Miracle-dz/UniFAR-FTUs huggingface dataset:
Electric Field Application In Vivo Regulates Neural Precursor Cell Behavior in the Adult Mammalian Forebrain

Abstract Deep brain stimulation (DBS), which uses electrical stimulation, is a well-established neurosurgical technique used to treat neurologic disorders. Despite its broad therapeutic use, the effects of electrical stimulation on brain cells is not fully understood. Here, we examine the effects of electrical stimulation on neural stem and progenitor cells (collectively neural precursor cells; NPCs) from the subventricular zone in the adult forebrain of C57BL/6J mice. Previous work has demonstrated that adult-derived NPCs are electro sensitive and undergo rapid and directed migration in response to application of clinically relevant electric fields (EFs). We examine NPC proliferation kinetics and their differentiation profile following EF application using in vitro and in vivo assays. In vitro direct current electrical stimulation of 250 mV/mm is sufficient to elicit a 2-fold increase in the neural stem cell pool and increases neurogenesis and oligogenesis. In vivo, asymmetric biphasic electrical stimulation similarly increases the size of the NPC pool and alters neurogenesis. These findings provide insight into the effects of electrical stimulation on NPCs and suggest its potential use as a regenerative approach to neural repair.
The effect of breed and feeding level on carcass composition, fatty acid profile and expression of genes encoding enzymes involved in fat metabolism in two muscles of pigs fed a diet enriched in n-3 fatty acids. A preliminary study

The experiment was performed according to a 2 x 2 factorial design with breed and level of nutrition as factors affecting fat metabolism in pigs. Two groups of gilts, each comprising 4 Polish Large White (PLW) and 4 Synthetic Line 990 (L990) animals, were fed from 60 to 105 kg body weight on the experimental diet at 85% or 95% of assumed ad libitum intake. The diet contained 2% of linseed, 0.5% rapeseed and 0.5% fish oils as the source of n-3 fatty acids (FA). The carcass protein content was smaller and backfat thick- ness greater in L990 than in PLW pigs. Also the intramuscular fat content in the biceps femoris (BF) and longissimus dorsi (LD) muscles and their contents of total FA, saturated fatty acids, monounsaturated fatty acids (MUFA) and poly- unsaturated fatty acids (PUFA) were higher or tended to be higher in L990 than in PLW pigs, the differences between the breeds being more pronounced in the BF muscle. The n-6/n-3 PUFA ratio tended to be lower in the LD muscle, whereas in the BF muscle, it was lower in L990 than in PLW pigs. Feeding at the 85% level resulted in smaller backfat thickness and carcass fat con- tent in animals of both breeds. Gene expression of stearoyl-CoA desaturase in both muscles was higher in L990 pigs, which could have resulted in the higher MUFA and PUFA contents in this breed. Gene expression of fatty acid-binding protein 4 and peroxisome proliferator-activated receptor were affected both by breed and feeding level only in the BF muscle.
Histone deacetylase inhibitors induce medulloblastoma cell death independent of HDACs recruited in REST repression complexes

Repressor element 1‐silencing transcription factor (REST) acts as a transcriptional repressor by recruiting several chromatin modifiers, including histone deacetylase (HDAC). Elevated REST expression in medulloblastoma has been associated with tumor progression nevertheless, the tumor shows high sensitivity to HDAC inhibitors (HDACi). However, the functional implications of REST and its requirement for HDACi‐induced anti‐cancer effects are not well understood.
An Association Rrule Data Classification with Optimization

-Extracting collection reserve foreign statistics is a banderole nomination of observations mining and is gaining considerable attention in recent years. In process filtering and Data Classification data mining plays a role of key player. The use of Data mining as a process filtering technique is used in this paper for calculating the support value of the datasets. After calculating the support value the optimization technique is applied we in our case has used Ant Colony Optimization technique to find the highest threshold value by going through several iterations. By calculating through several iteration we get negative and positive association and our negative and positive association based classification improves efficacy of the result and shows the effectiveness of the approach used.
Cache Resiliency Techniques for a Low-Voltage RISC-V Out-of-Order Processor in 28-nm CMOS

Architecture-level assist techniques enable low-voltage operation by tolerating errors in SRAM-based caches. A line recycling (LR) technique is proposed to reuse faulty cache lines that fail at low voltages to correct errors with only 0.77% level-2 (L2) area overhead. LR can either save 33% of cache capacity loss from line disable or allow further reduction in minimum operating voltage (Vmin). Bit bypass implemented with SRAM extends the tag array to log error entries providing multibit-error protection for the metadata with minimal area overhead. An open-source out-of-order superscalar processor that implements the 64-bit RISC-V instruction set architecture is built to validate the proposed cache resiliency techniques. The 2.7 mm ${\times }$ 1.8 mm chip includes one core operating at 1.0 GHz at nominal 0.9 V with 1 MB of L2 cache in a 28-nm HPM process. LR reduces Vmin to 0.47 V, improving energy efficiency by 43% with negligible impact on CPI.
Tsugacorticium kenaicum (Hymenochaetales, Basidiomycota), a new corticioid genus and species from Alaska

The new corticioid genus and species, Tsugacorticium kenaicum from Alaska, is described. Primarily found on attached, dead, corticate branches of mountain hemlock in the Kenai Peninsula, T. kenaicum is characterized by small, soft, white to yellow basidiomata, thickened subhymenium, much branched dendrohyphidia, and small globose to subglobose basidiospores. Although morphologically similar to Dendrothele (Agaricales) and Dendrocorticium (Corticiales), phylogenetic analyses of the nuclear large subunit ribosomal RNA gene place this taxon in the Rickenellaceae clade in the Hymenochaetales. Tsugacorticium kenaicum is described, illustrated, and compared to morphologically similar and phylogenetically related taxa.
Responses of a Specialized Insectivorous Mammal (Myrmecophaga tridactyla) to Variation in Ambient Temperature 1

This study reports the effect of daily mean ambient temperature on daily activity and habitat use by the giant anteater (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil. We equipped 11 giant anteaters with a global position system collar programmed to acquire a location every 10 min for 15 d. A temperature data logger left in the meteorological station at the study site registered data on ambient temperature. Giant anteaters were mainly active in open fields except during the coldest days, when they sought protection inside forests. Giant anteaters also used open habitats for resting but during the hottest or chilliest days they sheltered in forests. In the Pantanal, where temperatures are usually high, giant anteaters were active all night long, but as daily average ambient temperature decreased anteaters began and finished activity progressively earlier and reduced total activity. As a consequence, time spent active at daylight increased progressively and time spent active during the night decreased progressively, probably to allow the anteaters to expose themselves to solar radiation and to avoid heat loss during the night.
Yersinia ruckeri sp. nov., the redmouth (RM) bacterium.

Cultures of the redmouth (RM) bacterium, one of the etiological agents of redmouth disease in rainbow trout (Salmo gairdneri) and certain other fishes, were characterized by means of their biochemical reactions, by deoxyribonucleic acid (DNA) hybridization, and by determination of guanine-plus-cytosine (G+C) ratios in DNA. The DNA relatedness studies confirmed the fact that the RM bacteria are members of the family Enterobacteriaceae and that they comprise a single species that is not closely related to any other species of Enterobacteriaceae. They are about 30% related to species of both Serratia and Yersinia. A comparison of the biochemical reactions of RM bacteria and serratiae indicated that there are many differences between these organisms and that biochemically the RM bacteria are most closely related to yersiniae. The G+C ratios of RM bacteria were approximated to be between 47.5 and 48.5%. These values are similar to those of yersiniae but markedly different from those of serratiae. On the basis of their biochemical reactions and their G+C ratios, the RM bacteria are considered to be a new species of Yersinia, for which the name Yersinia ruckeri is proposed. Strain 2396-61 (= ATCC 29473) is designated the type strain of the species.
Effectiveness of chain‐springs on leghold traps for reducing injuries to captured brushtail possums (Trichosurus vulpecula)

Abstract Leghold traps continue to be an essential tool for managing brushtail possums (Trichosurus vulpecula) in New Zealand. Concerns about the injuries such traps cause have given rise to efforts to make these traps more humane. One trap modification that had potential for reducing injuries was the placement of an appropriately sized spring between two links of a trap chain to provide a cushioning effect when the chain is extended. Such springs were added to Lanes‐Ace and Victor No. 1½ traps and the injuries caused to captured possums were compared with those to possums captured in the same traps without springs or in Victor No. 1 traps. The addition of chain‐springs reduced the severity of injuries significantly, but injury scores were still significantly higher than for possums captured in Victor No. 1 traps. Consequently, although chain‐springs improved the welfare performance of the Lanes‐Ace and Victor No. 1½ traps, they did not do so to such an extent that those traps could be considered as humane as the currently acceptable Victor No. 1 traps.
Representation learning via a semi-supervised stacked distance autoencoder for image classification

Image classification is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An autoencoder is a special type of neural network, often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional autoencoder, incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance autoencoder. Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic autoencoder structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance autoencoder method is compared with the traditional autoencoder, sparse autoencoder, and supervised autoencoder. Experimental results verify the effectiveness of the proposed model.
Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Dynamic channel assignment (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent reinforcement learning (RL) based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.
The Effect of Benson's Relaxation Technique on Anxiety, Depression and Sleep Quality of Elderly Patients Undergoing Hemodialysis

Background: Chronic kidney disease (CKD) is the most important cause of morbidity and mortality among elderly. Hemodialysis is the most commonly applied method for treatment, and is accompanied by a high incidence of psychological problems such as anxiety, depression, and sleep disturbance, which are having a negative impact on one’s life. Benson’s relaxation technique (BRT) is an effective technique used to reduce stress, anxiety, depression, and enhance sleep quality in elderly patients undergoing hemodialysis. Aim: determine the effect of Benson's relaxation technique on anxiety, depression, and sleep quality among elderly patients undergoing hemodialysis. Design: Quasi- experimental, pre and post intervention design was used. Setting: The study was carried out in the hemodialysis unit affiliated to Mansoura University Hospital, Mansoura, Egypt. Subjects: A purposive convenience sample of 92 elderly patients scheduled for maintenance hemodialysis. Tools: Five tools were used: interview schedule-mini-mental state examination (MMSE) - study subject's relaxation technique knowledge-hospital anxiety and depression scale (HADS) and Pittsburgh sleep quality index (PSQI). Results: The age of the studied subjects ranged from 60 to 92 years with mean ± SD = 66.256± 6.78 years, 70.9% are males, all mean total scores of hospital anxiety scale, depression scale, and Pittsburgh sleep quality index for elderly patients improved significantly after applying Benson's relaxation technique. Conclusion: Benson’s relaxation technique (BRT) is an effective technique to reduces level of anxiety, depression, and improve sleep quality in elderly patients undergoing hemodialysis. Recommendation: Hospitals are recommended to use the Benson's relaxation technique for the management of anxiety, depression and sleep disturbance associated with hemodialysis for elderly patients.
Microbial biomass and community composition in boreal lake sediments

We used phospholipid fatty acids (PLFA) to determine microbial biomass and community structure in the sediments of eight boreal lakes with different loadings of allochthonous organic carbon and total phosphorus (TP) in the water during the course of a year. The total concentration of PLFA, an estimate of the microbial biomass, depended more on TP, a proxy for pelagic primary production, but not on dissolved organic carbon, a proxy for terrestrial organic carbon input. The composition of PLFAs varied considerably over time, demonstrating seasonal dynamics in microbial community composition. When PLFA profiles in all lakes and seasons are compared, community composition is more similar within season than within lakes.
DAN: a deep association neural network approach for personalization recommendation

The collaborative filtering technology used in traditional recommendation systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional recommendation algorithms, thus leading to the emergence of recommendation systems based on deep learning. At present, deep learning recommendations mostly use deep neural networks to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep neural network recommendation algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the recommendation. Aimed at this problem, in this paper we propose a feedforward deep neural network recommendation method, called the deep association neural network (DAN), which is based on the joint action of multiple categories of information, for implicit feedback recommendation. Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the recommendation is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint recommendations can provide better recommendation performance.
Predicting vegetation types at treeline using topography and biophysical disturbance variables

The relationships between four vegetation types and variables representing topography and biophysical disturbance gradients were modeled for a study area in east-central Glacier National Park, Montana. Four treeline transition vegetation types including closed-canopy forest, open-canopy forest, meadow, and unvegetated surfaces (e.g. rock, snow, and ice) were identified and mapped through classification of satellite data and subsequent field verification. Topographic characteristics were represented using a digital elevation model and three variables derived from topoclimatic potential models (solar radiation potential, snow accumulation potential, and soil saturation potential). A combination of generalized additive and generalized linear modeling (GAM and GLM, respectively) techniques was used to construct logistic regression models representing the distributions of the four vegetation types. The variables explained significant amounts of variation in the vegetation types, but high levels of variation remained unexplained. A comparison of ‘expected’ and ‘observed’ vegetation patterns suggested that some unexplained variation may have occurred at the basin scale. A suite of tools and techniques is presented that facilitates predicting landscape-scale vegetation patterns and testing hypotheses about the spatial controls on those patterns.
Manifold-Based Robot Motion Generation

In order to make an autonomous robot system more adaptive to human-centered envi- ronments, it is effective to let the robot collect sensor values by itself and build controller to reach a desired configuration autonomously. Multiple sensors are often available to estimate the state of the robot, but they contain two problems: (1) sensing ranges of each sensor might not overlap with each other and (2) sensor variable can contain redundancy against the original state space. Regarding the first problem, a local coordinate defini tion based on a sensor value and its extension to unobservable region is presented. This technique helps the robot to estimate the sensor variable outside of its observation range and to integrate regions of two sensors that do not overlap. For a solution to the sec - ond problem, a grid-based estimation of lower-dimensional subspace is presented. This estimation of manifold allows the robot to have a compact representation, and thus the proposed motion generation method can be applied to the redundant sensor system. In the case of image feature spaces with a high-dimensional sensor signal, a manifold estimation-based mapping, known as locally linear embedding (LLE), was applied to an estimation of distance between robot body and an obstacle.
Chassis Geometry Optimization based on 3D-scans of the Ergonomic Driving Position

The paper presents the process of modifying the chassis of an electric vehicle of the Silesian Greenpower team based on 3D scanning technology. Principles of 3D scanning technology using Kinect 3D have been described, as well as preparation of the scans of the driver to optimize the geometry of the chassis. The driver has been scanned in various driving positions in order to establish the most ergonomic one. The geometry of the chassis has been modified according to the scanned convenient driving position and the newest technical regulations. The research allowed to improve the vehicle’s behavior on the corners, to reduce the frontal cross-section analyze the mass center of the driver. 
Standardization and Validation of Hindi Version of Kansas Marital Satisfaction Scale

The present study aimed to elucidate the psychometric properties, factorial structure, and predictive validity of Hindi version of Kansas Marital Satisfaction Scale in Indian cultural milieu. A total of 300 couples, 21 to 75 years old (300 husbands and 300 wives) were, conveniently sampled from Chowk and adjoining areas of Varanasi city of Uttar Pradesh, completed the Hindi version of Kansas Marital Satisfaction Scale. Factor analysis (principal components) with loadings equal to or more than 0.400, Eigen value equal to 1.00 and the Scree plot revealed single factors explaining a total of 81.116 % variance for husbands, 77.129 % variance for wives and 78.992 % variance for couples (husbands and wives). Confirmatory factor revealed that the fit indices were very good (χ2 = 0.00, p < 0.001; CFI = 1.00; GFI = 1.00; SRMR = 0.00; RMR = 0.00) over the level of analysis (for husbands, wives and whole sample). The reliability coefficients of the single factor emerged fairly high and indicated good reliability of the Hindi version of KMSS. KMSS correlated significantly and positively with all measures of DAS-H indicating good convergent validity of KMSS-H. The results also indicated no significant gender and age differences on marital satisfaction as measured by KMSS-H. The findings indicated that Kansas Marital satisfaction Scale-Hindi (KMSS-H) may function as a useful brief measure of marital satisfaction in Indian culture.
Tax Revenue and Foreign Direct Investment in Bangladesh: An Empirical Analysis

As per the common narrative, Foreign Direct Investment (FDI) is a crucial source of capital investment. Most of the developing countries try to attract FDI for improving their domestic investment scenario. However, FDI inflow may be hampered due to high tax revenue in developing countries. To the best of our knowledge, no studies have been conducted to investigate the underlying relationship between FDI and tax revenue in Bangladesh. Thus, the core objective of this paper is to analyze the impact of tax revenue on FDI with the help of time series data ranging from 2001-2015. According to Johansen’s cointegration test result, concerned variables are cointegrated in the long run. By employing the Granger causality test, we have found that there is a unidirectional causality running from tax revenue to FDI in the long run. For long run estimation, we have used Dynamic Ordinary Least Square (DOLS) approach. According to the estimation result, the coefficient of tax revenue is negative indicating an inverse relation with FDI. These results may help policymakers to come up with proper policies to boost the economy of Bangladesh.