Miracle-dz/UniFAR-FTUs huggingface dataset:
International Union of Basic and Clinical Pharmacology. CVI: GABAA Receptor Subtype- and Function-selective Ligands: Key Issues in Translation to Humans

GABAA receptors are the major inhibitory transmitter receptors in the brain. They are ligand-gated chloride channels and the site of action of benzodiazepines, barbiturates, neuroactive steroids, anesthetics, and convulsants. GABAA receptors are composed of five subunits that can belong to different subunit classes. The existence of 19 homologous subunits and their distinct regional, cellular, and subcellular distribution gives rise to a large number of GABAA receptor subtypes with distinct pharmacology, which modulate different functions of the brain. A variety of compounds have been identified that were claimed to modulate selectively individual GABAA receptor subtypes. However, many of these compounds have only incompletely been investigated or, in addition to a preferential modulation of a receptor subtype, also modulate other subtypes at similar concentrations. Although their differential efficacy at distinct receptor subtypes reduced side effects in behavioral experiments in rodents, the exact receptor subtypes mediating their behavioral effects cannot be unequivocally delineated. In addition, the discrepant in vivo effects of some of these compounds in rodents and man raised doubts on the applicability of the concept of receptor subtype selectivity as a guide for the development of clinically useful drugs. Here, we provide an up-to-date review on the currently available GABAA receptor subtype-selective ligands. We present data on their actual activity at GABAA receptor subtypes, discuss the translational aspect of subtype-selective drugs, and make proposals for the future development of ligands with better anxioselectivity in humans. Finally, we discuss possible ways to strengthen the conclusions of behavioral studies with the currently available drugs.
Using citation-context to reduce topic drifting on pure citation-based recommendation

Recent works in the area of academic recommender systems have demonstrated the effectiveness of co-citation and citation closeness in related-document recommendations. However, documents recommended from such systems may drift away from the main theme of the query document. In this work, we investigate whether incorporating the textual information in close proximity to a citation as well as the citation position could reduce such drifting and further increase the performance of the recommender system. To investigate this, we run experiments with several recommendation methods on a newly created and now publicly available dataset containing 53 million unique citation-based records. We then conduct a user-based evaluation with domain-knowledgeable participants. Our results show that a new method based on the combination of Citation Proximity Analysis (CPA), topic modelling and word embeddings achieves more than 20% improvement in Normalised Discounted Cumulative Gain (nDCG) compared to CPA.
Representation of the QM Subsystem for Long-Range Electrostatic Interaction in Non-Periodic Ab Initio QM/MM Calculations

In QM/MM calculations, it is essential to handle electrostatic interactions between the QM and MM subsystems accurately and efficiently. To achieve maximal efficiency, it is convenient to adopt a hybrid scheme, where the QM electron density is used explicitly in the evaluation of short-range QM/MM electrostatic interactions, while a multipolar representation for the QM electron density is employed to account for the long-range QM/MM electrostatic interactions. In order to avoid energy discontinuity at the cutoffs, which separate the short- and long-range QM/MM electrostatic interactions, a switching function should be utilized to ensure a smooth potential energy surface. In this study, we benchmarked the accuracy of such hybrid embedding schemes for QM/MM electrostatic interactions using different multipolar representations, switching functions and cutoff distances. For test systems (neutral and anionic oxyluciferin in MM (aqueous and enzyme) environments), the best accuracy was acquired with a combination of QM electrostatic potential (ESP) charges and dipoles and two switching functions (long-range electrostatic corrections (LREC) and Switch) in the treatment of long-range QM/MM electrostatics. It allowed us to apply a 10Å distance cutoff and still obtain QM/MM electrostatics/polarization energies within 0.1 kcal/mol and time-dependent density functional theory (TDDFT)/MM vertical excitation energies within 10−3 eV from theoretical reference values.
Hyperspectral Image Classification With Stacking Spectral Patches and Convolutional Neural Networks

Jointly combining the spatial and spectral features has proved to dramatically improve the performance of classifying hyperspectral image. Recently, utilizing neural networks to automatically model the spatial-spectral feature representations for hyperspectral images has become of great interest. This paper proposes a simple but innovative framework to classify hyperspectral image with two shallow convolutional neural networks (CNNs). First, principal component analysis whitening is applied to decorrelate hundreds of spectral bands. Instead of selecting the principal components to reduce the spectral dimensionality, we retain all the spectral bands but compress the image cuboid into a one-channel spectral quilt by stacking spectral patches. In this way, not only all the spectral information is retained but also the computational complexity of training a neural network is reduced compared with conventional networks that directly input the spectral volumes. Moreover, the spectral quilt will contain some novel textural patterns that are effective at distinguishing classes. Two shallow CNNs are then applied to classify the spectral quilts. As shown in the experiments, both networks can outperform the standard analysis methods.
A Quantum-Based Microwave Magnetic Field Sensor

In this paper, a quantum-based method for measuring the microwave magnetic field in free space is presented by exploring atomic Rabi resonance in the clock transition of 133Cs. A compact cesium glass cell serving as the microwave magnetic field sensing head was used to measure the spatial distribution of microwave radiation from an open-ended waveguide antenna. The measured microwave magnetic field was not restricted by other microwave devices. The longitudinal distribution of the magnetic field was measured. The experimental results measured by the sensor were in agreement with the simulation. In addition, a slightly electromagnetic perturbation caused by the glass cell was investigated through simulation calculations.
Text Morphing

In this paper, we introduce a novel natural language generation task, termed as text morphing, which targets at generating the intermediate sentences that are fluency and smooth with the two input sentences. We propose the Morphing Networks consisting of the editing vector generation networks and the sentence editing networks which are trained jointly. Specifically, the editing vectors are generated with a recurrent neural networks model from the lexical gap between the source sentence and the target sentence. Then the sentence editing networks iteratively generate new sentences with the current editing vector and the sentence generated in the previous step. We conduct experiments with 10 million text morphing sequences which are extracted from the Yelp review dataset. Experiment results show that the proposed method outperforms baselines on the text morphing task. We also discuss directions and opportunities for future research of text morphing.
High-Speed, Low-Power, and Highly Reliable Frequency Multiplier for DLL-Based Clock Generator

A low power and high-speed and highly reliable frequency multiplier for a delay-locked loop-based clock generator is proposed to produce a multiplied clock for different range of frequencies. The high-speed operation and low power consumption can be achieved using modified edge combiner. The proposed frequency multiplier is reducing the delay difference between positive- and negative-edge generation paths and also overcome the deterministic jitter problem. The proposed frequency multiplier is implemented in a 0.13-μm CMOS technology process and achieves power to a frequency ratio of 2.9 μW /MHz, and has the multiplication ratios of 16, and as an output range of 100 MHz-3.3 GHz.
Ginsenoside Rg3 and Rh2 protect trimethyltin‐induced neurotoxicity via prevention on neuronal apoptosis and neuroinflammation

The acute exposure of trimethyltin (TMT) develops clinical syndrome characterized by amnesia, aggressive behavior, and complex seizures. This neurotoxicant selectively induces hippocampal neuronal injury and glial activation accompanied with resultant neuroinflammation. Here we report two candidates ginsenosides Rg3 and Rh2 as neuroprotection agents using a mouse model of TMT intoxication via a single injection (2 mg/kg) and primary neuronal culture systems. Four‐week administration of Rg3 or Rh2 significantly reduced TMT‐induced seizures and behavioral changes. Rg3 and Rh2 significantly attenuated the oxidative stress evidenced by improvement on antioxidant enzymes and neuronal loss and astrocytic activation in mouse brain. In primary cultures, TMT induced significant neuronal death after 24‐h intoxication and vigorous secretion of inflammatory cytokines (IL‐1α/β, IL‐6, TNF‐α, and MCP‐1) in astrocytes. Pretreatment with Rg3 or Rh2 not only reduced cell death but efficiently suppressed above mentioned inflammatory cytokines confirmed by antibody array test. The underlying protective mechanism by Rg3 and Rh2 was delineated through selective upregulation of PI3K/Akt and suppression of ERK activation. Intriguingly, Rg3 and Rh2 protected oligodendrocyte progenitor cells (O‐2A) from TMT intoxication via promoting type 2 astrocytic differentiation without further inflammatory activation. Collectively, Rg3 and Rh2 interventions aimed at reducing oxidative stress and neuroinflammation neurotoxicity therefore are of therapeutic benefit in TMT‐induced neurodegeneration.
Design and Analysis of Microstrip Patch Antenna and Antenna Array for Vehicular Communication System

This paper presents the design and performance analysis of a rectangular microstrip patch antenna and its array in E and H plane. The antenna is designed at a centre frequency of 5.9GHz for Wi-Fi applications. The simulated return loss, directivity, side lobe level and radiation efficiency of simple patch has been achieved as −26.91dB, 6.862dB, −20.3dB and 77% respectively. An Improved directivity up to 9.6dB and 9.308dB with better matching also has been achieved in case of E and H plane array. Simulated result has been listed and comparative analysis has been done which reveals the fact that with help of array performance characteristics of antenna can be easily enhanced. The substrate used is RO4350 (lossy) whose $\boldsymbol{\varepsilon}_{\boldsymbol{r}}=3.66$ and low loss tangent of 0.004. The corporate feeding technique has been used for array design.
Policy, prevention, and regulation for Internet Gaming Disorder

In this commentary, I discuss the recent paper by Király et al. (2018), which provides a systematic review of current and potential policies addressing problematic gaming and suggesting current approaches include those (a) limiting video game availability, (b) reducing risk and harm, and (c) supporting gamers. This commentary uses a number of points raised by Király et al. (2018) to address the issue of policy context by discussing (a) the sociocultural environment and (b) the gamer and the game environment to (c) create the case for prevention to reduce risk and harm and to provide support for gamers and their families.
Resource Management in Fog/Edge Computing

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.
On Leader–Follower Consensus With Switching Topologies: An Analysis Inspired by Pigeon Hierarchies

Inspired by the interesting findings regarding the hierarchy and coordination of pigeons in Nagy et al. [Nature, 2010], we revisit the discrete-time leader–follower consensus problem with switching topologies. The purpose of this paper is to investigate the impact of hierarchical topologies and followers’ self-loops on the convergence performance of leader–follower consensus, including the convergence rate and robustness to switching topologies. We first study the fixed topology case, and show that the followers converge to the leader's state in finite time if and only if each follower has no self-loops and the topology is hierarchical. However, we show via counterexample, that leader–follower consensus may not be achieved when some followers have no self-loops and the topology is switching; even if each interaction graph has a spanning tree rooted at the leader. With the aid of binary relation theory, we further develop a new approach to present a novel sufficient condition for leader–follower consensus with switching topologies and with some followers having no self-loops. We prove that when no followers have self-loops and the same hierarchical organization is kept under the switching topologies, then the fastest rate of convergence in leader–follower consensus can be achieved; even in the presence of complex dynamic topologies. This is consistent with the natural phenomena found in pigeons by Nagy et al.
Optimal Adaptive and Accelerated Stochastic Gradient Descent

Stochastic gradient descent (\textsc{Sgd}) methods are the most powerful optimization tools in training machine learning and deep learning models. Moreover, acceleration (a.k.a. momentum) methods and diagonal scaling (a.k.a. adaptive gradient) methods are the two main techniques to improve the slow convergence of \textsc{Sgd}. While empirical studies have demonstrated potential advantages of combining these two techniques, it remains unknown whether these methods can achieve the optimal rate of convergence for stochastic optimization. In this paper, we present a new class of adaptive and accelerated stochastic gradient descent methods and show that they exhibit the optimal sampling and iteration complexity for stochastic optimization. More specifically, we show that diagonal scaling, initially designed to improve vanilla stochastic gradient, can be incorporated into accelerated stochastic gradient descent to achieve the optimal rate of convergence for smooth stochastic optimization. We also show that momentum, apart from being known to speed up the convergence rate of deterministic optimization, also provides us new ways of designing non-uniform and aggressive moving average schemes in stochastic optimization. Finally, we present some heuristics that help to implement adaptive accelerated stochastic gradient descent methods and to further improve their practical performance for machine learning and deep learning.
Modified SIW H-Plane Horn Antenna with Improved Gain Using Thin Substrate

This paper presents a new probe feed, high gain and compact Substrate Integrated Waveguide (SIW) H-plane horn antenna, in which both TE10 and TE30 modes of the structure are simultaneously excited leading to a uniform field distribution along radiating aperture. Also, to reduce Side Lobe Level (SLL) of the antenna and to enhance gain, a dielectric slab and a row of patches are placed at the front of the aperture. The proposed SIW horn antenna is numerically investigated using High Frequency Structure Simulator (HFSS) software. The simulation results of the proposed antenna including reflection coefficient, radiation patterns and antenna gain are reported. Results illustrate that the new structure provides gain of 9.5 dBi at the center frequency of 20 GHz, which is nearly 3 dB higher than that of the conventional SIW H-plane horn antenna.
GPU Parallelization of Physical Optics on Parametric Surfaces-AMA TLAB-Based Implementation

This paper presents a MATLAB-based approach for GPU parallelization of the Physical Optics method on parametric surfaces. Physical optics, which is widely used for computing the scattered field from electrically large objects, involves an intensive computational burden. However, illumination test and scattered field calculations can be performed independently. Thus, the calculations can be implemented on Graphical Processing Unit which makes it possible to solve the problem in parallel. Emphasis is placed on efficient MATLAB implementation over parametric surfaces. To show the computational efficiency improvement, the Radar Cross Section of a cylindrical object with conic nose as well as the radiation pattern of a reflector antenna are calculated. The numerical results show speedup up to 200 times in comparison with usual implementation on CPU.
Celer Network: Bring Internet Scale to Every Blockchain

Off-chain scaling techniques allow mutually distrustful parties to execute a contract locally among themselves instead of on the global blockchain. Parties involved in the transaction maintain a multi-signature fraud-proof off-chain replicated state machine, and only resort to on-chain consensus when absolutely necessary (e.g., when two parties disagree on a state). Off-chain scaling is the only way to support fully scale-out decentralized applications ("dApps") with better privacy and no compromise on the trust and decentralization guarantees. It is the inflection point for blockchain mass adoption, and will be the engine behind all scalable dApps. Celer Network is an Internet-scale, trust-free, and privacy-preserving platform where everyone can quickly build, operate, and use highly scalable dApps. It is not a standalone blockchain but a networked system running on top of existing and future blockchains. It provides unprecedented performance and flexibility through innovation in off-chain scaling techniques and incentive-aligned cryptoeconomics. Celer Network embraces a layered architecture with clean abstractions that enable rapid evolution of each individual component, including a generalized state channel and sidechain suite that supports fast and generic off-chain state transitions; a provably optimal value transfer routing mechanism that achieves an order of magnitude higher throughput compared to state-of-the-art solutions; a powerful development framework and runtime for off-chain applications; and a new cryptoeconomic model that provides network effect, stable liquidity, and high availability for the off-chain ecosystem.
Memory for neutral, emotional and trauma-related information in sexual abuse survivors

ABSTRACT Previous studies have shown that trauma-exposed individuals, including survivors of sexual abuse, show inferior performance in episodic memory tasks compared to non-exposed controls. This, however, has mainly been tested using neutral content. Our goal in this study was to determine whether this relative impairment in episodic memory extends to generally emotional and trauma-related content. Twenty-seven sexual abuse survivors and 27 control women participated in the study. They listened to stories with three content types (neutral, generally emotional and trauma-related) and performed a free-recall task immediately and 30 minutes later. Sexual abuse survivors showed poorer recall of neutral material compared to control participants. Lower recall was also observed for generally emotional content. However, importantly, there was no difference between groups in the recall of trauma-related content. The main novel contribution of this study is the demonstration that verbal episodic memory is not impaired for non-autobiographical trauma-related content in sexual abuse survivors. We discuss how this could be explained by personal relevance and attentional capture.
Anti-Aliasing and Data Compressing Technique for Time-Stretch Imaging System

Time-stretch imaging is a novel ultrafast imaging technique, which is based on space-to-time-to-wavelength mapping. We proposed an anti-aliasing technique based on optical time-division multiplexing. With the anti-aliasing technique, we can acquire a large amount of image information by using relatively low-sampling-rate electronic digitizer. Numerical analysis shows that the image quality can be improved by 4.16 dB comparing to that without using the anti-aliasing technique and at least 2.3 dB comparing to those digital signal processing techniques such as bilinear, bicubic and nearest-neighbor interpolation and Lanczos resampling techniques. In addition, we experimentally demonstrated a line-scan imaging system, which has the scan rate of 38.88 MHz. Defects and scratches on the object that were not identifiable originally can be clearly distinguished after using the proposed anti-aliasing technique.
Physician and Pharmacist Medication Decision-Making in the Time of Electronic Health Records: Mixed-Methods Study

Background Primary care needs to be patient-centered, integrated, and interprofessional to help patients with complex needs manage the burden of medication-related problems. Considering the growing problem of polypharmacy, increasing attention has been paid to how and when medication-related decisions should be coordinated across multidisciplinary care teams. Improved knowledge on how integrated electronic health records (EHRs) can support interprofessional shared decision-making for medication therapy management is necessary to continue improving patient care. Objective The objective of our study was to examine how physicians and pharmacists understand and communicate patient-focused medication information with each other and how this knowledge can influence the design of EHRs. Methods This study is part of a broader cross-Canada study between patients and health care providers around how medication-related decisions are made and communicated. We visited community pharmacies, team-based primary care clinics, and independent-practice family physician clinics throughout Ontario, Nova Scotia, Alberta, and Quebec. Research assistants conducted semistructured interviews with physicians and pharmacists. A modified version of the Multidisciplinary Framework Method was used to analyze the data. Results We collected data from 19 pharmacies and 9 medical clinics and identified 6 main themes from 34 health care professionals. First, Interprofessional Shared Decision-Making was not occurring and clinicians made decisions based on their understanding of the patient. Physicians and pharmacists reported indirect Communication, incomplete Information specifically missing insight into indication and adherence, and misaligned Processes of Care that were further compounded by EHRs that are not designed to facilitate collaboration. Scope of Practice examined professional and workplace boundaries for pharmacists and physicians that were internally and externally imposed. Physicians decided on the degree of the Physician-Pharmacist Relationship, often predicated by colocation. Conclusions We observed limited communication and collaboration between primary care providers and pharmacists when managing medications. Pharmacists were missing key information around reason for use, and physicians required accurate information around adherence. EHRs are a potential tool to help clinicians communicate information to resolve this issue. EHRs need to be designed to facilitate interprofessional medication management so that pharmacists and physicians can move beyond task-based work toward a collaborative approach.