Miracle-dz/UniFAR-FTUs huggingface dataset:
Leptin stimulates autophagy/lysosome-related degradation of long-lived proteins in adipocytes

ABSTRACT Obesity, a condition most commonly associated with hyper-leptinemia, is also characterized by increased expression of autophagy genes and likely autophagic activity in human adipose tissue (AT). Indeed, circulating leptin levels were previously shown to positively associate with the expression levels of autophagy genes such as Autophagy related gene-5 (ATG5). Here we hypothesized that leptin acts in an autocrine-paracrine manner to increase autophagy in two major AT cell populations, adipocytes and macrophages. We followed the dynamics of autophagosomes following acute leptin administration with or without a leptin receptor antagonist (SMLA) using high-throughput live-cell imaging in murine epididymal adipocyte and macrophage (RAW264.7) cell-lines. In macrophages leptin exerted only a mild effect on autophagy dynamics, tending to attenuate autophagosomes growth rate. In contrast, leptin-treated adipocytes exhibited a moderate, ~20% increase in the rate of autophagosome growth, an effect that was blocked by SMLA. This finding corresponded to mild increases in mRNA and protein expression of key autophagy genes. Interestingly, a long-lived proteins degradation assay uncovered a robust, >2-fold leptin-mediated stimulation of the autophagy/lysosome-related (bafilomycin-inhibited) activity, which was entirely blocked by SMLA. Collectively, leptin regulates autophagy in a cell-type specific manner. In adipocytes, autophagosome dynamics is moderately enhanced, but even more pronounced stimulation is seen in autophagy-related long-lived protein degradation. These findings suggest a causal link between obesity-associated hyperleptinemia and elevated adipocyte and AT autophagy-related processes.
Cyclin G1 and TASCC regulate kidney epithelial cell G2-M arrest and fibrotic maladaptive repair

Cyclin G1 regulates G2-M arrest in proximal tubular cells, promoting a TASCC-induced secretory phenotype, fibrosis, and kidney disease progression. Taking kidney fibrosis to TASCC The kidney has an inherent capacity to recover from acute injury; however, severe injury can lead to chronic kidney disease and fibrosis. Canaud et al. studied kidney epithelial cells’ maladaptive response to injury. The formation of target of rapamycin–autophagy spatial coupling compartments (TASCCs) in proximal epithelial cells was associated with cell cycle arrest and fibrosis in human chronic kidney disease, whereas knocking out cyclin G1 prevented TASCC formation and fibrosis in mouse models. This study provides mechanistic insight into renal fibrosis and identifies a potential therapeutic target. Fibrosis contributes to the progression of chronic kidney disease (CKD). Severe acute kidney injury can lead to CKD through proximal tubular cell (PTC) cycle arrest in the G2-M phase, with secretion of profibrotic factors. Here, we show that epithelial cells in the G2-M phase form target of rapamycin (TOR)–autophagy spatial coupling compartments (TASCCs), which promote profibrotic secretion similar to the senescence-associated secretory phenotype. Cyclin G1 (CG1), an atypical cyclin, promoted G2-M arrest in PTCs and up-regulated TASCC formation. PTC TASCC formation was also present in humans with CKD. Prevention of TASCC formation in cultured PTCs blocked secretion of profibrotic factors. PTC-specific knockout of a key TASCC component reduced the rate of kidney fibrosis progression in mice with CKD. CG1 induction and TASCC formation also occur in liver fibrosis. Deletion of CG1 reduced G2-M phase cells and TASCC formation in vivo. This study provides mechanistic evidence supporting how profibrotic G2-M arrest is induced in kidney injury and how G2-M–arrested PTCs promote fibrosis, identifying new therapeutic targets to mitigate kidney fibrosis.
Prevalence of Gastroesophageal Reflux Disease Symptoms in Infants and Children: A Systematic Review

Objectives: Gastroesophageal reflux disease (GERD) is defined as gastroesophageal reflux causing troublesome symptoms or complications. In this study we reviewed the literature regarding the prevalence of GERD symptoms in infants and children. Methods: Databases of PubMed, EMBASE, and Cochrane were systematically searched from inception to June 26, 2018. English-written studies based on birth cohort, school-based, or general population samples of ≥50 children aged 0 to 21 years were included. Convenience samples were excluded. Results: In total, 3581 unique studies were found, of which 25 studies (11 in infants and 14 in children) were included with data on the prevalence of GERD symptoms comprising a total population of 487,969 children. In infants (0–18 months), GERD symptoms are present in more than a quarter of infants on a daily basis and show a steady decline in frequency with almost complete disappearance of symptoms at the age of 12 months. In children older than 18 months, GERD symptoms show large variation in prevalence between studies (range 0%-38% of study population) and overall, are present in >10% and in 25% on respectively a weekly and monthly basis. Of the risk factors assessed, higher body mass index and the use of alcohol and tobacco were associated with higher GERD symptom prevalence. Conclusions: This systematic review demonstrates that the reported prevalence of GERD symptoms varies considerably, depending on method of data collection and criteria used to define symptoms. Nevertheless, the high reported prevalence rates support better investment of resources and educational campaigns focused on prevention.
Imaging Patterns are Associated with Interstitial Lung Abnormality Progression and Mortality.

Rationale Interstitial lung abnormalities (ILA) are radiologic abnormalities on chest CT scans that have been associated with an early or mild form of pulmonary fibrosis. While ILA have been associated with radiologic progression, it is not known if specific imaging patterns are associated with progression or risk of mortality. Objectives To determine the role of imaging patterns on the risk of death and ILA progression. Methods ILA (and imaging pattern) were assessed in 5320 participants from the AGES-Reykjavik Study, ILA progression was assessed in 3167 participants. Multivariable logistic regression was used to assess factors associated with ILA progression, Cox-proportional hazards models were used to assess time to mortality. Measurements and Main Results Over five years, 327 (10%) had ILA on at least one CT, 1435 (45%) did not have ILA on either CT. Of those with ILA, 238 (73%) had imaging progression, while 89 (27%) had stable to improved imaging; increasing age and copies of MUC5B genotype were associated with imaging progression. The Definite Fibrosis pattern was associated with the highest risk of progression (OR=8.4, 95% CI 2.7-25, P=0.0003). Specific imaging patterns were also associated with an increased risk of death. After adjustment, both a probable UIP and UIP pattern were associated with an increased risk of death when compared to those indeterminate for UIP, (HR=1.7, 95% CI 1.2-2.4, P=0.001) (HR=3.9, 95% CI 2.3-6.8, P<0.0001) respectively. Conclusions In those with ILA, imaging patterns can be used to help predict who is at the greatest risk of progression and early death.
Alignement de corpus multilingues au niveau des phrases

Le regain d'interet pour les methodes a base de corpus, la disponibilite croissante de textes sous forme electronique et l'internationalisation des activites humaines donnent un statut particulier aux corpus multilingues. Un des premiers traitements envisages sur ces corpus est de mettre en correspondance (d'aligner) des segments de texte entre differentes langues. Le cas de l'alignement de phrases est a peu pres maitrise. Les As. examinent ici les principes generaux mis en oeuvre pour realiser cette tâche, et quelques applications possibles
Predictors of new remote cerebral microbleeds after IV thrombolysis for ischemic stroke

Objective To assess the frequency, associated factors, and underlying vasculopathy of new remote cerebral microbleeds (CMB), as well as the risk of concomitant hemorrhagic complications related to new CMBs, after IV thrombolysis (IVT) in acute stroke patients. Methods We conducted an observational study using data from our local thrombolysis registry. We included consecutive stroke patients with MRI (3T)-based IVT and a follow-up MRI the next day between 2008 and 2017 (n = 396). Only CMBs located outside of the ischemic lesions were considered. We also performed a meta-analysis on new CMBs after IVT that included 2 additional studies. Results In our cohort, new remote CMBs occurred in 16/396 patients (4.0%) after IVT and the distribution was strictly lobar in 13/16 patients (81%). Patients with preexisting CMBs with a strictly lobar distribution were significantly more likely to have new CMBs after IVT (p = 0.014). In the random-effects meta-analysis (n = 741), the pooled cumulative frequency of new CMBs after IVT was 4.4%. A higher preexisting CMB burden (>2) was associated with a higher likelihood of new CMBs (odds ratio [OR] 3.6, 95% confidence interval [CI] 1.3–10.3) and new CMBs were associated with the occurrence of remote parenchymal hemorrhage (OR 28.8, 95% CI 8.6–96.4). Conclusions New remote CMBs after IVT occurred in 4% of stroke patients, mainly had a strictly lobar distribution, and were associated with IVT-related hemorrhagic complications. Preexisting CMBs with a strictly lobar distribution and a higher CMB burden were associated with new CMBs after IVT, which may indicate an underlying cerebral amyloid angiopathy.
Systematic Review of the Effectiveness of the Neonatal Early-Onset Sepsis Calculator

Neonatal early-onset sepsis is a serious health concern for term and late preterm infants. Screening for early-onset sepsis is often challenging due to variation in practice, nonspecific laboratory markers, and clinical findings that mimic immaturity. This systematic review evaluates the evidence for the effectiveness of the Neonatal Early-Onset Sepsis Calculator (EOScalc) as a screening tool to appropriately identify neonatal early-onset sepsis and the ability to decrease unnecessary antibiotic use in late preterm and term infants. A comprehensive search of retrospective cohort and retrospective case-control studies was conducted using 5 databases. Studies were included if they evaluated the EOScalc within the defined parameters of use and excluded if they were not published. Six studies were identified and included from 2014 to 2017. Study comparisons varied on the basis of differing clinical practice and use of the EOScalc. Findings included in this review suggest that utilization of the EOScalc can reduce empiric antibiotic therapy, unnecessary laboratory testing, and separation of infants and mothers without increasing infant mortality.
Community participation factors and poor neurocognitive functioning among persons with schizophrenia.

Poor neurocognitive functioning among individuals with schizophrenia is typically conceptualized as resulting from a disease process. The objective of this article is to further expand understanding of poor neurocognition beyond pathogenesis toward a perspective that also incorporates community participation factors. This article focuses on three such factors-sedentary behavior, loneliness, and poverty-that have been demonstrated to be related to neurocognition and are highly prevalent among individuals with schizophrenia. This article provides an overview of the research on each factor and discusses its possible connection to neurocognitive challenges for individuals with schizophrenia. Implications for research, policy, and practice efforts are then proposed to broaden approaches to understanding and addressing neurocognitive challenges in this population. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Robust predictions of specialized metabolism genes through machine learning

Significance Specialized metabolites are critical for plant–environment interactions, e.g., attracting pollinators or defending against herbivores, and are important sources of plant-based pharmaceuticals. However, it is unclear what proportion of enzyme-encoding genes play a role in specialized metabolism (SM) as opposed to general metabolism (GM) in any plant species. This is because of the diversity of specialized metabolites and the considerable number of incompletely characterized pathways responsible for their production. In addition, SM gene ancestors frequently played roles in GM. We evaluate features distinguishing SM and GM genes and build a computational model that accurately predicts SM genes. Our predictions provide candidates for experimental studies, and our modeling approach can be applied to other species that produce medicinally or industrially useful compounds. Plant specialized metabolism (SM) enzymes produce lineage-specific metabolites with important ecological, evolutionary, and biotechnological implications. Using Arabidopsis thaliana as a model, we identified distinguishing characteristics of SM and GM (general metabolism, traditionally referred to as primary metabolism) genes through a detailed study of features including duplication pattern, sequence conservation, transcription, protein domain content, and gene network properties. Analysis of multiple sets of benchmark genes revealed that SM genes tend to be tandemly duplicated, coexpressed with their paralogs, narrowly expressed at lower levels, less conserved, and less well connected in gene networks relative to GM genes. Although the values of each of these features significantly differed between SM and GM genes, any single feature was ineffective at predicting SM from GM genes. Using machine learning methods to integrate all features, a prediction model was established with a true positive rate of 87% and a true negative rate of 71%. In addition, 86% of known SM genes not used to create the machine learning model were predicted. We also demonstrated that the model could be further improved when we distinguished between SM, GM, and junction genes responsible for reactions shared by SM and GM pathways, indicating that topological considerations may further improve the SM prediction model. Application of the prediction model led to the identification of 1,220 A. thaliana genes with previously unknown functions, each assigned a confidence measure called an SM score, providing a global estimate of SM gene content in a plant genome.
Spinal cord neurenteric cyst: clinical and diagnostic findings and long term follow-up in two dogs

Teresa Gagliardo, Daniele Corlazzoli, Marco Rosati, Swan Specchi , Luciano Pisoni, Sara Del Magno, Simona Pappagalli, Greta Galli and Gualtiero Gandini Department of Veterinary Medical Sciences, University of Bologna, Ozzano dell’Emilia, Italy; Veterinary Diagnostic Center Palermovet, Palermo, Italy; Roma Sud Veterinary Clinic, Rome, Italy; Section of Clinical and Comparative Neuropathology, Centre for Clinical Veterinary Medicine, Ludwig-Maximilians-Universit€at M€unchen, Munich, Germany; Department of Diagnostic Imaging, Veterinary Institute of Novara, Granozzo con Monticello, Italy
Sparse Representation Based on the Analysis Model With Optimization on the Stiefel Manifold

Sparse representation with an analysis model becomes a challenging task because the optimization problem involves additional constraints on the analysis dictionary. We present the problem of learning the analysis dictionary through an optimization function with an orthogonality constraint, which can be generally solved by alternately implementing the analysis dictionary update and the sparse coding steps. In the dictionary update step, the traditional method for updating the analysis dictionary with the orthogonality constraint is to first perform the update in Euclidean space without taking the constraint into consideration and then project the solution onto a manifold where the orthogonality constraint is satisfied. However, this projection method is approximate, and the learned dictionary obtained by projection may not embody the inherent structure of the original dictionary. Thus, we develop a novel framework for seamlessly learning the analysis dictionary along with the manifold. This method can provide a more accurate and effective analysis dictionary. In the sparse coding problem, to avoid adjustment of the parameters of the formulation, we introduce an indicator function as the penalty function for optimization of the cost function. Thus, we reduce two parameters to one, which is easier to optimize. Then, we adopt a Douglas–Rachford-like scheme to solve the problem. From the results of the numerical experiments conducted to evaluate the recovery rate of the analysis dictionary, we can intuitively see the performance of the proposed algorithms. Furthermore, the proposed algorithms show good performance in image denoising for realistic applications.
Introduction to the Logistic Regression Model

Regression methods have become an integral component of any data analysis concerned with describing the relationship between a response variable and one or more explanatory variables. Quite often the outcome variable is discrete, taking on two or more possible values. The logistic regression model is the most frequently used regression model for the analysis of these data. Before beginning a thorough study of the logistic regression model it is important to understand that the goal of an analysis using this model is the same as that of any other regression model used in statistics, that is, to find the best fitting and most parsimonious, clinically interpretable model to describe the relationship between an outcome (dependent or response) variable and a set of independent (predictor or explanatory) variables. The independent variables are often called covariates. The most common example of modeling, and one assumed to be familiar to the readers of this text, is the usual linear regression model where the outcome variable is assumed to be continuous. What distinguishes a logistic regression model from the linear regression model is that the outcome variable in logistic regression is binary or dichotomous. This difference between logistic and linear regression is reflected both in the form of the model and its assumptions. Once this difference is accounted for, the methods employed in an analysis using logistic regression follow, more or less, the same general principles used in linear regression. Thus, the techniques used in linear regression analysis motivate our approach to logistic regression. We illustrate both the similarities and differences between logistic regression and linear regression with an example.
Asymmetric Key Cryptography

The term cryptography comes from the two Greek word skrupto and Graph which mean secret and writing. Cryptography is the process of disguising the messages which can only be read by sender and receiver. In cryptography basically the two techniques encryption and decryption are used. There are two types of cryptography process i.e.:Symmetric key cryptography (private key cryptography). Asymmetric key cryptography (public key cryptography).
Novel Combination of Mesenchymal Stem Cell-Conditioned Medium with Sorafenib Have Synergistic Antitumor Effect of Hepatocellular Carcinoma Cells

Objective: Hepatocellular carcinoma (HCC) is the most common liver malignancy. Sorafenib is the first-line systemic treatment for advanced HCCs. However, due to safety concerns, researchers are now looking for ways to boost the efficacy of the medication. One approach for reducing toxicity is combining sorafenib with other agents so that a lower dose of sorafenib is required. Mesenchymal stromal cells (MSCs) can have an inhibitory effect on HCC tumor growth. Mesenchymal Stem Cell-Conditioned Medium (MSC-CM) is the substance extracted from MSC culture and contains most of the potential cytokines secreted by MSCs. We, therefore, anticipated a synergistic Antitumor Effect of sorafenib in Combination with MSC-CM. In this study, we used HepG2 as our target cell lines. Methods: HepG2 cells were treated with sorafenib alone and with sorafenib + MSC-CM. CCK-8 assay was used to evaluate and compare the inhibition of cell growth between the two groups with different treatments. Results: The combination treatment of cell lines with sorafenib and MSC-CM had significantly reduced the values of IC50 compared to the use of sorafenib alone (3.4 vs. 2.7 respectively). Conclusion: This study suggests that a combination of sorafenib with MSC-CM can synergistically suppress the growth of HCC cells.
Analysis of Protein-Protein Interaction Networks Using High Performance Scalable Tools

Protein-Protein Interaction (PPI) Research currently generates an extraordinary amount of publications and interest in fellow computer scientists and biologists alike because of the underlying potential of the source material that researchers can work with. PPI networks are the networks of protein complexes formed by biochemical events or electrostatic forces serving a biological function [1] . Since the analysis of the protein networks is now growing, we have more information regarding protein, genomes and their influence on life. Today, PPI networks are used to study diseases, improve drugs and understand other processes in medicine and health that will eventually help mankind. Though PPI network research is considered extremely important in the field, there is an issue – we do not have enough people who have enough interdisciplinary knowledge in both the fields of biology and computer science; this limits our rate of progress in the field. Most biologists that are not expert coders need a way of calculating graph values and information that will help them analyze the graphs better without having to manipulate the data themselves. In this research, I test a few ways of achieving results through the use of available frameworks and algorithms, present the results and compare each method’s efficacy. My analysis takes place on very large datasets where I calculate several centralities and other data from the graph using different metrics, and I also visualize them in order to gain further insight. I also managed to note the significance of MPI and multithreading on the results thus obtained that suggest building scalable tools will help improve the analysis immensely.
Learning by Competition: Dual Discriminator Generative Adversarial Networks

There has been a resurgence of interest in generative adversarial networks (GANs) in recent years. The overall performance of the generator depends on how well the discriminator is trained. In this study, we use two discriminators and one generator in the adversarial architecture. Each discriminator has a different perspective on how to evaluate the generated data, which makes the dual discriminators compete with each other to improve the performance of the generator output. The competition of the discriminators is evaluated using a zero-sum game in order to make the optimizer converge into the Nash-equilibrium. Experimental results show that the proposed approach has better visual performance and the EM-distance metric over some well-known GAN models, including DCGAN, BEGAN, WGAN, and WGAN-GP.
Optimizing supercompilers for supercomputers

Between a problem statement and its solution as a computer simulation are several steps, from choosing a method, writing a program, compiling to machine code, making runtime decisions, and hardware execution. Here we will look at the middle three decision points. What decisions should be and must be left to the programmer? What decisions should be and must be relegated to a compiler? What decisions should be and must be left until runtime? Given my background, I will focus a great deal on the importance of compilers in supercomputing, and compare and contrast the advantages and impacts of compiler solutions to the "Performance + Portability + Productivity" problem with language and runtime solutions.