using one-way analysis of variance (ANOVA). Results showed no significant difference (p > 0.05) among the treatments in the birds’ growth performance indices. However, significant differences (p < 0.05) were observed in the birds’ values for liver weight, heart weight, shank length and thigh length with birds in T3 recording highest values of 61.30 g, 16.93 g and 12.00 cm for liver weight, heart weight and thigh length, respectively. It was thus concluded that finishing broilers fed 1.0 g of S. cerevisiae had superior carcass and organ characteristics than birds on the control and lower levels of inclusion.
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving

This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.
How does selective exposure affect partisan polarisation? Media consumption on electoral campaigns

ABSTRACT Internationally-recognized studies have identified the effects of selective exposure to the media and its implications for the proper functioning of democratic systems. The theory of cognitive dissonance, by which citizens decide to expose to like-minded contents and reject inconsistent ones with their ideology or values, allows us in this article to test their possible effects on voting and increasingly partisan polarisation in west European political systems. To carry out it, through a sample of 5943 citizens (from the post-electoral survey of the 2019 General Elections of the Spanish national centre for sociological analysis), we have analysed how exposure to media affects vote choice, party affiliation and reject other parties. Through statistical analysis models, results demonstrate media consumption effects on partisan polarisation in electoral campaigns, especially in populist parties. Accordingly, this article provides empirical evidence that ideological affiliation to this kind of parties is highly influenced by a restrictive media diet (exposure to a very limited number of media, even a just one) with high-polarised content.
Response of Apricot Fruit Quality to Protective Netting

The cultivation of fruit trees in protected environments is a technique that has been developed in recent years for peaches and nectarines, but not for apricots. This study was conducted to investigate the chemical composition of the fruits and their quality indices variations of the variety ‘Mikado’ as a function of its cultivation under a protective net or outdoors. As a practical agronomic assay, a homogeneous experimental plot was used in this study, where half of the apricot trees were cultivated under protective netting, and the other half without it. The data showed that for the total yield, no statistically significant differences were found with respect to ‘Mikado’ trees cultivated with or without a net. The trees cultivated under protective nets showed higher fruit weights. The results showed that the technique of using protective nets for the cultivation of extra-early apricot ‘Mikado’ is a profitable alternative for farmers, and their use does not affect fruit production or quality.
Whole-milk consumption decreases the risk of inflammatory bowel disease: a two-sample Mendelian randomization analysis

Supplemental Digital Content is available in the text Abstract Objective: The impact of dairy fat on inflammatory bowel disease remained inconclusive. We aimed to compare the effects of whole-milk and skimmed-milk consumption on the risk of inflammatory bowel disease using a Mendelian randomization analysis. Methods: We conducted a genome-wide association study of the preference for whole versus skimmed milk using data for 20,200 whole-milk consumers and 67,847 skimmed-milk consumers from the UK Biobank. The lead single nucleotide polymorphisms in the associated loci were identified at the genome-wide significance level, and were further employed as instrumental variables for whole-milk preference. We conducted a two-sample Mendelian randomization analysis with whole-milk preference as the exposure and inflammatory bowel disease as the outcome. The pleiotropic effects and heterogeneity of the instrumental variables were estimated using Mendelian randomization-Egger regression and Cochran Q test, respectively. This study was conducted using the UKB resources under the application “53536”. The UK Biobank was approved by the North West Multi-center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. Results: The genome-wide association study identified five lead nucleotide polymorphisms associated with whole-milk preference. Mendelian randomization indicated that whole-milk preference significantly decreased the risk of inflammatory bowel disease (β=−1.735, P = 0.048). Of the two subtypes, whole-milk preference was associated with a lower risk of Crohn disease (β=−2.549, P = 0.032), but had no significant effect on the risk of ulcerative colitis (β=−1.002, P = 0.44). Conclusion: Consumption of whole-milk fat may protect against Crohn disease, compared with skimmed milk. This conclusion was based on causal inference in a cohort study, and further validation in randomized controlled trials is warranted.
Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting

: The motivation behind this paper is to investigate the use of Softmax model for classification. We show that Softmax model is a nonlinear generalization for the logistic discrimination, that can approximate the posterior probabilities of classes where other Artificial neural network (ANN) models don't have this ability. We show that Softmax model has more flexibility than logistic discrimination in terms of correct classification. To show the performance of Softmax model a medical data set on thyroid gland state is used. The result is that Softmax model may suffer from overfitting.
Plankton biogeography in the North Atlantic Ocean and its adjacent seas: Species assemblages and environmental signatures

Abstract Plankton biodiversity is a key component of marine pelagic ecosystems. They are at the base of the food web, control the productivity of marine ecosystems, and provide many provisioning and regulating ecological services. It is therefore important to understand how plankton are organized in both space and time. Here, we use data of varying taxonomic resolution, collected by the Continuous Plankton Recorder (CPR) survey, to map phytoplankton and zooplankton biodiversity in the North Atlantic and its adjacent seas. We then decompose biodiversity into 24 species assemblages and investigate their spatial distribution using ecological units and ecoregions recently proposed. Finally, we propose a descriptive method, which we call the environmental chromatogram, to characterize the environmental signature of each plankton assemblage. The method is based on a graphic that identifies where species of an assemblage aggregate along an environmental gradient composed of multiple ecological dimensions. The decomposition of the biodiversity into species assemblages allows us to show (a) that most marine regions of the North Atlantic are composed of coenoclines (i.e., gradients of biocoenoses or communities) and (b) that the overlapping spatial distribution of assemblages is the result of their environmental signatures. It follows that neither the ecoregions nor the ecological units identified in the North Atlantic are characterized by a unique assemblage but instead by a mosaic of assemblages that overlap in many places.
A Generalized Framework of Optimal Two-Stage Designs for Exploratory Basket Trials

Abstract The primary goal of an exploratory oncology clinical trial is to identify an effective drug for further development. To expedite the drug development process and increase the chance of finding active tumor indications where the treatment works, multiple tumor cohorts are often investigated simultaneously in a basket trial. In this article, we propose a generalized framework of an optimal basket trial design in the exploratory setting where tumor indications can be homogeneous (objective response rates of all indications are the same under both the null and alternative hypotheses) or heterogeneous (objective response rates are not the same under at least one of the null or alternative hypotheses). The proposed design prunes the inactive tumor indication in Stage 1 and pools the remaining tumor indications at the end of Stage 2 to evaluate the overall effectiveness of whether the treatment works in at least one tumor indication. The design parameters are optimized to minimize the expected sample size while explicitly controlling the global Type I and Type II error rates. In addition, we consider reallocating the planned Stage 2 sample size of pruned indications to achieve a higher power when the total planned sample size is fixed. Simulation studies are conducted to show the favorable operating characteristics of the proposed design under certain scenarios.
On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.
The COVID‐19 Pandemic: An Evolving Story. Professional and Personal Insights using Self and Culture as Agents of Calm and Healing after a Year of Co‐habitation with Imminent Threat

Being a cross‐cultural systemic therapist, clinical supervisor, and educator means that culture and language are central to my work. They provide a scaffold to develop deeper understanding, increased trust, and connection between myself and my supervisees, students, and clients and facilitate a process for the latter to connect to their own selves and values. Given the fear and uncertainty generated by the COVID‐19 pandemic, there exists a pervasive activation of the sympathetic nervous system in the community. In this article, I present two case studies as examples of a cross‐cultural/cross‐linguistic approach that facilitates two clients to find a place of comfort and calmness and consequently a balancing activation of the para‐sympathetic nervous system. First is a client who, due to the overwhelming pandemic chaos, suddenly exhibited a host of signs and symptoms of a functional neurological nature, which she experienced as a lack of control and disconnection from her body, her primary language, and herself. Within a trusted therapeutic relationship that draws on the culture and primary language of the therapist, the client regains connection with language and enhances her ability to communicate and connect with her body. Second is an international student who is encouraged to use her culture of origin and primary language to induce calmness, reconnect with herself, and return to the familiar as a ‘known’ collectively inclusive, comforting, and nurturing environment.
It takes two to tango: Statistical modeling and machine learning

ABSTRACT Statistical methods (SM) have been dominant in generating insights from any type of data for generations. However, with the recent advances in technology, machine learning (ML) has become one of the widely spoken methods to generate insights with more ease of use. While the followers of statistical methods have a differing view point about ML, and the followers of ML have a differing viewpoint about SM, this article isolates the merits of each of these two methods and advances arguments for when to use what method based on the purpose, context, frequency of use, cost, expertise and time. To be specific, the main purpose of SM is for inference and that of ML is prediction. Further, this article goes one step further and creates a scenario where it shows that when we combine the learning from using a statistical method and apply it to machine learning, the ultimate benefit can be greater than the sum of each method’s benefits.
Study of impact of COVID-19 infection on ectopic pregnancy in a tertiary care center

Background: COVID-19 is an infectious disease caused by a recently discovered coronavirus (SARS-CoV-2). Most of the people infected with the coronavirus will experience mild to moderate respiratory illness and recover without requiring any special treatment. In current study we have studied impact COVID-19 pandemic on clinical outcome and management of ectopic pregnancy.Methods: Women with confirmed or suspected ectopic pregnancy admitted in emergency for further management and tested via nasopharyngeal (NP) or oropharyngeal swab for SARS-CoV-2 using reverse transcriptase polymerase chain reaction (RT-PCR) from April 1, 2020 to November 30, 2020 were included in the study.Results: In our study out of 32 case of ectopic pregnancy, 3 patients were COVID-19 positive. Total non COVID-19 patients were 29. Out of 32 ectopic pregnancies only 4 were unruptured ectopic pregnancy and rest 28 had ruptured ectopic pregnancy. Mortality among ectopic pregnancy was noted only in 1 case (3.12%). Conclusions: Timely diagnosis and initiating management at first point of care can reduce the morbidity and mortality due to ectopic pregnancy. Patient’s knowledge attitude and awareness will be achieved through health education. Women with clinical signs and physical symptoms of a ruptured ectopic pregnancy, such as hemodynamic instability or an acute abdomen, should be evaluated and treated urgently. In pandemic situation where some part of hospital facilities devoted to COVID-19 patients, so planned distribution of resources to tackle medical emergency will bear fruitful positive outcome.
Serial Monitoring of Circulating Tumor DNA by Next-Generation Gene Sequencing as a Biomarker of Response and Survival in Patients With Advanced NSCLC Receiving Pembrolizumab-Based Therapy

PURPOSE Although the majority of patients with metastatic non–small-cell lung cancer (mNSCLC) lacking a detectable targetable mutation will receive pembrolizumab-based therapy in the frontline setting, predicting which patients will experience a durable clinical benefit (DCB) remains challenging. MATERIALS AND METHODS Patients with mNSCLC receiving pembrolizumab monotherapy or in combination with chemotherapy underwent a 74-gene next-generation sequencing panel on blood samples obtained at baseline and at 9 weeks. The change in circulating tumor DNA levels on-therapy (molecular response) was quantified using a ratio calculation with response defined by a > 50% decrease in mean variant allele fraction. Patient response was assessed using RECIST 1.1; DCB was defined as complete or partial response or stable disease that lasted > 6 months. Progression-free survival and overall survival were recorded. RESULTS Among 67 patients, 51 (76.1%) had > 1 variant detected at a variant allele fraction > 0.3% and thus were eligible for calculation of molecular response from paired baseline and 9-week samples. Molecular response values were significantly lower in patients with an objective radiologic response (log mean 1.25% v 27.7%, P < .001). Patients achieving a DCB had significantly lower molecular response values compared to patients with no durable benefit (log mean 3.5% v 49.4%, P < .001). Molecular responders had significantly longer progression-free survival (hazard ratio, 0.25; 95% CI, 0.13 to 0.50) and overall survival (hazard ratio, 0.27; 95% CI, 0.12 to 0.64) compared with molecular nonresponders. CONCLUSION Molecular response assessment using circulating tumor DNA may serve as a noninvasive, on-therapy predictor of response to pembrolizumab-based therapy in addition to standard of care imaging in mNSCLC. This strategy requires validation in independent prospective studies.