Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge Transport in Trap-Sensitized Infrared PbS Quantum-Dot-Based Photoconductors: Pros and Cons

Control of quantum-dot (QD) surface chemistry offers a direct approach for the tuning of charge-carrier dynamics in photoconductors based on strongly coupled QD solids. We investigate the effects of altering the surface chemistry of PbS QDs in such QD solids via ligand exchange using 3-mercaptopropionic acid (MPA) and tetrabutylammonium iodide (TBAI). The roll-to-roll compatible doctor-blade technique was used for the fabrication of the QD solid films as the photoactive component in photoconductors and field-effect phototransistors. The ligand exchange of the QD solid film with MPA yields superior device performance with higher photosensitivity and detectivity, which is due to less dark current and lower noise level as compared to ligand exchange with TBAI. In both cases, the mechanism responsible for photoconductivity is related to trap sensitization of the QD solid, in which traps are responsible of high photoconductive gain values, but slow response times under very low incident optical power (<1 pW). At medium–high incident optical powers (>100 pW), where traps are filled, both MPA- and TBAI-treated photodevices exhibit similar behavior, characterized by lower responsivity and faster response time, as limited by the mobility in the QD solid.
Molecular Hypergraph Grammar with its Application to Molecular Optimization

Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a latent continuous vector (and its corresponding molecule) within a limited number of property evaluations. While the most recent work, for the first time, achieved 100% validity, its architecture is rather complex due to auxiliary neural networks other than VAE, making it difficult to train. This paper presents a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to achieve 100% validity. Our idea is to develop a graph grammar encoding the hard chemical constraints, called molecular hypergraph grammar (MHG), which guides VAE to always generate valid molecules. We also present an algorithm to construct MHG from a set of molecules.
Reduced monocyte and macrophage TNFSF15/TL1A expression is associated with susceptibility to inflammatory bowel disease

Chronic inflammation in inflammatory bowel disease (IBD) results from a breakdown of intestinal immune homeostasis and compromise of the intestinal barrier. Genome-wide association studies have identified over 200 genetic loci associated with risk for IBD, but the functional mechanisms of most of these genetic variants remain unknown. Polymorphisms at the TNFSF15 locus, which encodes the TNF superfamily cytokine commonly known as TL1A, are associated with susceptibility to IBD in multiple ethnic groups. In a wide variety of murine models of inflammation including models of IBD, TNFSF15 promotes immunopathology by signaling through its receptor DR3. Such evidence has led to the hypothesis that expression of this lymphocyte costimulatory cytokine increases risk for IBD. In contrast, here we show that the IBD-risk haplotype at TNFSF15 is associated with decreased expression of the gene by peripheral blood monocytes in both healthy volunteers and IBD patients. This association persists under various stimulation conditions at both the RNA and protein levels and is maintained after macrophage differentiation. Utilizing a “recall-by-genotype” bioresource for allele-specific expression measurements in a functional fine-mapping assay, we localize the polymorphism controlling TNFSF15 expression to the regulatory region upstream of the gene. Through a T cell costimulation assay, we demonstrate that genetically regulated TNFSF15 has functional relevance. These findings indicate that genetically enhanced expression of TNFSF15 in specific cell types may confer protection against the development of IBD.
Neuroimaging of Sleep Disturbances in Movement Disorders

Sleep dysfunction is recognized as a distinct clinical manifestation in movement disorders, often reported early on in the disease course. Excessive daytime sleepiness, rapid eye movement sleep behavior disorder and restless leg syndrome, amidst several others, are common sleep disturbances that often result in significant morbidity. In this article, we review the spectrum of sleep abnormalities across atypical Parkinsonian disorders including multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), as well as Parkinson's disease (PD) and Huntington's disease (HD). We also explore the current concepts on the neurobiological underpinnings of sleep disorders, including the role of dopaminergic and non-dopaminergic pathways, by evaluating the molecular, structural and functional neuroimaging evidence based on several novel techniques including magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), single-photon emission computed tomography (SPECT) and positron emission tomography (PET). Based on the current state of research, we suggest that neuroimaging is an invaluable tool for assessing structural and functional correlates of sleep disturbances, harboring the ability to shed light on the sleep problems attached to the limited treatment options available today. As our understanding of the pathophysiology of sleep and wake disruption heightens, novel therapeutic approaches are certain to transpire.
Ameliorative effect of selenium nanoparticles and fish oil on cisplatin and gamma irradiation-induced nephrotoxicity in male albino rats

Abstract Cisplatin (CP) is a major antineoplastic drug for the treatment of solid tumors, however, its clinical utility is limited by nephrotoxicity. Also, radiotherapy is an important treatment modality for many malignancies. The present studies were performed to test whether fish oil (FO) and/or selenium nanoparticles (SeNPs) administration have an ameliorative effect on CP and γ-irradiation induced nephrotoxicity. FO and/or SeNPs were administered to male albino rats daily for 12 days before being intraperitoneally injected with a single dose of CP (10 mg/kg body weight) and whole body exposed to a single dose of γ-radiation (0.7 Gy). Biochemical analysis and histopathological examination were performed. Pretreatment with FO and/or SeNPs before the administration of CP and exposure to γ-radiation significantly reduced CP- and γ-radiation-induced high levels of serum urea and creatinine and renal tumor necrosis factor-α, caspase-3 and cyclooxygenase-2, also they significantly prevented renal total antioxidant capacity levels decrease and ameliorated the levels of most studied trace elements. The histopathological results supported the biochemical findings of this study. The administration of FO and/or SeNPs might be useful for preventing nephrotoxicity which can be caused by CP and radiotherapy during the treatment of various malignancies.
CrowdExpress: A Probabilistic Framework for On-Time Crowdsourced Package Deliveries

Most of current urban logistic systems fail to strike a nice trade-off between speed and cost. An express logistic service often implies a high delivery cost. Crowdsourced logistics is a promising solution to alleviating such contradiction. In this article, we propose a new form of crowdsourced logistics that organizes passengers and packages in a shared room, i.e., using taxis that are already transporting passengers as package hitchhikers to achieve on-time deliveries. It is well-recognized that taxi drivers are good at delivering passengers to their destinations efficiently. As a result, the proposed new urban logistics system has potentials to lower the cost and accelerate package deliveries simultaneously. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express service. In the first phase, we mine the historical taxi GPS trajectory data offline to build the package transport network. In the second phase, we develop an online taxi scheduling algorithm to adaptively discover the path with the maximum arriving-on-time probability “on-the-fly” upon real-time passenger-sending requests, and direct the package routing accordingly. Finally, we evaluate the system using the real-world taxi data generated by over 19,000 taxis in a month in the city of New York, US. Results show that around 9,500 packages can be successfully delivered daily on time with the success rate over 94 percent.
Chemical Profiles and Simultaneous Quantification of Aurantii fructus by Use of HPLC-Q-TOF-MS Combined with GC-MS and HPLC Methods

Aurantii fructus (AF) is a traditional Chinese medicine that has been used to improve gastrointestinal motility disorders for over a thousand years, but there is no exhaustive identification of the basic chemical components and comprehensive quality control of this herb. In this study, high-performance liquid chromatography coupled with quadrupole time of flight mass spectrometry (HPLC-Q-TOF-MS) and gas chromatography coupled mass spectrometry (GC-MS) were employed to identify the basic chemical compounds, and high-performance liquid chromatography (HPLC) was developed to determine the major biochemical markers from AF extract. There were 104 compounds belonging to eight structure types, including 13 amino acids or peptides, seven alkaloids, 18 flavanones, 14 flavones, 15 polymethoxyflavonoids, six triterpenoids, nine coumarins, and 18 volatile oils, as well as four other compounds that were systematically identified as the basic components from AF, and among them, 41 compounds were reported for the first time. Twelve bioactive ingredients were chosen as the benchmark markers to evaluate the quality of AF. The analysis was completed with a gradient elution at a flow rate of 0.7 mL/min within 55 min. This efficient method was validated showing good linearity, precision, stability, repeatability and recovery. Furthermore, the method was successfully applied to the simultaneous determination of 12 chemical markers in different samples of AF. This study could be applied to the identification of multiple bioactive substances and improve the quality control of AF.
Elliptical Distributions-Based Weights-Determining Method for OWA Operators

The ordered weighted averaging (OWA) operators play a crucial role in aggregating multiple criteria evaluations into an overall assessment supporting the decision makers' choice. One key point steps is to determine the associated weights. In this paper, we first briefly review some main methods for determining the weights by using distribution functions. Then we propose a new approach for determining OWA weights by using the RIM quantifier. Motivated by the idea of normal distribution-based method to determine the OWA weights, we develop a method based on elliptical distributions for determining the OWA weights, and some of its desirable properties have been investigated.
Causes and prognosis of chronic intestinal pseudo-obstruction in 48 subjects

Abstract The aim of the study was to evaluate the prognosis and survival of pediatric subjects with chronic intestinal pseudo-obstruction (CIPO) and investigate the independent risk factors affecting their prognosis. This was a retrospective case series of all pediatric subjects suffering from CIPO and treated at the Pediatric Surgical ward of Xinhua Hospital between January 2006 and January 2016. The overall mortality was 19/48 (39.6%). Because of delayed CIPO diagnosis, many subjects underwent a variety of surgical procedures. The rate of additional surgical procedures was high (35/48, 72.9%), but the number of surgical procedures, parenteral nutrition, and megacystis did not affect mortality. Mycotic infection was significantly associated with mortality, while onset at <1 year and hypoganglionosis showed a tendency to be associated with mortality. Mycotic infection was associated with mortality of children with CIPO. Despite improving treatment approaches, the overall prognosis of CIPO remains poor. The choice of the surgical intervention could be based on standard criteria.
Dihydromyricetin Attenuates Myocardial Hypertrophy Induced by Transverse Aortic Constriction via Oxidative Stress Inhibition and SIRT3 Pathway Enhancement

Dihydromyricetin (DMY), one of the flavonoids in vine tea, exerts several pharmacological actions. However, it is not clear whether DMY has a protective effect on pressure overload-induced myocardial hypertrophy. In the present study, male C57BL/6 mice aging 8–10 weeks were subjected to transverse aortic constriction (TAC) surgery after 2 weeks of DMY (250 mg/kg/day) intragastric administration. DMY was given for another 2 weeks after surgery. Blood pressure, myocardial structure, cardiomyocyte cross-sectional area, cardiac function, and cardiac index were observed. The level of oxidative stress in the myocardium was assessed with dihydroethidium staining. Our results showed that DMY had no significant effect on the blood pressure. DMY decreased inter ventricular septum and left ventricular posterior wall thickness, relative wall thickness, cardiomyocyte cross-sectional areas, as well as cardiac index after TAC. DMY pretreatment also significantly reduced arterial natriuretic peptide (ANP), brain natriuretic peptide (BNP) mRNA and protein expressions, decreased reactive oxygen species production and malondialdehyde (MDA) level, while increased total antioxidant capacity (T-AOC), activity of superoxide dismutase (SOD), expression of sirtuin 3 (SIRT3), forkhead-box-protein 3a (FOXO3a) and SOD2, and SIRT3 activity in the myocardium of mice after TAC. Taken together, DMY ameliorated TAC induced myocardial hypertrophy in mice related to oxidative stress inhibition and SIRT3 pathway enhancement.
Genome-Wide DNA Methylation Comparison between Brassica napus Genic Male Sterile Line and Restorer Line

DNA methylation is an essential epigenetic modification that dynamically regulates gene expression during plant development. However, few studies have determined the DNA methylation profiles of male-sterile rapeseed. Here, we conducted a global comparison of DNA methylation patterns between the rapeseed genic male sterile line 7365A and its near-isogenic fertile line 7365B by whole-genome bisulfite sequencing (WGBS). Profiling of the genome-wide DNA methylation showed that the methylation level in floral buds was lower than that in leaves and roots. Besides, a total of 410 differentially methylated region-associated genes (DMGs) were identified in 7365A relative to 7365B. Traditional bisulfite sequencing polymerase chain reaction (PCR) was performed to validate the WGBS data. Eleven DMGs were found to be involved in anther and pollen development, which were analyzed by quantitative PCR. In particular, Bnams4 was hypo-methylated in 7365A, and its expression was up-regulated, which might affect other DMGs and thus control the male sterility. This study provided genome-wide DNA methylation profiles of floral buds and important clues for revealing the molecular mechanism of genic male sterility in rapeseed.
Learning Sequence Encoders for Temporal Knowledge Graph Completion

Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license.
Neuronal Response Latencies Encode First Odor Identity Information across Subjects

Odorants are coded in the primary olfactory processing centers by spatially and temporally distributed patterns of glomerular activity. Whereas the spatial distribution of odorant-induced responses is known to be conserved across individuals, the universality of its temporal structure is still debated. Via fast two-photon calcium imaging, we analyzed the early phase of neuronal responses in the form of the activity onset latencies in the antennal lobe projection neurons of honeybee foragers. We show that each odorant evokes a stimulus-specific response latency pattern across the glomerular coding space. Moreover, we investigate these early response features for the first time across animals, revealing that the order of glomerular firing onsets is conserved across individuals and allows them to reliably predict odorant identity, but not concentration. These results suggest that the neuronal response latencies provide the first available code for fast odor identification. SIGNIFICANCE STATEMENT Here, we studied early temporal coding in the primary olfactory processing centers of the honeybee brain by fast imaging of glomerular responses to different odorants across glomeruli and across individuals. Regarding the elusive role of rapid response dynamics in olfactory coding, we were able to clarify the following aspects: (1) the rank of glomerular activation is conserved across individuals, (2) its stimulus prediction accuracy is equal to that of the response amplitude code, and (3) it contains complementary information. Our findings suggest a substantial role of response latencies in odor identification, anticipating the static response amplitude code.
Empirical Vulnerability Analysis of Automated Smart Contracts Security Testing on Blockchains

The emerging blockchain technology supports decentralized computing paradigm shift and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of software, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-specific languages and its testability. There have been high-profile incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating financial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. This implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a specific goal to more readily realize the application of blockchain smart contracts in security and privacy, we should first understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-specific programming language, Solidity, to provide the first body of knowledge for creating more secure blockchain-based software.
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms.
BestSF

The Sparse Matrix-Vector Multiplication (SpMV) kernel dominates the computing cost in numerous scientific applications. Many implementations based on different sparse formats were proposed to improve this kernel on the recent GPU architectures. However, it has been widely observed that there is no “best-for-all” sparse format for the SpMV kernel on GPU. Indeed, serious performance degradation of an order of magnitude can be observed without a careful selection of the sparse format to use. To address this problem, we propose in this article BestSF (Best Sparse Format), a new learning-based sparse meta-format that automatically selects the most appropriate sparse format for a given input matrix. To do so, BestSF relies on a cost-sensitive classification system trained using Weighted Support Vector Machines (WSVMs) to predict the best sparse format for each input sparse matrix. Our experimental results on two different NVIDIA GPU architectures using a large number of real-world sparse matrices show that BestSF achieved a noticeable overall performance improvement over using a single sparse format. While BestSF is trained to select the best sparse format in terms of performance (GFLOPS), our further experimental investigations revealed that using BestSF also led, in most of the test cases, to the best energy efficiency (MFLOPS/W). To prove its practical effectiveness, we also evaluate the performance and energy efficiency improvement achieved when using BestSF as a building block in a GPU-based Preconditioned Conjugate Gradient (PCG) iterative solver.