Miracle-dz/UniFAR-FTUs huggingface dataset:
Age‐ and Genotype‐Dependent Variability in the Protein Abundance and Activity of Six Major Uridine Diphosphate‐Glucuronosyltransferases in Human Liver

The ontogeny of hepatic uridine diphosphate‐glucuronosyltransferases (UGTs) was investigated by determining their protein abundance in human liver microsomes isolated from 136 pediatric (0–18 years) and 35 adult (age >18 years) donors using liquid chromatography / tandem mass spectrometry (LC‐MS/MS) proteomics. Microsomal protein abundances of UGT1A1, UGT1A4, UGT1A6, UGT1A9, UGT2B7, and UGT2B15 increased by ∼8, 55, 35, 33, 8, and 3‐fold from neonates to adults, respectively. The estimated age at which 50% of the adult protein abundance is observed for these UGT isoforms was between 2.6–10.3 years. Measured in vitro activity was generally consistent with the protein data. UGT1A1 protein abundance was associated with multiple single nucleotide polymorphisms exhibiting noticeable ontogeny–genotype interplay. UGT2B15 rs1902023 (*2) was associated with decreased protein activity without any change in protein abundance. Taken together, these data are invaluable to facilitate the prediction of drug disposition in children using physiologically based pharmacokinetic modeling as demonstrated here for zidovudine and morphine.
Loading differences in single-leg landing in the forehand- and backhand-side courts after an overhead stroke in badminton: A novel tri-axial accelerometer research

ABSTRACT Anterior cruciate ligament (ACL) injuries in badminton commonly occur during single-leg landing after an overhead stroke in the backhand-side court. This study compared differences in trunk acceleration and kinematic variables during single-leg landing in the forehand- and backhand-side courts after an overhead stroke. Eighteen female junior badminton players performed two singles games while wearing a tri-axial accelerometer. The moment that over 4g of resultant acceleration was generated was determined using synchronised video cameras. Trunk lateral inclination and hip abduction angles at the point of landing with over 4g of resultant acceleration were analysed. Mediolateral acceleration in the backhand-side court was greater than that in the opposite-side court (p < 0.001, ES = 0.840). Both trunk lateral angles were larger than those previously reported in injured participants and the hip abduction angle in the backhand-side court was larger than that in the forehand-side court (p < 0.001, ES = 2.357). The lateral and vertical acceleration in the backhand-side court showed moderate-to-strong correlations with the trunk and hip angles. The mediolateral physical demand and high-risk posture in the backhand-side court may be associated with a higher incidence of knee injuries during badminton games.
Lack of Resilience Is Related to Stress-Related Sleep Reactivity, Hyperarousal, and Emotion Dysregulation in Insomnia Disorder.

STUDY OBJECTIVES According to the diathesis-stress model of insomnia, insomnia may develop in vulnerable individuals in response to stress. Resilience is a psychobiological factor that determines an individual's capacity to adapt successfully to stressful events and low resilience increases vulnerability for development of mental disorders. The aim was to explore resilience in subjects with insomnia and its relationship with the factors that contribute to its development and perpetuation. METHODS The study consisted of 58 subjects with Insomnia Disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and 38 good sleepers. Resilience Scale for Adults (RSA), Ford Insomnia Response to Stress Test (FIRST), Pre-sleep Arousal Scale (PSAS), and Difficulties in Emotion Regulation Scale (DERS) were administered while taking into account psychiatric symptoms. Differences in means between groups were assessed using t test or Mann-Whitney U/Wilcoxon test. Linear/multivariable regression analyses and mediation analyses were performed. RESULTS Subjects with insomnia (24 females, mean age 49 ± 2.1 years) had lower RSA and higher FIRST, DERS, and PSAS scores than good sleepers (22 females, mean age 47.2 ± 1.2 years). After controlling for anxiety/depressive symptoms, low resilience correlated with high stress-related sleep reactivity (P = .004), pre-sleep cognitive hyperarousal (P = .01) and emotion dysregulation (P = .01). Emotion dysregulation mediated the relationship between low resilience and cognitive hyperarousal (Z = 2.06, P = .03). CONCLUSIONS Subjects with insomnia showed low resilience, which was related to high stress-related sleep reactivity, emotional dysregulation, and hyperarousal. If resilience helps to minimize the extent of pathogenesis in the developmental process, an early identification of vulnerable candidates should be useful for preventing insomnia development and maintenance. COMMENTARY A commentary on this article appears in this issue on page 709.
Is current risk assessment of non-genotoxic carcinogens protective?

Abstract Non-genotoxic carcinogens (NGTXCs) do not cause direct DNA damage but induce cancer via other mechanisms. In risk assessment of chemicals and pharmaceuticals, carcinogenic risks are determined using carcinogenicity studies in rodents. With the aim to reduce animal testing, REACH legislation states that carcinogenicity studies are only allowed when specific concerns are present; risk assessment of compounds that are potentially carcinogenic by a non-genotoxic mode of action is usually based on subchronic toxicity studies. Health-based guidance values (HBGVs) of NGTXCs may therefore be based on data from carcinogenicity or subchronic toxicity studies depending on the legal framework that applies. HBGVs are usually derived from No-Observed-Adverse-Effect-Levels (NOAELs). Here, we investigate whether current risk assessment of NGTXCs based on NOAELs is protective against cancer. To answer this question, we estimated Benchmark doses (BMDs) for carcinogenicity data of 44 known NGTXCs. These BMDs were compared to the NOAELs derived from the same carcinogenicity studies, as well as to the NOAELs derived from the associated subchronic studies. The results lead to two main conclusions. First, a NOAEL derived from a subchronic study is similar to a NOAEL based on cancer effects from a carcinogenicity study, supporting the current practice in REACH. Second, both the subchronic and cancer NOAELs are, on average, associated with a cancer risk of around 1% in rodents. This implies that for those chemicals that are potentially carcinogenic in humans, current risk assessment of NGTXCs may not be completely protective against cancer. Our results call for a broader discussion within the scientific community, followed by discussions among risk assessors, policy makers, and other stakeholders as to whether or not the potential cancer risk levels that appear to be associated with currently derived HBGVs of NGXTCs are acceptable.
Estimation of Student Performance by Considering Consecutive Lessons

Examining student learning behavior is one of the crucial educational issues. In this paper, we propose a new method to predict student performance by using comment data mining. A teacher just asks students after every lesson to freely describe and write about their learning situations, attitudes, tendencies, and behaviors. The method employs Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM) to predict student grades in each lesson. In order to obtain further improvement of prediction results, we apply a majority vote method to the predicted results obtained in consecutive lessons to keep track of each student's learning situation. Also, we evaluate the reliability of the predicted student grades to know when we can rely prediction results of student grade during the period of the semester. The experiment results show that our proposed method continuously tracked student learning situation and improved prediction performance of final student grades compared to Probabilistic Latent Semantic Analysis (PLSA) and Latent Semantic Analysis (LSA) models. Also, considering the differences of prediction results in the two consecutive lessons helps to evaluate the reliability of the predicted results.
The MEK1/2 Inhibitor AZD6244 Sensitizes BRAF-Mutant Thyroid Cancer to Vemurafenib

Background BRAFV600E mutation occurs in approximately 45% of papillary thyroid cancer (PTC) cases, and 25% of anaplastic thyroid cancer (ATC) cases. Vemurafenib/PLX4032, a selective BRAF inhibitor, suppresses extracellular signal-regulated kinase kinase/extracellular signal-regulated kinase 1/2 (MEK/ERK1/2) signaling and shows beneficial effects in patients with metastatic melanoma harboring the BRAFV600E mutation. However, the response to vemurafenib is limited in BRAF-mutant thyroid cancer. The present study evaluated the effect of vemurafenib in combination with the selective MEK1/2 inhibitor AZD6244 on cell survival and explored the mechanism underlying the combined effect of vemurafenib and AZD6244 on thyroid cancer cells harboring BRAFV600E. Material/Methods Thyroid cancer 8505C and BCPAP cells harboring the BRAFV600E mutation were exposed to vemurafenib (0.01, 0.1, and 1 μM) and AZD6244 (0.01, 0.1, and 1 μM) alone or in the indicated combinations for the indicated times. Cell viability was detected by the MTT assay. Cell cycle distribution and induction of apoptosis were detected by flow cytometry. The expression of cyclin D1, P27, (P)-ERK1/2 was evaluated by Western blotting. The effect of vemurafenib or AZD6244 or their combination on the growth of 8505C cells was examined in orthotopic xenograft mouse models in vivo. Results Vemurafenib alone did not increase cell apoptosis, whereas it decreased cell viability by promoting cell cycle arrest in BCPAP and 8505C cells. AZD6244 alone increased cell apoptosis by inducing cell cycle arrest in BCPAP and 8505C cells. Combination treatment with AZD6244 and vemurafenib significantly decreased cell viability and increased apoptosis in both BCPAP and 8505C cells compared with the effects of each drug alone. AZD6244 alone abolished phospho-ERK1/2 (pERK1/2) expression at 48 h, whereas vemurafenib alone downregulated pERK1/2 at 4–6 h, with rapid recovery of expression, reaching the highest level at 24–48 h. Combined treatment for 48 h completely inhibited pERK1/2 expression. Combination treatment with vemurafenib and AZD6244 inhibited cell growth and induced apoptosis by causing cell-cycle arrest, with the corresponding changes in the expression of the cell cycle regulators p27Kip1 and cyclin D1. Co-administration of vemurafenib and AZD6244 in vivo had a significant synergistic antitumor effect in a nude mouse model. Conclusions Vemurafenib activated pERK1/2 and induced vemurafenib resistance in thyroid cancer cells. Combination treatment with vemurafenib and AZD6244 inhibited ERK signaling and caused cell cycle arrest, resulting in cell growth inhibition. Combination treatment in patients with thyroid cancer harboring the BRAFV600E mutation may overcome vemurafenib resistance and enhance the therapeutic effect.
Genetic Diversity and Distribution of Blastocystis Subtype 3 in Human Populations, with Special Reference to a Rural Population in Central Mexico

Blastocystis subtype 3 (ST3) is a parasitic protist found in the digestive tract of symptomatic and asymptomatic humans around the world. While this parasite exhibits a high prevalence in the human population, its true geographic distribution and global genetic diversity are still unknown. This gap in knowledge limits the understanding of the spread mechanisms, epidemiology, and impact that this parasite has on human populations. Herein, we provided new data on the geographical distribution and genetic diversity of Blastocystis ST3 from a rural human population in Mexico. To do so, we collected and targeted the SSU-rDNA region in fecal samples from this population and further compared its genetic diversity and structure with that previously observed in populations of Blastocystis ST3 from other regions of the planet. Our analyses reveled that diversity of Blastocystis ST3 showed a high haplotype diversity and genetic structure to the world level; however, they were low in the Morelos population. The haplotype network revealed a common widespread haplotype from which the others were generated recently. Finally, our results suggested a recent expansion of the diversity of Blastocystis ST3 worldwide.
Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling

The capability of processing and digesting raw data is one of the key features of a human-like artificial intelligence system. For instance, real-time machine translation should be able to process and understand spoken natural language, and autonomous driving relies on the comprehension of visual inputs. Representation learning is a class of machine learning techniques that autonomously learn to derive latent features from raw data. These new features are expected to represent the data instances in a vector space that facilitates the machine learning task. This thesis studies two specific data situations that require efficient representation learning: knowledge graph data and high dimensional sequences. In the first part of this thesis, we first review multiple relational learning models based on tensor decomposition for knowledge graphs. We point out that relational learning is in fact a means of learning representations through one-hot mapping of entities. Furthermore, we generalize this mapping function to consume a feature vector that encodes all known facts about each entity. It enables the relational model to derive the latent representation instantly for a new entity, without having to re-train the tensor decomposition. In the second part, we focus on learning representations from high dimensional sequential data. Sequential data often pose the challenge that they are of variable lengths. Electronic health records, for instance, could consist of clinical event data that have been collected at subsequent time steps. But each patient may have a medical history of variable length. We apply recurrent neural networks to produce fixed-size latent representations from the raw feature sequences of various lengths. By exposing a prediction model to these learned representations instead of the raw features, we can predict the therapy prescriptions more accurately as a means of clinical decision support. We further propose Tensor-Train recurrent neural networks. We give a detailed introduction to the technique of tensorizing and decomposing large weight matrices into a few smaller tensors. We demonstrate the specific algorithms to perform the forward-pass and the back-propagation in this setting. Then we apply this approach to the input-to-hidden weight matrix in recurrent neural networks. This novel architecture can process extremely high dimensional sequential features such as video data. The model also provides a promising solution to processing sequential features with high sparsity. This is, for instance, the case with electronic health records, since they are often of categorical nature and have to be binary-coded. We incorporate a statistical survival model with this representation learning model, which shows superior prediction quality.
Identification of Specific Components of the Eicosanoid Biosynthetic and Signaling Pathway Involved in Pathological Inflammation during Intra-abdominal Infection with Candida albicans and Staphylococcus aureus

ABSTRACT Polymicrobial intra-abdominal infections (IAIs) are a significant cause of morbidity and mortality, particularly when fungal pathogens are involved. Our experimental murine model of IAI involving intraperitoneal inoculation of Candida albicans and Staphylococcus aureus results in synergistic lethality (∼80%) due to exacerbated inflammation. Monomicrobial infection results in no mortality, despite a microbial burden and dissemination similar to those in a coinfection. In the coinfection model, the immunomodulatory eicosanoid prostaglandin E2 (PGE2) was determined to be necessary and sufficient to induce mortality, implicating PGE2 as the central mediator of the amplified inflammatory response. The aim of this study was to identify key components of the PGE2 biosynthetic and signaling pathway involved in the inflammatory response and explore whether these can be targeted to prevent or reduce mortality. Using selective pharmacological inhibitors of cyclooxygenases (COX) or PGE2 receptor antagonists in the C. albicans-S. aureus IAI mouse model, we found that inhibition of COX and/or blocking of PGE2 receptor 1 (EP1) or PGE2 receptor 3 (EP3) signaling reduced proinflammatory cytokine production, promoted interleukin-10 production, reduced cellular damage in the peritoneal cavity, and, most importantly, significantly improved survival. The greatest effect on survival was obtained by the simultaneous inhibition of COX-1 activity and EP1 and EP3 receptor signaling. Importantly, early inhibition of PGE2 pathways dramatically improved the survival of fluconazole-treated mice compared with that achieved with fluconazole treatment alone. These findings indicate that COX-1 and the EP1 and EP3 receptors mediate the downstream pathological effects of PGE2 during polymicrobial IAI and may serve as effective therapeutic targets.
FASK with Interventional Knowledge Recovers Edges from the Sachs Model

We report a procedure that, in one step from continuous data with minimal preparation, recovers the graph found by Sachs et al. \cite{sachs2005causal}, with only a few edges different. The algorithm, Fast Adjacency Skewness (FASK), relies on a mixture of linear reasoning and reasoning from the skewness of variables; the Sachs data is a good candidate for this procedure since the skewness of the variables is quite pronounced. We review the ground truth model from Sachs et al. as well as some of the fluctuations seen in the protein abundances in the system, give the Sachs model and the FASK model, and perform a detailed comparison. Some variation in hyper-parameters is explored, though the main result uses values at or near the defaults learned from work modeling fMRI data.
Policing immigrants: Fear of deportations and perceptions of law enforcement and criminal justice

Summary This study examined the relationship between the fear of deportation and perceptions of law enforcement, the criminal justice system, and the willingness to report crimes among Latinos in the US. Understanding the relationship between increased immigration enforcement and fear of deportation may promote public safety by improving the relationship between the police and Latino communities. Findings Multivariate ordinal logistic regression analyses of the data found that participants who had a greater fear of deportation reported: (1) less confidence that police would not use excessive force (p < .01); (2) less confidence that police would treat Latinos fairly (p < .05); (3) a lower likelihood of reporting crimes (p < .05); and (4) less confidence that the courts would treat Latinos fairly (p < .01). Applications Social work practitioners can help address this issue and improve the relationship between law enforcement, the criminal justice system, and the Latino community by: delivering cultural competence training for police and criminal justice personnel; educating Latinos about how to protect their rights when interacting with police and the criminal justice system; and by advocating for immigration reform that can help improve public safety and reduce fear in Latino communities.
LBP Channels for Pedestrian Detection

This paper introduces a new channel descriptor for pedestrian detection. This type of descriptor usually selects a set of one-valued filters within the enormous set of all possible filters for improved efficiency. The main claim underpinning this paper is that the recent works on channel-based features restrict the filter space search, therefore bringing along the obsolescence of one-valued filter representation. To prove our claim, we introduce a 12-valued filter representation based on local binary patterns. Indeed, various improvements now allow for this texture feature to provide a very discriminative, yet compact descriptor. Filter selection boasting new combination restrictions as well as a reverse selection process are also presented to choose the best filters. experiments on the INRIA and Caltech-USA datasets validate the approach.
THE EFFECTIVENESS OF EARLY CLINICAL EXPOSURE AND E-LEARNING IN TEACHING ANATOMY

Address for Correspondence: Dr. Mamata Chimmalgi, Professor, Department of Anatomy, Sree Gokulam Medical College & Research Foundation, Venjaramoodu, Trivandrum – 695607 Telephone: 91-9387122769 / 9763887130 E-Mail: mamatachimmalgi@gmail.com Objectives: The primary objective of this study was to determine if Early Clinical Exposure (ECE) and E-learning can be effective alternative methods of teaching anatomy for the first-year medical students. Secondary objectives were to determine (a) if there was gender bias with these methods and (b) if these methods influenced high, average or low achievers selectively. Materials and Methods: Study was conducted in the department of anatomy with 150 first-year medical students participating in it. Students were randomly divided into three groups of 50 each. Six selected topics were taught to the three groups using ECE, e-learning and traditional lecture alternatively. After each session, a test was conducted using 20 objective questions to assess the learning outcome. Average score of the groups were analysed using Fisher one-way ANOVA or Welch test. Multiple comparisons were done using post-hoc tests when the difference between the average scores was found to be significant. Gender difference in the scores for each method was noted. Learning outcome of high, average and low achievers with ECE and e-learning were compared. Results: Average total scores after ECE were comparable to lecture and superior to e-learning in five of the six sessions. E-learning, however, was not as effective as ECE in improving the test scores. Except for one group, where the boys outperformed the girls after e-learning, girls outperformed the boys in all the groups with all the three methods of instruction. ECE or e-learning did not influence high, average or low achievers selectively. Conclusion: Based on the learning outcome, ECE can be an effective method to teach Anatomy in the described format. E-learning did not improve the test scores in the self-study format used in this study, but, 3D visualization can benefit the students when used as an adjunct to the traditional methods.
Thermal transport in Si/Ge nanocomposites

In this paper, a systematic study is carried out to investigate the thermal transport in Si/Ge nanocomposites by using molecular dynamics simulation. Emphasis is placed on the effect of nanowire size, heat flux, Si/Ge interface, atomic ratio and defects (voids). The results show that the thermal conductivity of nanowire composites is much lower than that of alloy, which accounts mainly for ZT enhancement and owes a great deal to the effect of interface thermal resistance. A ‘reflecting effect’ in temperature distribution is observed at the Si/Ge interface, which is largely due to the lack of right quantum temperature correction in the region adjacent to the interface. The thermal conductivity of the nanocomposite is found to have weak dependence on the bulk temperature (200–900 K) and the heat flux in the range (0.5–3.5) × 1010 W m−2. Simulation results reveal that for a constant Si wire dimension, the thermal conductivity of the Si1−xGex nanocomposites increases with x. Our study on the influence of the defects (voids) has the same order of relative thermal conductivity reduction with increasing void density in comparison with the experimental data. Due to the small size (10 nm) of Si nanowires in our nanocomposites, the voids show less effect on thermal conductivity reduction in comparison with the experimental data with 100 nm Si wires.