Miracle-dz/UniFAR-FTUs huggingface dataset:
DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks

The combinatorial search space presents a significant challenge to learning causality from data. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint, allowing for the exploration of deep generative models to better capture data sample distributions and support the discovery of Directed Acyclic Graphs (DAGs) that faithfully represent the underlying data distribution. However, so far no study has investigated the use of Wasserstein distance for causal structure learning via generative models. This paper proposes a new model named DAG-WGAN, which combines the Wasserstein-based adversarial loss, an auto-encoder architecture together with an acyclicity constraint. DAGWGAN simultaneously learns causal structures and improves its data generation capability by leveraging the strength from the Wasserstein distance metric. Compared with other models, it scales well and handles both continuous and discrete data. Our experiments have evaluated DAG-WGAN against the state-of-the-art and demonstrated its good performance.
Educational attainment, structural brain reserve and Alzheimer’s disease: a Mendelian randomization analysis

Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes, and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n=1,131,881), late-onset Alzheimer's disease (35,274 cases, 59,163 controls), and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax=33,211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease, (ii) educational attainment and imaging-derived phenotypes, (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the protective effect of education on Alzheimer's disease risk. Genetically-proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically-predicted years of schooling = 0.70, 95% confidence interval [CI]: 0.60, 0.80). There was also evidence for positive associations between genetically-predicted educational attainment and four cortical macro-structure metrics (surface area: {beta}=0.30, 95% CI: 0.20, 0.40; volume: {beta}=0.29, 95% CI: 0.20, 0.37; intrinsic curvature: {beta}=0.18, 95% CI: 0.11, 0.25; local gyrification index: {beta}=0.21, 95% CI: 0.11, 0.31), as well as inverse associations with cortical intracellular volume fraction ({beta}=-0.09, 95% CI: -0.15, -0.03) and white matter hyperintensities volume ({beta}=-0.14, 95% CI: -0.23, -0.05). Genetically-proxied levels of three cortical macro-structure metrics were positively associated with years of education (surface area: {beta}=0.13, 95% CI: 0.10, 0.16; volume: {beta}=0.15, 95% CI: 0.11, 0.19; intrinsic curvature: {beta}=0.12, 95% CI: 0.04, 0.19). We found no evidence of associations between genetically-predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically-predicted education with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as bi-directional causal associations between education and several brain macro- and micro-structure metrics. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.
On the Recursiveness of Pascal Sequences

When Pascal’s triangle is displayed in the first quadrant of the coordinate plane with Pascal numbers lying on the nonnegative integer lattice points, the diagonals of the array with nonnegative rational slopes give rise to infinite sequences of binomial coefficients. Does each sequence satisfy a linear recurrence relation with constant coefficients? We provide a complete answer to this question.
Metabolomics in the field of biomedical research

Metabolomics is a relatively new high-throughput technology that aims at measuring all endogenous metabolites within a biological sample in an unbiased fashion. The resulting metabolic profiles may be regarded as functional signatures of the physiological state, and have been shown to comprise effects of genetic regulation as well as environmental factors. This potential to connect genotypic to phenotypic information promises new insights and biomarkers for different research fields, including biomedical and pharmaceutical research. In the statistical analysis of metabolomics data, many techniques from other omics fields can be reused. However recently, a number of tools specific for metabolomics data have been developed as well. The focus of this mini review will be on recent advancements in the analysis of metabolomics data especially by utilizing Gaussian graphical models and independent component analysis. Statistical methods for the analysis of high-throughput metabolomics
Instagram Images and Videos Popularity Prediction: a Deep Learning-Based Approach

In recent years, social media platforms have seen tremendous growth in terms of the number of users, forms of interaction, and diversity of content. While these channels are purely a source of entertainment for many users, for others they represent the main source of revenue or advertising for their products and services. In order to capture users’ attention, companies and professionals aim at achieving high popularity of their posts. In this work, we aspire to predict post popularity on the Instagram platform through Machine Learning approaches, with the goal of presenting a methodological tool that could provide useful information for post performance optimization. While previous contributions on the subject addressed the generic popularity of a post on the platform, we focus on the post popularity on a speciﬁc proﬁle using only the visual content related to the post (image or video). We describe in detail the process and workﬂow to design a measure of popularity consistent even over the long time frame. Furthermore, we take advantage of state-of-the-art Convolutional Neural Networks and provide interpretability traits for their predictions, a quality that is nowadays highly welcomed in the industry. Lastly, we use a situation of scarce video data to ex-periment with ways of performing mixed training with both images and videos, providing problem-independent ideas and architectures that can potentially be applied to other video classiﬁcation tasks.
Anesthesia Induced Developmental Neurotoxicity (AIDN)

“How do we determine what are physical, chemical and psychological hazards of occupation and in particular those that are rare and not easily recognized? ” and “. the available human studies . cannot exclude the possibility that the anesthesiainduced neurotoxicity observed in many animal studies may also occur in children” Sir Austin Bradford Hill, Royal Society of Medicine,1965
The Bilingual Mental Lexicon in L2 Sentence Processing

This paper explores the consequences of the language-integrated nature of the bilingual mental lexicon for L2 sentence processing. It reviews L2 processing studies on gender agreement and syntactic structure building that test whether delays and cross-linguistic influence in lexical processing of the L2 lead to differences between L2 and L1 sentence processing. Slower L2 lexical processing delays and attenuates effects of syntactic structure in L2 sentence processing. In addition, cross-linguistic lexical influence can engender non-target patterns in L2 compared to L1 sentence processing. The paper spells out the assumptions and predictions of the Lexical Bottleneck Hypothesis, and I discuss how its insights can be incorporated into current models of L2 sentence processing.
Evidence for a common pharmacological interaction site on K Ca 2 channels providing both selective activation and selective inhibition of the human K Ca 2.1 subtype

We have previously identified Ser293 in transmembrane segment 5 as a determinant for selective K Ca 2.1 channel activation by GW542573X (4-(2-methoxyphenylcarbamoyloxymethyl)- piperidine-1-carboxylic acid tert -butyl ester). Now we show that Ser293 mediates both activation and inhibition of K Ca 2.1: CM-TPMF
In Silico Classification of Major Clearance Pathways of Drugs with their Physiochemical Parameters

Predicting major clearance pathways of drugs is important in understanding their pharmacokinetic properties in clinical use, such as drug-drug interactions and genetic polymorphisms, and their subsequent pharmacological/toxicological effects. Here we established an in silico classification method to predict the major clearance pathways of drugs by identifying the boundaries of physicochemical parameters in empirical decisions for each clearance pathway. It requires only 4 physicochemical parameters (charge, molecular weight (MW), lipophilicity (log D), and protein unbound fraction in plasma (fup)) that were predicted from their molecular structures without performing any benchwork experiments. The training dataset consisted of 141 approved drugs, whose major clearance pathways were determined to be metabolism by CYP3A4, CYP2C9 and CYP2D6, hepatic uptake by OATPs, or renal excretion in an unchanged form. After grouping by charge, each drug was plotted in a 3-dimensional space according to three axes of MW, log D, and fup. Then, rectangular boxes for each clearance pathway were drawn mathematically under the criterion of “maximizing F-value (harmonic mean of precision and recall) with minimum volume”, yielding to a precision of 88% which was confirmed through two types of validation; leave-one-out method and validation using a new dataset. With further modification towards multiple pathways and/or other pathways, not only would this in silico classification system be useful for industrial scientists at the early stage of drug development, which can lead to the selection of candidate compounds with optimum pharmacokinetic properties, but also for regulators in evaluating new drugs and giving regulatory requirements that are pharmacokinetically reasonable.
MILLENIALS ATTITUDES AND INTENSIONS TO USE A SMARTPHONE APP TO PROMOTE HEALTH AND HYGIENE HABITS

With the improvement of new innovations, the quantity of studies that attempt to assess customer conduct towards the utilization of health and hygiene applications has expanded. The point of this examination is to play out a methodical survey of the writing on the expectation to utilize versatile (applications) identified with the field of wellness and physical movement by buyers. This efficient survey is a basic assessment of the outcome from quantitative examinations in the field of appraisal of purchaser conduct towards health and hygiene applications. A sum of 13 examinations is dissected that propose models for assessing the aims to utilize wellness applications by sport buyers.
Steady suction for controlling across‐wind loading of high‐rise buildings

To reduce across‐wind effects on high‐rise buildings, this paper introduces a new active aerodynamic control named steady suction. To test its effect, the control mechanism of steady suction is discussed first, and then, a synchronization pressure test was conducted in a wind tunnel to measure the across‐wind loading on a high‐rise model (Commonwealth Advisory Aeronautical Research Council standard high‐rise building model). A series of analytical methods were used to compare the different effects on across‐wind aerodynamic forces caused by different parameters. The results show that when the wind blows straight on the wide side of the model, steady suction arranged on the narrow side close to the leading edge can effectively reduce the fluctuating base moment. When the wind blows straight on the narrow side, steady suction arranged on the middle of the wide side effectively reduces the fluctuating base moment. Copyright © 2016 John Wiley & Sons, Ltd.
Determination of Heavy Metals of Commercial Bottled Water In Indonesia By ICP / MS

Heavy metals and toxic elements four brands of commercial bottled water from Indonesia market were analyzed with an Inductive Couple Plasma-Mass Spectrometer (ICP-MS). The heavy metals and toxic elements to be analyzed were copper (Cu), iron (Fe), lead (Pb), cadmium (Cd), chromium (Cr), arsenic (As), selenium (Se) and manganese (Mn). The results are the heavy metals and toxic elements from all commercial water samples were below the permissible level of the World Health Organization (WHO) and Standar Nasional Indonesia (SNI). Hence, this determination clarify no risk about quality and safety of commercial bottled water for public drinking water.
The Classiﬁcation of Arch Fingerprint Using Mathematical Model and Deep Learning Features Selection

Fingerprints are unique patterns, made by friction ridges and fur-rows, which appear on the pads of the ﬁngers and thumbs. The analysis of ﬁngerprint is valuable tool to identify suspects and crimes. There are three basic types of ﬁngerprints, one of them is the arch ﬁngerprint. In this paper, the classes of the arch ﬁngerprint are modelled in a dynamical system. The global dynamics and the existence and stability of equilibria are studied. The orientation image of the arch ﬁngerprint is visualized in smooth deformation of the phase por-trait of a planar system. Most ﬁngerprint datasets are not categorized to be retained by deep learning computer tools that allow to classify a new ﬁngerprint input image to its class, so ﬁnding a dynamical system to categorize ﬁngerprint image data set allows deep learning computer science program to be retrained with more accuracy. Convolutional Neural Networks (CNNs) architectures are computerized
MeCP2 Protein in RETT Syndrome and Autism

The RETT(RTT) syndrome is a x-linked dominant disorder in the development of nervous system. It has been found out that mutation in the MeCP2 gene considered as a transcription suppressor is responsible for the occurance of 90% of RETT syndrome. Autism is a common brain developmental irregularity that shows complicated genetic mechanisms on analysis. However, it is not very well known if the mutation in the MeCP2 gene may lead to autism. This review explains both the disorders and describes the role of MeCP2 in the functioning of the disease. The review also summarizes the experiment performed on 69 autistic females to observe the MeCP2 gene mutation in them.
Parental income gradients in child and adolescent mortality: Norwegian trends over half a century

ABSTRACT Background: Child mortality declined rapidly the last century in many high-income countries. However, little is known about the socioeconomic differences in this decline, and whether these vary across causes of death. Methods: We use register data including all Norwegian births between 1968 to 2010 (2.1 million), and analyse how all-cause and cause-specific child (0-5) and adolescent mortality (6-21) vary with relative parental income the year before the birth Results: Child all-cause mortality decreased with increasing parental relative income within all birth cohorts. Among children aged 0-5, the socioeconomic gradient for all-cause, accidental deaths, sudden infant death syndrome and perinatal factors declined over the periode, while there was no systematic decline related to congenital malformations. Among children 6-21, the gradient did not weaken similarly, although there were indications of declines in the soci-oeconomic gradient related to all-cause deaths, accidents, and suicides. While the absolute differences in mortality declined over time, the relative differences remained stable. Conclusion: There has been a large reduction in child mortality in all socioeconomic groups across 50 years for all-cause and most cause-specific mortality groups. However, children of parents in the lowest part of the income distribution still have an elevated mortality, and the relative differences have not always been declining.
Risk factors of chronic liver disease among adult patients in tertiary hospitals, Northern Ethiopia: An unmatched case-control study

Introduction: Chronic liver disease imposed substantial health and economic burden causing 1.75 million deaths and increased hospital admission rates. However, it is a neglected health issue in resource-limited countries like Ethiopia, with the paucity of data on the determinants of chronic liver disease. Besides, available studies considered single or limited factors. Thus, the aim of this study was to assess the determinant factors of chronic liver disease among patients who were attending at the gastroenterology units. Methods: An unmatched case-control study was conducted on 281 (94 cases and 187 controls) systematically selected subjects at tertiary hospitals in Northern Ethiopia from November 2018 to June 2019. Data were collected using an interviewer-administered questionnaire and checklists, entered to Epi data version 4.4. 2, and analyzed using SPSS version 25. Bi-variable and multivariate analyses were done using binary logistic regression. Variables with p< 0.25 in the bi-variable analysis were fitted to the final model. An adjusted odds ratio with 95% CI was used to declare significance and associations. Results: An overall 281 (94 cases and 187 controls) patients attending at the gastroenterology units had been included with a response rate of 100%. Being chronic alcohol consumer (AOR=2.8; 95% CI: 1.1-7.0), having a history of herbal medication use (AOR=14; 95% CI: 5.2-42), history of injectable drug use (AOR=8.7; 95% CI: 3-24.8), and hepatitis B infection (AOR=12; 95% CI: 3.0-49) were found to have an independent association with chronic liver disease. Conclusion: Alcohol consumption, history of herbal medication use, hepatitis B infection, and history of parenteral medication use were found to be determinant factors of chronic liver disease. Strengthening viral hepatitis screening activities, interventions focused on behavioral change, and linking traditional healers to the healthcare system is crucial.
Profile of estetrol, a promising native estrogen for oral contraception and the relief of climacteric symptoms of menopause

ABSTRACT Introduction Estrogens used in women’s healthcare have been associated with increased risks of venous thromboembolism (VTE) and breast cancer. Estetrol (E4), an estrogen produced by the human fetal liver, has recently been approved for the first time as a new estrogenic component of a novel combined oral contraceptive (E4/drospirenone [DRSP]) for over a decade. In phase 3 studies, E4/DRSP showed good contraceptive efficacy, a predictable bleeding pattern, and a favorable safety and tolerability profile. Areas covered This narrative review discusses E4ʹs pharmacological characteristics, mode of action, and the results of preclinical and clinical studies for contraception, as well as for menopause and oncology. Expert opinion Extensive studies have elucidated the properties of E4 that underlie its favorable safety profile. While classical estrogens (such as estradiol) exert their actions via both activation of nuclear and membrane estrogen receptor α (ERα), E4 presents a specific profile of ERα activation: E4 binds and activates nuclear ERα but does not induce the activation of membrane ERα signaling pathways in specific tissues. E4 has a small effect on normal breast tissue proliferation and minimally affects hepatic parameters. This distinct profile of ERα activation, uncoupling nuclear and membrane activation, is unique.
Hijacking Methyl Reader Proteins for Nuclear-Specific Protein Degradation

Targeted protein degradation (TPD) by PROTACs is a promising strategy to control disease-causing protein levels within the cell. While TPD is emerging as an innovative drug discovery paradigm, there are currently only a limited number of E3 ligase: ligand pairs that are employed to induce protein degradation. Herein, we report a novel approach to induce protein degradation by hijacking a methyl reader: E3 ligase complex. L3MBTL3 is a methyl lysine reader protein that binds to the Cul4DCAF5 E3 ligase complex and targets methylated proteins for proteasomal degradation. By co-opting this natural mechanism, we report the design and biological evaluation of L3MBTL3-recruiting PROTACs and demonstrate nuclear-specific degradation of FKBP12 and BRD2. We envision this as a generalizable approach to utilize other reader protein-associated E3 ligase complexes in PROTAC design to expand the E3 ligase toolbox and explore the full potential of TPD.
Practical Perfusion Quantification in Multispectral Endoscopic Video: Using the Minutes after ICG Administration to Assess Tissue Pathology

The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by using fluorescent dyes, typically Indocyanine Green (ICG). Due to their often leaky and chaotic vasculatures, intravenously administered ICG perfuses through cancerous tissues differently. We investigate here how a few characteristic values derived from the time series of fluorescence can be used in simple machine learning algorithms to distinguish benign lesions from cancers. These features capture the initial uptake of ICG in the colon, its peak fluorescence, and its early wash-out. By using simple, explainable algorithms we demonstrate, in clinical cases, that sensitivity (specificity) rates of over 95% (95%) for cancer classification can be achieved.
Structures of synthetic helical filaments and tubes based on peptide and peptido-mimetic polymers

Abstract Synthetic peptide and peptido-mimetic filaments and tubes represent a diverse class of nanomaterials with a broad range of potential applications, such as drug delivery, vaccine development, synthetic catalyst design, encapsulation, and energy transduction. The structures of these filaments comprise supramolecular polymers based on helical arrangements of subunits that can be derived from self-assembly of monomers based on diverse structural motifs. In recent years, structural analyses of these materials at near-atomic resolution (NAR) have yielded critical insights into the relationship between sequence, local conformation, and higher-order structure and morphology. This structural information offers the opportunity for development of new tools to facilitate the predictable and reproducible de novo design of synthetic helical filaments. However, these studies have also revealed several significant impediments to the latter process – most notably, the common occurrence of structural polymorphism due to the lability of helical symmetry in structural space. This article summarizes the current state of knowledge on the structures of designed peptide and peptido-mimetic filamentous assemblies, with a focus on structures that have been solved to NAR for which reliable atomic models are available.