Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacokinetics, tissue distribution, bioavailability, and excretion of nuciferine, an alkaloid from lotus, in rats by LC/MS/MS

Abstract Objective: In order to characterize the pharmacokinetics, tissue distribution, bioavailability, and excretion of nuciferine, a reliable gradient LC/MS/MS-based method was developed and validated. Methods: Sprague-Dawley rats were intravenously injected with a bolus of nuciferine (0.2 mg/kg) and orally given a single dose of nuciferine (10.0 mg/kg). Blood samples were withdrawn via the ocular vein at specific times. Organs, including the liver, kidney, brain, lung, heart, and spleen, were collected at specific times after oral administration of 10.0 mg/kg nuciferine. The plasma and tissue samples were assayed by LC/MS/MS. Results: The results indicated that nuciferine had rapid distribution and poor absorption into systemic circulation. The value of absolute bioavailability was only 1.9 ± 0.8% after administration of 10.0 mg/kg nuciferine by oral and administration of 0.2 mg/kg nuciferine intravenously (IV) to rats. The AUC0→4 h values in tissues were in the order of kidney > lung > spleen > liver > brain > heart. The majority of excretion of nuciferine (50.7%) was excreted through kidneys with parent drug after oral administration without liver metabolism. Conclusion: This study may provide a meaningful basis for clinical application of such a bioactive compound of herbal medicines.
Low-Dispersion, High-Voltage, Low-Leakage GaN HEMTs on Native GaN Substrates

In this paper, the advantages of GaN high electron mobility transistors (HEMTs) grown on native GaN over GaN/Si or GaN/sapphire substrates are investigated and correlated with epitaxial material quality. Transmission electron microscopy plan-view and cross-sectional analyses of GaN/GaN reveal dislocation densities below <inline-formula> <tex-math notation="LaTeX">${1} \times {10}^{{6}}$ </tex-math></inline-formula> cm<sup>−2</sup>, which is at least three orders of magnitude lower than that of GaN/Si or GaN/sapphire. In the case of GaN/Si, the dislocations not only originate from the substrate/nucleation layer interface, but also the strain relief and isolation buffer stacks are main contributors to the dislocation density. GaN/GaN HEMTs show superior electrical and thermal performance and feature three orders of magnitude lower OFF-state leakage. The current collapse (also referred to as current dispersion or <inline-formula> <tex-math notation="LaTeX">$R_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>-increase) after stress bias is less than 15% compared with 50% in the case of GaN/Si. A 2% drop of the ON-state current due to self-heating in dc operation when compared with 13% and 16% for GaN/Si and GaN/sapphire, respectively. The GaN/Si thermal performance becomes comparable to that of GaN/GaN only after substrate removal. Therefore, GaN/GaN provides high ON-state current, low OFF-state leakage current, minimal current collapse, and enhanced thermal power dissipation capability at the same time, which can directly be correlated with the absence of high dislocation density.
Titanium dioxide (TiO2) and silver chloride (AgCl)-based pH sensor using spin coating method

Development of thick film-based sensor using new material alternatives has not experienced significant progress, in pH sensor especially. One of the reasons is the lack of research in the field of electrochemical sensors in manufacturing pH with new materials. This research aims to develop the performance of thick film (microfabrication)-based pH sensor with spin coating method. The materials used in microfabrication included AgCl as a reference electrode, TiO2 as working electrode and Al2O3 as substrate. Sensor was fabricated using three designs with the electrode distance of 1.5, 1 and 0.5 mm. Experiment were carried out in room temperature with the observed parameters of voltage and current output of the pH sensor. These two parameters were used to calculate the sensitivity sensor. The results show that the electrode distance of 1.5, 1 and 0.5 mm revealed the sensitivity of −40.586 mV/pH, −45.229 mV/pH and −29.486 mV/pH respectively.
Efficient and Robust Image Coding and Transmission Based on Scrambled Block Compressive Sensing

Image transmission in a wireless visual sensor network (WVSN) with limited resources over an unreliable and bandwidth-limited wireless channel is challenging. This paper presents a highly efficient and robust image coding and transmission scheme with a simple encoder based on compressive sensing (CS) for WVSNs. First, an image measurement based on scrambled block compressive sampling with a separable sensing operator is proposed to simplify the encoder. Second, a progressive nonuniform quantization, which exploits the measurement distribution at the encoder side and the measurement dependencies at the decoder side, is designed to improve the rate-distortion (R-D) performance while maintaining low complexity at the encoder. Third, to further improve the R-D performance, a progressive non-local low-rank reconstruction is designed at the decoder. The experimental results show that the proposed scheme can achieve higher R-D performance compared with the benchmark CS-based image coding and transmission schemes. Higher robustness can be achieved compared with the traditional source-channel coding, such as Consultative Committee for Space Data Systems$-$Image Data Compression (CCSDS-IDC) with Raptor codes under a time-varying packet loss channel, and the encoding time can be significantly reduced compared with the traditional image coding schemes. The experimental results also show that the proposed scheme achieves state-of-the-art coding efficiency with lower computational complexity at the encoder while still supporting error resilience.
Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter

An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2% and an absolute maximum error of 6% in case of 0°C. In the other case (25°C and 50°C) it is less than 2%.
Robust WLAN-Based Indoor Intrusion Detection Using PHY Layer Information

Intrusion detection techniques are widely used to guarantee the security of people’s possessions. With the rapid development of wireless communication, device-free passive human detection based on wireless techniques may have more opportunities in intrusion detection. WiFi has been widely deployed in both public and private areas, which can be used as generalized sensors to detect human motion beyond communication. As a result, there have been several researches on WLAN-based motion detection. However, the detection accuracy of previous approaches declines significantly when people’s moving speed becomes very slow. In this paper, we explore a novel method which has a relative stable detection performance under different moving speeds. We extract a novel feature representing the fluctuation of the whole channel from channel state information at the physical layer of 802.11n wireless networks, and utilize a probability technique to detect human motion. A hidden Markov model is leveraged as the classifier to make human detection a probability problem. We implement the system using off-the-shelf WiFi devices and evaluate it in two scenarios. As indicated in the evaluation results, our approach is an appropriate method for intrusion detection.
Individual responses of rodents in modelling of affective disorders and in their treatment: prospective review

Abstract Introduction Lack of good animal models for affective disorders, including major depression and bipolar disorder, is noted as a major bottleneck in attempts to study these disorders and develop better treatments. We suggest that an important approach that can help in the development and use of better models is attention to variability between model animals. Results Differences between mice strains were studied for some decades now, and sex differences get more attention than in the past. It is suggested that one factor that is mostly neglected, individual variability within groups, should get much more attention. The importance of individual differences in behavioral biology and ecology was repeatedly mentioned but its application to models of affective illness or to the study of drug response was not heavily studied. The standard approach is to overcome variability by standardization and by increasing the number of animals per group. Conclusions Possibly, the individuality of specific animals and their unique responses to a variety of stimuli and drugs, can be helpful in deciphering the underlying biology of affective behaviors as well as offer better prediction of drug responses in patients.
Numerical simulations in particle physics

Numerical simulations have become an important tool to understand and predict nonperturbative phenomena in particle physics. We attempt to present a general overview over the field. First, the basic concepts of lattice gauge theories are described, including a discussion of currently used algorithms and the reconstruction of continuum physics from lattice data. We then proceed to present some results for QCD, both at low energies and at high temperatures, as well as for the electro-weak sector of the standard model.
The Effects of Sleep Extension on Sleep, Performance, Immunity and Physical Stress in Rugby Players

(1) Background: The purpose of the present study was to examine the efficacy of sleep extension in professional rugby players. The aims were to: (i) characterise sleep quantity in elite rugby players and determine changes in immune function and stress hormone secretion during a pre-season training programme; (ii) evaluate the efficacy of a sleep extension intervention in improving sleep, markers of physical stress, immune function and performance. (2) Methods: Twenty five highly trained athletes from a professional rugby team (age (mean ± SD) 25 ± 2.7 years; height 1.87 ± 0.07 m; weight 105 ± 12.1 kg) participated in a six week pre-post control-trial intervention study. Variables of sleep, immune function, sympathetic nervous activity, physiological stress and reaction times were measured. (3) Results: Sleep extension resulted in a moderate improvement in sleep quality scores ([mean; ± 90% confidence limits] −24.8%; ± 54.1%) and small to moderate increases in total sleep time (6.3%; ± 6.3%) and time in bed (7.3%; ± 3.6%). In addition, a small decrease in cortisol (−18.7%; ± 26.4%) and mean reaction times (−4.3%; ± 3.1%) was observed following the intervention, compared to the control. (4) Conclusions: Professional rugby players are at risk of poor sleep during pre-season training, with concomitant rises in physical stress. Implementing a sleep extension programme among professional athletes is recommended to improve sleep, with beneficial changes in stress hormone expression and reaction time performance.
Real-time object recognition algorithm based on deep convolutional neural network

Radar detection of moving objects is vulnerable to external environment. By introducing the object confirmation algorithm, the intelligent radar perimeter security system's false alarm rate can be reduced significantly. The object confirmation algorithm is essentially an object detection algorithm. Due to the poor generalization of artificial feature extraction algorithms, we use the deep convolution neural network to extract deep features automatically for object confirmation. In order to meet the real-time requirement in engineering practices, our algorithm use the YOLOv2 system as a basis, and selects anchor boxes which meet object scales of our training data set by k-means++ clustering. To improve the YOLOv2 network structure, low-layer deep features which denote the texture information and high-layer deep features which denote the semantic information are combined layer by layer to make object detection more accurate. The experimental results show that the false alarm rate of the intelligent radar perimeter security system is further reduced by introducing the object confirmation algorithm. Especially for extreme weather, false alarms of radar detection greatly increase. But most of them are eliminated after running object confirmation algorithm. Therefore the warning accuracy of the entire system can be guaranteed. The detection speed of the object confirmation algorithm is 33FPS, which meets the real-time requirement of engineering practices.
Successive Interference Cancellation for LDPC Coded Nonorthogonal Multiple Access Systems

We design, for the first time, novel successive interference cancellation (SIC) schemes for a two-user nonorthogonal multiple access system using low-density parity-check codes. We first develop a low-complexity SIC scheme treating interference as noise, referred to as the N-SIC scheme, and a SIC with joint detection scheme treating the modulation of interference signal at the near user as side information, referred to as the J-SIC scheme. We then propose a new extrinsic information assisted SIC scheme, referred to as the E-SIC scheme, which exchanges the extrinsic decoding information of the signals for two users and thus incurs an increase complexity. With simulations, we demonstrate that the error performance advantage of the E-SIC scheme over the N-SIC and J-SIC schemes is more significant when the power allocated to the near user is larger, e.g., higher than 45%. When this power becomes smaller, e.g., less than 10%, the low-complexity N-SIC scheme can achieve the best error performance. In addition, we have an important finding that when the two users adopt different order modulation, the decoding convergence speed of the E-SIC scheme improves when the difference between the constellation sizes of two users is larger.
Micromagnetic Calculation for Magnetic Tunnel Junctions With Equal Parallel and Antiparallel Thermal Stability

In magnetic tunnel junctions (MTJs) with perpendicular magnetic easy axes, the retention times for parallel and antiparallel states are different because of dipolar coupling with the reference layer (RL). To make retention times symmetrical, we model three types of MTJs that have single or synthetic ferrimagnetic (SyF) RLs with stepped structures. Micromagnetic calculations are performed on the basis of the Landau–Lifshitz–Gilbert equation of motion to evaluate the stray fields from the RLs of each design. A SyF structure with a step on the bottom magnetic layer has the smallest stray field of 2 mT at lateral dimensions of 30 nm × 30 nm in the layer. Symmetric thermal stability for parallel and antiparallel states should help to realize high-density nonvolatile magnetic random-access memory and logic chips.
Optimization of Handover Parameters for LTE/LTE-A in-Building Systems

The optimization of handover (HO) parameters for in-building systems is investigated in this paper. We proposed a novel methodology that provides in-building base stations with the flexibility to customize HO parameters to specific radio frequency conditions at the cell-edge for different loading scenarios. We propose the use of machine learning and data mining techniques to allow the base stations to autonomously learn and identify characteristic patterns in the received signal strength values (reported by users during the HO process), and apply optimal HO parameters for each case. Our optimization strategy jointly considers the radio frequency conditions at the cell-edge and the load levels of the base stations, to determine optimal HO parameters that maximize the quality of service and guarantee the continuity of service at the cell-edge. We evaluated our methodology with experimental data collected from two fully operational LTE in-building systems deployed in a university campus. Our results show that with our methodology the spectral efficiency at the cell-edge can be greatly improved. Downlink data rate gains at the cell-edge reached a value close to 150% for a certain loading scenario compared to the traditional approach of selecting a unique set of HO parameters for the entire in-building system.
Text sentiment analysis based on multi-granularity joint solution

With the increase of the number of comments on major websites, how to distinguish the emotional tendencies of these comments has become the focus of researches. Through the analyzing and studying of these comments, a sentiment analysis method based on multi-granularity joint solution is proposed in this paper. In order to find the accurate evaluating objects, obtaining the emotional vocabularies and mining the relative evaluating objects hiding behind these vocabularies have been done at first; then a multi-granularity joint solution model is built; Furthermore, the scores of each comments are given by this model. Finally, some experiments to test the validity and accuracy of our proposed method have been executed, the results show that the average accuracy rate has been improved by 4% compared with the traditional multi — granularity methods.
GeneMANIA update 2018

Abstract GeneMANIA (http://genemania.org) is a flexible user-friendly web site for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query gene list, GeneMANIA finds functionally similar genes using a wealth of genomics and proteomics data. In this mode, it weights each functional genomic dataset according to its predictive value for the query. Another use of GeneMANIA is gene function prediction. Given a single query gene, GeneMANIA finds genes likely to share function with it based on their interactions with it. Enriched Gene Ontology categories among this set can point to the function of the gene. Nine organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Escherichia coli, Homo sapiens, Mus musculus, Rattus norvegicus and Saccharomyces cerevisiae). Hundreds of data sets and hundreds of millions of interactions have been collected from GEO, BioGRID, IRefIndex and I2D, as well as organism-specific functional genomics data sets. Users can customize their search by selecting specific data sets to query and by uploading their own data sets to analyze. We have recently updated the user interface to GeneMANIA to make it more intuitive and make more efficient use of visual space. GeneMANIA can now be used effectively on a variety of devices.
Visual analytics for radiomics: Combining medical imaging with patient data for clinical research

The visualization and analysis of electronic health records (EHRs) are becoming increasingly relevant for clinical researchers. While the digitization of medical images is general practice today, many clinics are just starting to build up database with the related patient data, patient histories, and treatment outcomes. This paper reports on a project with a medical group of ear, nose, and throat (ENT) specialists. It combines medical image analysis and Radiomics with visual analytics of patient data to build, analyze, and evaluate patient cohorts. The combined visual interface for both browsing and analyzing patient data was developed in collaboration with the medical researchers. In addition to offering a new way of cohort building, our approach also provides a first comprehensive view on the EHR, including the relevant anatomy of patients. This project triggered a new effort to extend the digitized patient database from around 100 patients to the entire patient population at our partner's clinic.
A study of small complicated axisymmetric parts manufacturing in industry 4.0

The Industry 4.0 is regard as the fourth industrial revolution which is geared to the demand of customization products. As one kind of basic components in industry, the small complicated axisymmetric parts are usually low-volume customization products. So it is significant to explore their manufacturing process in the Industry 4.0 era. The production procedure of a representative 4 stages cone has been studied. First, requirements of customers are sent to the industrial internet. Then technology suppliers will design an appropriate forming process with the CAD (Computer-Aided Design), CAE (Computer-Aided Engineering), CAPP (Computer-Aided Process Planning), CAM (Computer-Aided Manufacturing) and other Industry 4.0 technologies. Finally, distributed smart factories will organize production and send target products to customers depending on the industrial internet. Based on this study, it can be seen that the metal spinning method is suitable for the small complicated axisymmetric parts producing in Industry 4.0. The remote customization manufacturing will be more popularize. Even, the CAD, CAE, CAPP and CAM method will play more important roles in manufacture with the help of industrial internet.
Research of Security as a Service for VMs in IaaS Platform

With the rapid promotion and application of cloud computing technology in various fields, cloud computing security has become the focus of attention. To satisfy the virtual machine (VM) security requirements of communication access control, network anomaly detection, memory monitoring, and file antivirus in Infrastructure as a Service (IaaS) platform, a comprehensive protection framework with the capacity of defense-in-depth for tenant VMs was proposed in this paper, which employed three different layers to satisfy above security requirements of tenant business from the outside to the inside of the VM. At the first layer, a tenant domain model was abstracted and realized based on software defined networking (SDN), which was used to re-obtain the capacity for communication access control for VM traffic and ensure security isolation of different tenant business networks. Besides, to detect the network abnormality of tenant VMs, a traffic structure stability model was constructed according to the deviation degree between current and historical normal network traffic structure profile. At the second layer, the capacities of network access control and anomaly detection, the same as the capacities used in the first layer, which were provided based on VM granularity. At the third layer, to monitor the VM memory information, a VM security monitoring method with agentless based on online analysis of VM memory was proposed by employing physical memory analysis mechanism. Moreover, a file antivirus method named HyperAV for VM based on virtualization was given, which was constructed of a frontend and a rear end. HyperAV optimized the process of virus scanning by monitoring the sector change information of a running VM with low performance costs. The experimental results demonstrated the effectiveness and low performance costs of the proposed protection framework and the corresponding security mechanisms, respectively.