Miracle-dz/UniFAR-FTUs huggingface dataset:
Kidney Function, ACE-Inhibitor/Angiotensin Receptor Blocker Use, and Survival Following Hospitalization for Heart Failure: A Cohort Study

Background: Angiotensin-converting enzyme inhibitors/angiotensin receptor blocker (ACE-I/ARB) improve outcomes in patients with heart failure and reduced left-ventricular (LV) systolic function. However, these medications can cause a rise in serum creatinine and their benefits in patients with HF accompanied by kidney disease are less certain. Objective: To characterize associations between estimated glomerular filtration rate (eGFR), patterns of ACE-Is and ARBs use, and 1-year survival following hospitalization for heart failure (HF). Design: We formed a retrospective cohort study of patients admitted with HF and followed HF medication prescriptions using the pharmaceutical information network, stratified by discharge eGFR. Setting: Cardiology services in 3 centers in Southern Alberta, Canada. Patients: The study cohort included patients admitted to hospital with a clinical diagnosis of HF. Measurements: eGFR was determined from inpatient laboratory data prior to discharge. Outpatient prescription data prior to and following the index hospitalization was obtained using the Pharmaceutical Information Network of Alberta and survival was determined from provincial vital statistics. Methods: Characteristics of the HF cohort were obtained from the Admissions Module of the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. Multivariable Cox proportional hazards models were used to evaluate the association between time-varying ACE-I/ARB use, and mortality, and to test whether eGFR modified this association. Results: Totally, 1404 patients were included. Within the first 3 months following discharge, ACE-I/ARBs were used in 71%, 67%, 62%, and 52% for those with eGFR > 90, 45-89, 30-44, and < 30 mL/min/1.73 m2, respectively, with differences in use persisting after 1 year of follow-up. Patients with eGFR < 45 mL/min/1.73 m2 had significantly lower rates of ACE-I/ARB use following hospitalization. In adjusted models, ACE-I/ARB use following discharge was associated with 25% lower risk of mortality (Hazard Ratio [HR]: 0.75, 95% confidence interval [CI]: 0.61-0.92; P < 0.01), without evidence that this association differed by eGFR (P = 0.75). Limitations: LV function measurements were not available for the cohort. Due to the observation design of the study, treatment-selection bias may be present. Conclusion: Patients with HF and reduced eGFR at time of hospital discharge were less likely to receive ACE-I/ARB despite these medications being associated with lower mortality independent of eGFR. These findings demonstrate the need for further research on strategies for safe use of ACE-I and ARB in patients with HF and kidney disease.
Mathematics Content Understanding for Cyberlearning via Formula Evolution Map

Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula. In this paper, we propose a novel problem, "mathematics content understanding", for cyberlearning and cyberreading. To address this problem, we create a Formula Evolution Map (FEM) offline and implement a novel online learning/reading environment, PDF Reader with Math-Assistant (PRMA), which incorporates innovative math-scaffolding methods. The proposed algorithm/system can auto-characterize student emerging math-information need while reading a paper and enable students to readily explore the formula evolution trajectory in FEM. Based on a math-information need, PRMA utilizes innovative joint embedding, formula evolution mining, and heterogeneous graph mining algorithms to recommend high quality Open Educational Resources (OERs), e.g., video, Wikipedia page, or slides, to help students better understand the math-content in the paper. Evaluation and exit surveys show that the PRMA system and the proposed formula understanding algorithm can effectively assist master and PhD students better understand the complex math-content in the class readings.
IMFlexCom

In this article, we propose an In-Memory Flexible Computing platform (IMFlexCom) using a novel Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array architecture, which could work in dual mode: memory mode and computing mode. Such intrinsic in-memory logic (AND/OR/XOR) could be used to process data within memory to greatly reduce power-hungry and long distance massive data communication in conventional Von Neumann computing systems. A comprehensive reliability analysis is performed, which confirms ∼90mV and ∼10mV (worst-case) sense margin for memory and in-memory logic operation in variations on resistance-area product and tunnel magnetoresistance. We further show that sense margin for in-memory logic computation can be significantly increased by increasing the oxide thickness. Furthermore, we employ bulk bitwise vector operation and data encryption engine as case studies to investigate the performance of our proposed design. IMFlexCom shows ∼35× energy saving and ∼18× speedup for bulk bitwise in-memory vector AND/OR operation compared to DRAM-based in-memory logic. Again, IMFlexCom can achieve 77.27% and 85.4% lower energy consumption compared to CMOS-ASIC- and CMOL-based Advanced Encryption Standard (AES) implementations, respectively. It offers almost similar energy consumption as recent DW-AES implementation with 66.7% less area overhead.
AI Painting: An Aesthetic Painting Generation System

There are many great works done in image generation. However, it is still an open problem how to generate a painting, which is meeting the aesthetic rules in specific style. Therefore, in this paper, we propose a demonstration to generate a specific painting based on users' input. In the system called AI Painting, we generate an original image from content text, transfer the image into a specific aesthetic effect, simulate the image into specific artistic genre, and illustrate the painting process.
High Performance Computing with FPGAs and OpenCL

In this work we evaluate the potential of FPGAs for accelerating HPC workloads as a more power-efficient alternative to GPUs. Using High-Level Synthesis and a large set of optimization techniques, we show that FPGAs can achieve better performance than CPUs, and better power efficiency than both CPUs and GPUs for typical HPC workloads. Furthermore, we show that for the specific case of stencil computation, the unique architectural advantages of FPGAs allow them to surpass high-end CPU, Xeon Phi and GPU devices. Unlike previous work, our FPGA-based stencil accelerator combines spatial blocking with temporal blocking to achieve high performance without restricting input size. With support for high-order stencils, we achieve the highest single-FPGA performance for 2D and 3D stencil computation of any order, to this day.
Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems

Two-sided marketplaces are platforms that have customers not only on the demand side (e.g. users), but also on the supply side (e.g. retailer, artists). While traditional recommender systems focused specifically towards increasing consumer satisfaction by providing relevant content to consumers, two-sided marketplaces face the problem of additionally optimizing for supplier preferences, and visibility. Indeed, the suppliers would want afair opportunity to be presented to users. Blindly optimizing for consumer relevance may have a detrimental impact on supplier fairness. Motivated by this problem, we focus on the trade-off between objectives of consumers and suppliers in the case of music streaming services, and consider the trade-off betweenrelevance of recommendations to the consumer (i.e. user) andfairness of representation of suppliers (i.e. artists) and measure their impact on consumersatisfaction. We propose a conceptual and computational framework using counterfactual estimation techniques to understand, and evaluate different recommendation policies, specifically around the trade-off between relevance and fairness, without the need for running many costly A/B tests. We propose a number of recommendation policies which jointly optimize relevance and fairness, thereby achieving substantial improvement in supplier fairness without noticeable decline in user satisfaction. Additionally, we consider user disposition towards fair content, and propose a personalized recommendation policy which takes into account consumer's tolerance towards fair content. Our findings could guide the design of algorithms powering two-sided marketplaces, as well as guide future research on sophisticated algorithms for joint optimization of user relevance, satisfaction and fairness.
AI education matters: teaching with deep learning frameworks in introductory machine learning courses

In this article, we demonstrate an assignment1 in which students use TensorFlow to build a face recognition system. Students build shallow and deep neural networks for face recognition in TensorFlow and use transfer learning to obtain near-perfect performance on a simple face recognition task. Visualizing neural networks in order to explain how they work is central to the assignment.
A High-Content Screening Assay for the Discovery of Novel Proteasome Inhibitors from Formosan Soft Corals

The ubiquitin-proteasome system (UPS) is a major proteolytic pathway that safeguards protein homeostasis. The main 26S proteasome consists of a 20S catalytic core proteasome and a 19S substrate recognition proteasome. UPS dysfunction underlies many important clinical diseases involving inflammation, tumors, and neurodegeneration. Currently, three 20S proteasome inhibitors, bortezomib, carfilzomib, and ixazomib, have been approved for the treatment of multiple myeloma. We aim to screen UPS inhibitors for biomedical purposes. The protein interaction network of human cytomegalovirus UL76 targets UPS, resulting in aggregations of ubiquitinated proteins termed aggresomes. In this study, we demonstrated that cell-based high-content measurements of EGFP-UL76 aggresomes responded to bortezomib and MG132 treatment in a dose-dependent manner. Employing this high-content screening (HCS) assay, we screened natural compounds purified from Formosan soft corals. Four cembrane-based compounds, sarcophytonin A (1), sarcophytoxide (2), sarcophine (3), and laevigatol A (4), were found to enhance the high-content profiles of EGFP-UL76 aggresomes with relative ratios of 0.2. By comparison to the mechanistic action of proteasome inhibitors, compounds 1 and 3 modulated the accumulation of ubiquitinated proteins, with a unique pattern likely targeting 19S proteasome. We confirmed that the EGFP-UL76 aggresome-based HCS system greatly improves the efficacy and sensitivity of the identification of proteasome inhibitors.
Musicality-Novelty Generative Adversarial Nets for Algorithmic Composition

Algorithmic composition, which enables computer to generate music like human composers, has lasting charm because it intends to approximate artistic creation, most mysterious part of human intelligence. To deliver both melodious and refreshing music, this paper proposes the Musicality-Novelty Generative Adversarial Nets for algorithmic composition. With the same generator, two adversarial nets alternately optimize the musicality and novelty of the machine-composed music. A new model called novelty game is presented to maximize the minimal distance between the machine-composed music sample and any human-composed music sample in the novelty space, where all well-known human composed music products are far from each other. We implement the proposed framework using three supervised CNNs with one for generator, one for musicality critic and one for novelty critic on the time-pitch feature space. Specifically, the novelty critic is implemented by Siamese neural networks with temporal alignment using dynamic time warping. We provide empirical validations by generating the music samples under various scenarios.
Video Understanding via Convolutional Temporal Pooling Network and Multimodal Feature Fusion

In this paper, we present a new end-to-end convolutional neural network architecture for video classification, and apply the model to action and scene recognition in untrimmed videos for the Challenge on Comprehensive Video Understanding in the Wild. The proposed architecture takes densely sampled video frames as inputs, and apply a temporal pooling operator inside the network to capture temporal context of the input video. As a result, our architecture outputs distinct video-level features with a set of different temporal pooling operators. Furthermore, we design a multimodal feature fusion model by concatenating our video-level features with those given in the challenge dataset. Experimental results on the challenge dataset demonstrate that the proposed architecture and the multimodal feature fusion approach together achieve outstanding performance in action and scene recognition.
PotentialNet for Molecular Property Prediction

The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein–ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning—instead of feature engineering—deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein–ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EFχ(R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.
Benefits of β-hydroxy-β-methylbutyrate supplementation in trained and untrained individuals

ABSTRACT β-Hydroxy-β-Methylbutyrate (HMB) is a metabolite of the branched-chain amino acid leucine and its ketoacid α-ketoisocaproate. HMB has been widely used as an ergogenic supplement to increase muscle strength, muscle hypertrophy and enhance recovery. The physiological mechanisms that underlie these benefits are related to HMB’s ability to stimulate muscle protein synthesis and minimize muscle breakdown. Although evidence supporting the benefits of HMB supplementation is not conclusive, many of these studies have suffered from methodological flaws including different formulations, supplement duration and population studied. HMB in its free acid formulation is suggestive of having a greater potential for efficacy in both trained and untrained populations than its calcium-salt form. However, the evidence regarding HMB’s role in limiting muscle degradation and increasing muscle protein synthesis has created an exciting interest in examining its efficacy among untrained individuals. Recent investigations examining intense training have demonstrated efficacy in maintaining muscle mass and attenuating the inflammatory response.
SING: Symbol-to-Instrument Neural Generator

Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz. In this work, we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity. Our model is trained end-to-end to generate notes from nearly 1000 instruments with a single decoder, thanks to a new loss function that minimizes the distances between the log spectrograms of the generated and target waveforms. On the generalization task of synthesizing notes for pairs of pitch and instrument not seen during training, SING produces audio with significantly improved perceptual quality compared to a state-of-the-art autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is about 32 times faster for training and 2, 500 times faster for inference.
Rasip1 is a RUNX1 target gene and promotes migration of NSCLC cells

Background Runt-related transcription factor 1 (RUNX1), an essential regulator of hematopoiesis, is overexpressed in patients with nonsmall-cell lung cancer (NSCLC) and is correlated with enhanced metastatic ability. Ras-interacting protein 1 (Rasip1), a potential oncogene, is required for blood vessel formation, and recently, it has been shown that Rasip1 is widely expressed in NSCLC patients. We noticed that Rasip1 promoter contains several potential RUNX1-binding sequences. However, the relationship between Rasip1 and RUNX1 in NSCLC is still unknown. In this study, the potential function of RUNX1 involving in Rasip1 expression and the potential role of Rasip1 in lung cancer cells were investigated. Materials and methods Rasip1 and RUNX1 expressions were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting in NSCLC cells lines. A549 and H1299 cells were transfected with plasmids or interfering RNA (siRNA) to upregulate or downregulate the expression of Rasip1 and RUNX1. Cell motility was assessed by transwell and wound-healing assay. Location of Rasip1 and RUNX1 was detected via immunofluorescence. Meanwhile, chromatin immunoprecipitation was done using an anti-RUNX1 antibody. Rasip1 promoter was constructed, and cells were lysed for the analysis of luciferase activity. Results In this study, we showed that ectopic expression or knockdown of RUNX1 resulted in a significant increase or reduction in Rasip1 expression, respectively. RUNX1 bound directly to a specific DNA sequence within Rasip1 promoter and modulated its transcription. Furthermore, silencing of Rasip1 inhibited the migration of RUNX1-overexpressing NSCLC cells through inactivation of Rac1 pathway. Moreover, we found that Rasip1 was expressed ubiquitously in NSCLC cells lines and enhanced cell migration. In addition, EGFR signaling was involved both in the expression and the subcellular localization of Rasip1. Conclusion Our data indicated that Rasip1 is regulated in part by the transcription factor RUNX1 and might be developed as a therapeutic target for NSCLC.
Chemical induction of hepatic apoptosis in rodents

The urge of identifying new pharmacological interventions to prevent or attenuate liver injury is of critical importance and needs an expanded experimental toolbox. Hepatocyte injury and cellular death is a prominent feature behind the pathology of liver diseases. Several research activities focused on identifying chemicals and hepatotoxicants that induce cell death by apoptosis, in addition to presenting its corresponding signaling pathway. Although such efforts provided further understanding of the mechanisms of cell death, it has also raised confusion concerning identifying the involvement of several modes of cell death including apoptosis, necrosis and fibrosis. The current review highlights the ability of several chemicals and potential hepatotoxicants to induce liver damage in rodents by means of apoptosis while the probable involvement of other modes of cell death is also exposed. Thus, several chemical substances including hepatotoxins, mycotoxins, hyperglycemia inducers, metallic nanoparticles and immunosuppressant drugs are reviewed to explore the hepatic cytotoxic spectrum they could exert on hepatocytes of rodents. In addition, the current review address the mechanism by which hepatotoxicity is initiated in hepatocytes in different rodents aiding the researcher in choosing the right animal model for a better research outcome.
Comparison of miRNA expression profiles in individuals with chronic or aggressive periodontitis.

OBJECTIVES MicroRNAs (miRNAs) may play an important role in inflammatory response. However, the involvement of miRNAs in the pathogenesis of periodontitis is unclear. The present study aimed to compare the miRNA expression profiles in individuals with chronic (CP) or aggressive (AP) periodontitis. MATERIALS AND METHODS Eighteen non-smoker individuals (CP = 9 and AP = 9) without any history of systemic diseases or previous periodontal therapies were selected at the Clinics of Periodontology from the Federal University of Minas Gerais. Gingival tissue samples were collected during the initial periodontal therapy. miRNAs were isolated, and expression patterns of 754 miRNAs were assessed with a quantitative miRNA PCR array. miRNAs expression profiles were compared between CP and AP groups. RESULTS There were no differences observed in the miRNAs expression profiles between CP and AP (p > 0.05). According to the microarray analyses, the most expressed miRNAs in both groups were hsa-miR-1274b, hsa-let-7b-5p, hsa-miR-24-3p, hsa-miR-19b-3p, hsa-miR-720, hsa-miR-126-3p, hsa-miR-17-3p and hsa-miR-21-3p. CONCLUSION Findings suggested no differences in miRNAs expression profiles between chronic and aggressive forms of periodontitis. The overexpression of specific miRNAs could provide insights into the pathogenesis of both forms of the disease.
Value-Added Tax Dilemma and Governance Path Analysis of Highway Transport Industry in China

As a basic industry of the national economy, the highway transport industry is one of the first batches of pilot industries in the taxation reform of the "replacing business tax with value-added tax (RBTVAT)". One of the bottlenecks in the orderly development of the tax system and the healthy development of the industry is the problems occurred in the value-added tax (VAT) implementation process, such as tax credit deficiency, falsely making out invoice for VAT, and serious national tax revenue losses. This paper studies the VAT implementation mechanism of the scientific design, effective management of "Internet plus taxation" and scientific construction of VAT tax service system, providing new ideas for the tax dilemma and governance path analysis of highway transport industry. The results of this paper also have important reference value for the RBTVAT governance in other industries.
Glutathione transferase modulates acute ethanol‐induced sedation in Drosophila neurones

Heavy alcohol consumption leads to neuropathological damage and alcohol use disorder, which affects the health of people and results in a cost burden. However, the genes modulating sensitivity to ethanol remain largely unknown. Here, we identified a novel gene, Drosophila glutathione transferase omega 1 (GstO1), which plays a critical role in regulating sensitivity to ethanol sedation. GstO1 mutant flies showed highly increased ethanol sensitivity. Furthermore, the expression level of GstO1 regulates the behavioural response to ethanol, because decreasing and increasing GstO1 affects sedation sensitivity in a contrasting manner. In addition, the RNA interference‐mediated knockdown of GstO1 expression reveals that GstO1 mediates sensitivity to ethanol sedation in neurones, including dopaminergic and serotonergic neurones. Altogether, our findings provide the first evidence for the involvement of glutathione transferase in the response to alcohol in Drosophila and provide a novel mechanistic insight into the toxicity and sensitivity of ethanol exposure.