Miracle-dz/UniFAR-FTUs huggingface dataset:
Dominant Neuropeptide Cotransmission in Kisspeptin-GABA Regulation of GnRH Neuron Firing Driving Ovulation

A population of kisspeptin-GABA coexpressing neurons located in the rostral periventricular area of the third ventricle (RP3V) is believed to activate gonadotropin-releasing hormone (GnRH) neurons to generate the luteinizing hormone (LH) surge triggering ovulation. Selective optogenetic activation of RP3V kisspeptin (RP3VKISS) neurons in female mice for >30 s and ≥10 Hz in either a continuous or bursting mode was found to reliably generate a delayed and long-lasting activation of GnRH neuron firing in brain slices. Optogenetic activation of RP3VKISS neurons in vivo at 10 Hz generated substantial increments in LH secretion of similar amplitude to the endogenous LH surge. Studies using GABAA receptor antagonists and optogenetic activation of RP3V GABA (RP3VGABA) neurons in vitro revealed that low-frequency (2 Hz) stimulation generated immediate and transient GABAA receptor-mediated increases in GnRH neuron firing, whereas higher frequencies (10 Hz) recruited the long-lasting activation observed following RP3VKISS neuron stimulation. In vivo, 2 Hz activation of RP3VGABA neurons did not alter LH secretion, whereas 10 Hz stimulation evoked a sustained large increase in LH identical to RP3VKISS neuron activation. Optogenetic activation of RP3VKISS neurons in which kisspeptin had been deleted did not alter LH secretion. These studies demonstrate the presence of parallel transmission streams from RP3V neurons to GnRH neurons that are frequency dependent and temporally distinct. This comprises a rapid and transient GABAA receptor-mediated activation and a slower onset kisspeptin-mediated stimulation of long duration. At the time of the LH surge, GABA release appears to be functionally redundant with the neuropeptide kisspeptin being the dominant cotransmitter influencing GnRH neuron output. SIGNIFICANCE STATEMENT Miscommunication between the brain and ovaries is thought to represent a major cause of infertility in humans. Studies in rodents suggest that a population of neurons located in the rostral periventricular area of the third ventricle (RP3V) are critical for activating the gonadotropin-releasing hormone (GnRH) neurons that trigger ovulation. The present study provides evidence that an RP3V neuron population coexpressing kisspeptin and GABA provides a functionally important excitatory input to GnRH neurons at the time of ovulation. This neural input releases GABA and/or kisspeptin in the classical frequency dependent and temporally distinct nature of amino acid-neuropeptide cotransmission. Unusually, however, the neuropeptide stream is found to be functionally dominant in activating GnRH neurons at the time of ovulation.
Electromagnetic computational analysis of double sided linear switched reluctance motor for reduction of force ripples

Double Sided Linear Switched Reluctance Motors (DSL-SRM) are being increasingly used in high force density and high precision control applications. This paper focuses on the reduction of force ripples for increasing the force density of the motor. In order to reduce the force ripples, DSL-SRM with a skewed translator is proposed. The proposed structure is designed and developed by using computational magnetic tools. This concept is effective for reduction of the force ripples and improves the performance the machine. The proposed design reduced the percentage of force ripples to 15% and improves the force density.
A multilayer optimization scheme to retain the voltage and frequency in standalone microgrids

A new technique is proposed in this paper to manage the frequency and voltage of standalone remote area microgrids within predefined limits. To this end, a multilayer approach is used to determine the most suitable actions. The proposed technique contemplates the dynamic supply adequacy and sustainability of the microgrid in addition to the operational costs. It takes action instantly after an event that violates the microgrid's voltage and/or frequency and adjusts the generation levels of the dispatchable sources and determining the best configuration for the microgrid's network. It then proceeds to determine the level of power support from available neighboring microgrids and controlling the loads, as well as the charge or discharge power of existing battery storage systems, in addition to curtailing the renewable sources in successive actions. The developed operation-stage technique uses a metaheuristic optimization. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.
Construction of Hyaluronic Tetrasaccharide Clusters Modified Polyamidoamine siRNA Delivery System

The CD44 protein, as a predominant receptor for hyaluronan (HA), is highly expressed on the surface of multiple tumor cells. HA, as a targeting molecule for a CD44-contained delivery system, increases intracellular drug concentration in tumor tissue. However, due to the weak binding ability of hyaluronan oligosaccharide to CD44, targeting for tumor drug delivery has been restricted. In this study, we first use a HA tetrasaccharide cluster as the target ligand to enhance the binding ability to CD44. A polyamidoamine (PAMAM) dendrimer was modified by a HA tetrasaccharide cluster as a nonviral vector for small interfering RNA (siRNA) delivery. The dendrimer/siRNA nanocomplexes increased the cellular uptake capacity of siRNA through the CD44 receptor-mediated endocytosis pathway, allowing the siRNA to successfully escape the endosome/lysosome. Compared with the control group, nanocomplexes effectively reduced the expression of GFP protein and mRNA in MDA-MB-231-GFP cells. This delivery system provides a foundation to increase the clinical applications of PAMAM nanomaterials.
Fuzzy Feature Extraction for Multichannel EEG Classification

EEG signals (EEGs) are usually collected by placing multiple electrodes at various positions along the scalp as multichannel data. Given that many channels are collected for each single-trial, the multichannel EEG classification problem can be treated as multivariate time series classification problem. For multichannel EEG data to be more accurately classified, we propose an algorithm, called the fuzzy multichannel EEG classifier (FMCEC). This algorithm can take into consideration the interaction among different signals collected at different time instants and locations on the skull when constructing a classifier. The FMCEC first preprocess raw EEG data by eliminating noise by discretization of the data. It then performs fuzzification of the resulting discretized data to capture imprecision and vagueness in the data. Given the fuzzified data, FMCEC then discovers intrachannel patterns within each channel and then interchannel patterns between different channels of EEGs. The discovered patterns, which are represented as fuzzy temporal patterns, are then used to characterize and differentiate between different classes of multichannel EEG data. To evaluate the effectiveness of FMCEC, we tested it with several sets of real EEG datasets. The results show that the algorithm can be a promising tool for the classification of multichannel EEG data.
Pharmacologic Depletion of Microglia Increases Viral Load in the Brain and Enhances Mortality in Murine Models of Flavivirus-Induced Encephalitis

As CNS invasion by flaviviruses is a rare but life-threatening event, it is critical to understand how brain-resident immune cells elicit protection or injury during disease progression. Microglia have been shown to be important in viral clearance but may also contribute to CNS injury as part of the neuroinflammatory process. By utilizing a microglial depletion model, we can begin to parse out the exact roles of microglia during flaviviral pathogenesis with hopes of understanding specific mechanisms as potential targets for therapeutics. ABSTRACT Flaviviruses account for most arthropod-borne cases of human encephalitis in the world. However, the exact mechanisms of injury to the central nervous system (CNS) during flavivirus infections remain poorly understood. Microglia are the resident immune cells of the CNS and are important for multiple functions, including control of viral pathogenesis. Utilizing a pharmacologic method of microglia depletion (PLX5622 [Plexxikon Inc.], an inhibitor of colony-stimulating factor 1 receptor), we sought to determine the role of microglia in flaviviral pathogenesis. Depletion of microglia resulted in increased mortality and viral titer in the brain following infection with either West Nile virus (WNV) or Japanese encephalitis virus (JEV). Interestingly, microglial depletion did not prevent virus-induced increases in the expression of relevant cytokines and chemokines at the mRNA level. In fact, the expression of several proinflammatory genes was increased in virus-infected, microglia-depleted mice compared to virus-infected, untreated controls. In contrast, and as expected, expression of the macrophage marker triggering receptor expressed on myeloid cells 2 (TREM2) was decreased in virus-infected, PLX5622-treated mice compared to virus-infected controls. IMPORTANCE As CNS invasion by flaviviruses is a rare but life-threatening event, it is critical to understand how brain-resident immune cells elicit protection or injury during disease progression. Microglia have been shown to be important in viral clearance but may also contribute to CNS injury as part of the neuroinflammatory process. By utilizing a microglial depletion model, we can begin to parse out the exact roles of microglia during flaviviral pathogenesis with hopes of understanding specific mechanisms as potential targets for therapeutics.
Robust sliding mode control for single-link flexible joint using LMIs approach

This paper deals with the problem of robust Sliding Mode Control (SMC) of uncertain nonlinear dynamic systems. The uncertainties being norm bounded in both state and input matrices as well as norm bounded nonlinearity. Sufficient conditions using Linear Matrix Inequalities (LMIs) are derived to guarantee the existence of linear sliding surface and the quadratic stability of the reduced-order equivalent dynamic system restricted to the sliding surface. Thus, one can design a continuous nonlinear control strategy ensuring a sliding motion despite the presence of uncertainties. We analysis and applies the designed controller to a flexible joint robotic system, in which the simulations results are validate its effectiveness and good performance.
Effect of acupuncture on in vitro fertilization

Background: Although many patients try to seek acupuncture to improve in vitro fertilization (IVF) outcomes, evidence regarding its efficacy and acupoints characters are lacking. The aim of this protocol is to evaluate the effectiveness and safety, as well as the acupoints characteristics of acupuncture in the treatment of female undergoing IVF, by conducting a systematic review and data mining. Methods: The following 6 databases will be searched from their inception to April 30, 2018: PubMed, Chinese National Knowledge Infrastructure, Wanfang, VIP database, Embase, and Cochrane Library. The randomized controlled trials (RCTs) or case–control studies of acupuncture that assess clinical effects and side effects in female undergoing IVF are included. The primary outcome measures will be number of oocytes retrieved, fertilization rate, oocyte cleavage rate, high-quality embryos rate, ovarian hyperstimulation syndrome (OHHS) incidence rate, clinical pregnancy rate (CPR), biochemical pregnancy rate (BPR), implantation rate, and cycle cancellation rate. Two reviewers will independently undertake data extraction and quality assessments. Data will be synthesized by RevMan V.5. 3 software. Acupoints characteristics will be excavated using Traditional Chinese Medicine inheritance support system (TCMISS). Reporting bias will be assessed by Funnel plots, Begg test, and Egger test. Results: This review will assess the clinical efficacy and safety, as well as the acupoints characteristics of acupuncture on IVF. Conclusion: These findings will summarize the current evidence of acupuncture on IVF outcomes and may provide guidance for clinicians and infertile women to select acupuncture for IVF.
Firefly algorithm based-pitch angle control of a tidal stream generator for power limitation mode

This paper deals with the modeling and control of a Tidal Stream Generator (TSG) for marine renewable energy. The Power Take Off system consists of a Tidal Stream Turbine coupled to a Doubly Fed Induction Generator with a 1.5 MW power. At any tidal site, there is a variation in the tidal current speed with the occurring of maximum velocities. This means that the TSG system needs to be controlled in order to limit the generated power and shedding mechanical load at high current speeds. For this purpose, a Proportional Integral (PI) controller for the blade pitch angle is investigated. A firefly algorithm based-metaheuristic theory is used to tune the gains of the PI. Simulation results show the performance of the implemented control strategy by optimizing the generated power in case of strong flow speeds.
Effect of kinesiology tape application direction on quadriceps strength

Background: Controversy exists regarding whether the kinesiology tape application direction affects muscle strength. Methods: Eighteen healthy volunteers (12 men, 6 women) participated. Kinesiology tape was randomly applied to the quadriceps muscles either from origin to insertion or from insertion to origin. A Biodex isokinetic dynamometer was used to measure the peak torque of the quadriceps pre-and post-taping. Results: There was a significant difference in muscle strength after taping, regardless of the kinesiology tape application direction. There were no significant differences in the peak torque of the quadriceps between the 2 kinesiology tape application directions. Conclusions: The application of kinesiology tape application to the rectus femoris, vastus medialis, and vastus lateralis of the quadriceps increased the muscle torque, regardless of the tape application direction. Therefore, to enhance quadriceps strength, we recommend the application of kinesiology tape to 3 of the muscles of the quadriceps (specifically, the rectus femoris, vastus medialis and vastus lateralis), irrespective of the tape application direction.
Excess noise and deep level defects diagnostics in semiconductor barrier structures

This work is devoted to theoretical and experimental investigation of the problem of excess noise and deep level defects diagnostics in semiconductor barrier structures. Different models of deep energy levels recharging and relaxation processes in semiconductor barrier structures are discussed. The activation-drift model of low-frequency noise generation in physically barrier layers with the influence of the Frenkel effect has been developed. Experimental studies of deep levels in the semiconductor structures and devices with taken into account the Frenkel effect allows predicting more precisely the reliability and performance characteristics.
Outlier detection in sensed data using statistical learning models for IoT

Internet of Things (IoT) devices are composed of millions of sensors that continuously sense environmental parameters which are effectively fused to gain insights on a particular area or region based on which desired actions are taken. However, before any fusion of the data takes place, checking the quality of data is of paramount importance as these data may also be contaminated with outliers which limit their overall efficiency. Outliers or in other words, any abrupt change in values can be either due to a drastic change in environment (Event) or because of any malfunctioning of the sensor (Error). This paper introduces an IoT architecture to detect the occurrence of both Error and Event in a forest environment with the help of four statistical models, i.e., Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM) and Linear Discriminant Analysis (LDA). We take into account the spatial and temporal dependencies of the data as well. Simulation results show that the models can effectively detect both types of outliers with upto 100% accuracy for Error detection and upto 98.51% in case of Event detection. Also, the results provide evidence that using spatial and temporal parameters in event detection can have a positive influence on the prediction accuracy which was observed to increase from 81.68% to 96.53% using RF. Among the four models, RF is seen to outperform the others.
Induction motor health identification using wavelet transform technique

This Paper presents an analytical technique to diagnosis the unsymmetrical supply fault of a polyphase induction motor by using the line to neutral voltage signature. For this the polyphase induction motor must be connected in star and its neutral point must be accessible. The line to neutral voltage signature is taken from the experimental set up with both the symmetrical and unsymmetrical supply condition. These signatures are analyzed by using the discrete wavelet technique. The severity of the fault of an induction motor is found from the signal (approximation signals) which along with detail signal is generated in the two different conditions.
Spectral and Energy Efficiency Analysis for SLNR Precoding in Massive MIMO Systems With Imperfect CSI

We derive tractable bound expressions on achievable spectral efficiency for a multiple-input multiple-output (MIMO) system with a signal-to-leakage-plus-noise ratio precoding scheme (SLNR-PS) under the condition of imperfect channel state information. These bounds are tight and approach exact values when the number of base station (BS) antennas is large. A problem of energy efficiency (EE) maximization is investigated by using a practical power consumption model. The effects of the system parameters and quality of channel estimation, including the number of BS antennas, transmit power, and training length on the performance metrics, are explicitly analyzed. Following that, an alternating optimization algorithm is employed to obtain the optimum EE. It has been shown that the proposed SLNR-PS performs better than the matched-filtering and zero-forcing schemes and a deployment of massive MIMO with the optimal transmit power and training length can achieve high EE.
Constraint Gaussian Filter With Virtual Measurement for On-Line Camera-Odometry Calibration

Extrinsic calibration is one of the most important problems in robotics, whose objective is to estimate the relative poses among the sensors and the robot. Currently, most on-line solutions of extrinsic calibration are based on the Gaussian filters, which estimate all the system states iteratively. However, a large number of the system states are not related to calibration. Estimating all these redundant states may highly increase the dimensionality of the problem, and therefore, undermine the calibration performance in both efficiency and robustness. In this paper, we propose an innovative on-line calibration algorithm, called constraint Gaussian filter with virtual measurements (VMCGF). The nature of VMCGF is a filter with a compact state vector containing only the states of interest, also called essential states. Violating the modeling principles of the traditional Gaussian filters, the measurements cannot be expressed by the observation function with the essential states solely. Exploiting the constraints between the measurements and the essential states, virtual measurements are generated according to the properties of the generalized chi-square distribution. Although originally developed to solve the calibration problem, VMCGF is a general filtering algorithm, that can be applied to solve other problems that might suffer from redundant states. The implementation of VMCGF on a camera odometry calibration problem is introduced, and its observability properties are analyzed. Both simulations and experiments are conducted to validate our algorithm.
A consensus based solution to the classic economic dispatch problem on a multi-agent system framework

Smart grids are a modernized type of present-day electric grid. It is a cyber-physical network that employs a advanced level of monitoring, control which can combat the technological difficulties faced today. With the help of this innovative framework, the fundamental problem of economic dispatch needs to be revisited. In this paper, a consensus based approach is presented to solve the Economic Load Dispatch (ELD) problem in smart grid. A multi-agent based intelligent system framework is created by co-simulation of MATLAB and JADE to implement the proposed approach in a distributed manner. The mathematical formulation of the proposed approach is presented in this paper. Simulation results on various case studies are presented to demonstrate the effectiveness of the approach.
Degrees of freedom region of device-relaying cellular network

In this paper, we characterize the degrees of freedom (DoF) region of a MIMO device-relaying cellular network (DRCN) with three users and one base station (BS), where each user exchanges unicast messages with the BS. We assume that one of the users has no direct link to the BS, and hence, device-relaying is utilized to exchange data between this user and the BS, i.e., data is relayed via another user which has a direct link to the BS and a device to device (D2D) link to this user. We assume that each node operates in perfect full-duplex mode. Cut-set and genie-aided bounds are utilized to derive an outer bound on the DoF region. We provide achievability schemes that utilize signal space alignment for network coding, null-space beamforming and zero-forcing. The achievable schemes provide an inner bound on the DoF region that coincides with the outer bound.
Stay Fresh: Speculative Synchronization for Fast Distributed Machine Learning

Large machine learning models are typically trained in parallel and distributed environments. The model parameters are iteratively refined by multiple worker nodes in parallel, each processing a subset of the training data. In practice, the training is usually conducted in an asynchronous parallel manner, where workers can proceed to the next iteration before receiving the latest model parameters. While this maximizes the rate of updates, the price paid is compromised training quality as the computation is usually performed using stale model parameters. To address this problem, we propose a new scheme, termed speculative synchronization. Our scheme allows workers to speculate about the recent parameter updates from others on the fly, and if necessary, the workers abort the ongoing computation, pull fresher parameters, and start over to improve the quality of training. We design an effective heuristic algorithm to judiciously determine when to restart training iterations with fresher parameters by quantifying the gain and loss. We implement our scheme in MXNet–a popular machine learning framework–and demonstrate its effectiveness through cluster deployment atop Amazon EC2. Experimental results show that speculative synchronization achieves up to 3× speedup over the asynchronous parallel scheme in many machine learning applications, with little additional communication overhead.