Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of key candidate genes in neuropathic pain by integrated bioinformatic analysis

This study aimed to disclose differentially expressed genes (DEGs) in dorsal root ganglia (DRGs) of neuropathic pain (NP) from spared nerve injury (SNI) model, thereby identifying specific and meaningful genetic targets for the diagnosis and treatment of NP. The GSE89224 was downloaded from the GEO database. DEGs were screened using the GEO2R online tool. Functional enrichment analysis of DEGs was then performed using the DAVID and constructed using the R ggplot2 package. Protein‐protein interaction (PPI) network was constructed from the STRING database and visualized in Cytoscape software. MicroRNA targeting these DEGs was obtained from the TarBase and miRTarBase database, while transcription factor (TF)‐targeting DEGs were predicted from the ENCODE database, both of which utilized the visual analytics platform NetworkAnayst. Finally, a merged microRNA‐TF network was constructed based on the above two networks and was then analyzed with Cytoscape. Eighty DEGs were screened, only Vstm2b and Htr3a were downregulated and 78 genes were upregulated. The real‐time polymerase chain reaction was applied to validate the gene expression of the top five DEGs (Npy, Atf3, Gpr151, Sprr1a, and Cckbr) in the DRG tissue 5 days after SNI surgery. It was found that Npy, Atf3, and Sprr1a have a significant increase after SNI stimulation, while Gpr151 and Cckbr showed a slight upward trend. Functional analysis was performed on all DEGs, of which 58 biological processes were enriched by gene ontology analysis, and 11 signaling pathways were enriched by KEGG analysis. In the PPI network, Atf3, Jun, Timp, and Npy had a higher degree. Thus, combined with various bioinformatic analyses, Npy and Atf3 may serve as the prognostic and therapeutic targets of NP. Key microRNA (mmu‐mir‐16‐5p) and TF (MEF2A) were predicted to be associated with the pathogenetic process of NP with microRNA‐TF regulatory network analysis, which were also identified as key regulators in the progression of NP.
Transcriptional alterations in glioma result primarily from DNA methylation–independent mechanisms

In cancer cells, aberrant DNA methylation is commonly associated with transcriptional alterations, including silencing of tumor suppressor genes. However, multiple epigenetic mechanisms, including polycomb repressive marks, contribute to gene deregulation in cancer. To dissect the relative contribution of DNA methylation–dependent and –independent mechanisms to transcriptional alterations at CpG island/promoter-associated genes in cancer, we studied 70 samples of adult glioma, a widespread type of brain tumor, classified according to their isocitrate dehydrogenase (IDH1) mutation status. We found that most transcriptional alterations in tumor samples were DNA methylation–independent. Instead, altered histone H3 trimethylation at lysine 27 (H3K27me3) was the predominant molecular defect at deregulated genes. Our results also suggest that the presence of a bivalent chromatin signature at CpG island promoters in stem cells predisposes not only to hypermethylation, as widely documented, but more generally to all types of transcriptional alterations in transformed cells. In addition, the gene expression strength in healthy brain cells influences the choice between DNA methylation- and H3K27me3-associated silencing in glioma. Highly expressed genes were more likely to be repressed by H3K27me3 than by DNA methylation. Our findings support a model in which altered H3K27me3 dynamics, more specifically defects in the interplay between polycomb protein complexes and the brain-specific transcriptional machinery, is the main cause of transcriptional alteration in glioma cells. Our study provides the first comprehensive description of epigenetic changes in glioma and their relative contribution to transcriptional changes. It may be useful for the design of drugs targeting cancer-related epigenetic defects.
A GLIS3–CD133–WNT-signaling axis regulates the self-renewal of adult murine pancreatic progenitor-like cells in colonies and organoids

The existence and regenerative potential of resident stem and progenitor cells in the adult pancreas are controversial topics. A question that has been only minimally addressed is the capacity of a progenitor cell to self-renew, a key attribute that defines stem cells. Previously, our laboratory has identified putative stem and progenitor cells from the adult murine pancreas. Using an ex vivo colony/organoid culture system, we demonstrated that these stem/progenitor-like cells have self-renewal and multilineage differentiation potential. We have named these cells pancreatic colony-forming units (PCFUs) because they can give rise to three-dimensional colonies. However, the molecular mechanisms by which PCFUs self-renew have remained largely unknown. Here, we tested the hypothesis that PCFU self-renewal requires GLIS family zinc finger 3 (GLIS3), a zinc-finger transcription factor important in pancreas development. Pancreata from 2- to 4-month-old mice were dissociated, sorted for CD133highCD71low ductal cells, known to be enriched for PCFUs, and virally transduced with shRNAs to knock down GLIS3 and other proteins. We then plated these cells into our colony assays and analyzed the resulting colonies for protein and gene expression. Our results revealed a previously unknown GLIS3–to–CD133–to–WNT signaling axis in which GLIS3 and CD133 act as factors necessary for maintaining WNT receptors and signaling molecules in colonies, allowing responses to WNT ligands. Additionally, we found that CD133, but not GLIS3 or WNT, is required for phosphoinositide 3-kinase (PI3K)/AKT Ser/Thr kinase (AKT)-mediated PCFU survival. Collectively, our results uncover a molecular pathway that maintains self-renewal of adult murine PCFUs.
A Modified Parallel Plate Flow Chamber to Study Local Endothelial Response to Recirculating Disturbed Flow.

Atherosclerosis develops at arterial sites where endothelial cells (ECs) are exposed to low time-averaged shear stress, in particular in regions of recirculating disturbed flow. To understand how hemodynamics contributes to EC dysfunction in atheroma development, an in vitro parallel plate flow chamber gasket was modified with protruding baffles to produce large recirculating flow regions. Computational fluid dynamics predicted that more than 60% of the flow surface area was below the 12 dynes/cm2 atheroprotective threshold. Bovine aortic endothelial cells (BAECs) were then seeded in the parallel plate flow chamber with either the standard laminar or the new disturbed flow gasket and exposed to flow for 36 hours. Cell morphology, nitric oxide, proliferation, permeability, and monocyte adhesion were assessed by phase contrast and confocal microscopy. BAEC exposed to 20 dynes/cm2 shear stress in the laminar flow device aligned and elongated in the flow direction while increasing nitric oxide, decreasing permeability, and maintaining low proliferation and monocyte adhesion. As expected, BAEC in the recirculating flow and low shear stress disturbed flow device regions did not elongate or align, produced less nitric oxide, and showed higher proliferation, permeability, and monocyte adhesion than cells in the laminar flow device. Surprisingly, cells in disturbed flow device regions exposed to atheroprotective shear stress did not consistently align or decrease permeability, and these cells demonstrated low nitric oxide levels. These results suggest that atheroprotective signaling may be inhibited by neighboring cells exposed to recirculating flow, highlighting the complex relationship between hemodynamics and atheroma.
“When a Girl Says No, You Should Be Persistent Until She Says Yes”: College Students and Their Beliefs About Consent

One in five college women report being sexually assaulted with 23% to 45% of men reporting attempting or completing a sexual assault while attending a university. One important concept in preventing sexual assault is consent. It is important to ensure that when students are being asked to wait for consent, they understand what consent is and the potential ways it could manifest in a sexual situation. In total, 25 female and 20 male college students participated in semistructured focus groups or interviews to gain a better understanding of their perspectives of consent and how they ensure that it has been given. Results demonstrate that college students do not have a consistent, coherent, or precise definition of consent. Participants often described consent using vague language and were only able to clearly identify verbal indicators of consent. Much of the discussion of consent centered around an individual’s ability or inability to clearly and directly communicate his or her needs. Data also show that how students communicate about consent is influenced by gender expectations. There are multiple complicating factors when determining consent, including alcohol consumption. Participants discussed not understanding how to navigate sexual encounters when one or both parties had been consuming alcohol. Results also showed that there are multiple factors that may lead women to say yes to sex they don’t want, and men to not ask for consent. For college students, consent is a complex concept—a concept they may not have a practical and useful definition of. Sexual assault prevention must take steps to provide college students with a definition of consent informed by their experiences and the reality of their sexual encounters.
The challenge of multiple cardiovascular risk factor control outside Western Europe: Findings from the International ChoLesterol management Practice Study

Background Comprehensive control of multiple cardiovascular risk factors reduces cardiovascular risk but is difficult to achieve. Design A multinational, cross-sectional, observational study. Methods The International ChoLesterol management Practice Study (ICLPS) investigated achievement of European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) guideline low-density lipoprotein cholesterol (LDL-C) targets in patients receiving lipid-modifying therapy in countries outside Western Europe. We examined the rate of, and association between, control of multiple risk factors in ICLPS participants with dyslipidaemia, diabetes and hypertension (N = 2377). Results Mean (standard deviation) age of patients was 61.4 (10.4) years; 51.3% were male. Type 2 diabetes was the most common form of diabetes (prevalence, 96.9%). The prevalence of metabolic syndrome was 67.8%, obesity 40.4%, atherosclerotic disease 39.6% and coronary artery disease 33.5%. All patients were at high (38.2%) or very high (61.8%) cardiovascular risk according to ESC/EAS guidelines. Body mass index (BMI) was <25 kg/m2 in 20.3% of patients, 62.8% had never smoked and 25.2% were former smokers. Overall, 12.2% achieved simultaneous control of LDL-C, diabetes and blood pressure. Risk factor control was similar across all participating countries. The proportion of patients achieving individual guideline-specified treatment targets was 43.9% for LDL-C, 55.5% for blood pressure and 39.3% for diabetes. Multiple correspondence analysis indicated that control of LDL-C, control of blood pressure, control of diabetes, BMI and smoking were associated. Conclusion Comprehensive control of multiple cardiovascular risk factors in high-risk patients is suboptimal worldwide. Failure to control one risk factor is associated with poor control of other risk factors.
Stroop-like animal size test: Links with child effortful control, personality and problem behavior

ABSTRACT This study examined children’s performance on the Stroop-like animal size test and its relations to parent-reported temperamental effortful control, personality, and common emotional and behavioral problems in a Russian sample of 5-12-year-olds (N = 202). The animal size test demonstrated a Stroop-like effect for accuracy and response time (RT) in both genders and across all ages. Children’s performance on the animal size test considerably improved with age such that older children performed more accurately, were faster and their responses were less variable than younger children’s responses. The findings indicated that RT was negatively related to temperamental trait of effortful control and its key components, attention focusing and inhibitory control, whereas RT variability was negatively related to both regulatory traits, effortful control and conscientiousness, and their components. Children’s performance on the AST was meaningfully related to hyperactivity-inattention, externalizing behavior and overall level of childhood psychopathology. These findings provide support for the usefulness of the animal size test in the neuropsychological assessment of preschool and school-age children.
Roles of the INO80 and SWR1 Chromatin Remodeling Complexes in Plants

Eukaryotic genes are packed into a dynamic but stable nucleoprotein structure called chromatin. Chromatin-remodeling and modifying complexes generate a dynamic chromatin environment that ensures appropriate DNA processing and metabolism in various processes such as gene expression, as well as DNA replication, repair, and recombination. The INO80 and SWR1 chromatin remodeling complexes (INO80-c and SWR1-c) are ATP-dependent complexes that modulate the incorporation of the histone variant H2A.Z into nucleosomes, which is a critical step in eukaryotic gene regulation. Although SWR1-c has been identified in plants, plant INO80-c has not been successfully isolated and characterized. In this review, we will focus on the functions of the SWR1-c and putative INO80-c (SWR1/INO80-c) multi-subunits and multifunctional complexes in Arabidopsis thaliana. We will describe the subunit compositions of the SWR1/INO80-c and the recent findings from the standpoint of each subunit and discuss their involvement in regulating development and environmental responses in Arabidopsis.
Hardware and Software Platform of an Intellectual Access Monitoring and Control System of an Enterprise

The uniform structure of the hardware-software platform is offered and developed for an intellectual control and management system, monitoring the access for people and technical objects, solving the normal and abnormal situations tasks. It is proposed to make the intellectual AMCS of two parts according to the separation principle. With the help of the first part the task of the observation object state assessment is solved. The second block is the determined suboptimal regulator which is offered to be implemented by means of the fuzzy logic system. An original mathematical model of the intellectual access monitoring and control system is worked out. It allows taking into consideration abnormal situations. The illustrating examples using the natural intelligent AMCS model and the demonstration stand are given.
Framework of Industrial Robot System Programming and Management Software

As the increase in task complexity and precision of modern industrial robots with new performance or technologies, new programming and management software are demanded in a complete general robot control software system which provides two types of human-computer interaction platforms: teaching pendant and integrated development environment for field operators and off-line programmers respectively. As a subset of the robot integrated development environment, a new framework of the programming and management software, which combines the generality of universal robot middleware and convenience of special integrated development environments, is presented in this paper. This software aims at adapting the requirement of a general industrial robot controller and providing friendly interactive experience. Firstly, the framework of the cross-platform software system is displayed. Then the implementation of some components such as robot parameter configuration and management, PLC editor, is presented. And finally a practical example of this software is shown experiments.
Color- and Gradient-Related Visual Dictionary Models for Discrimination of Glycine Max (L.) Merrill Quality

Glycine max (L.) Merrill is one of the most important economic and food crops in the world. Defective rate is an important indicator affecting Glycine max (L.) Merrill surface quality factors. In this study, we introduce two kinds of color- and gradient-related visual dictionary models for discrimination of Glycine max (L.) Merrill quality. The RGB color components is firstly transferred to L*a*b* color domain. The speeded-up robust features (SURF) is performed to obtain the local gradient-related characteristic representation. The KNN clustering method is individually performed to cluster the global L*a*b* color features and local gradient-related SURF features. The clustering centers can be viewed as visual words. All the visual words are collected to generate the visual dictionary. The visual clustering features are finally discriminated by the support vector machine algorithm. The result shows that the gradient-related model achieves higher discrimination accuracy than the color-related visual dictionary model.
CNN-Based Cognitive Radar Array Selection

In cognitive radar, it may be desired to select an optimal subarray from a full antenna array in each scan to reduce the cost and computational complexity. Previous works on antenna selection rely on mostly optimization or greedy search methods. In this paper, we introduce a deep learning approach for antenna selection in a cognitive radar scenario. We design a deep convolutional neural network (CNN) to select the best subarray for direction-of-arrival estimation for each scan. The CNN accepts the array covariance matrix as its input and, unlike previous works, does not require prior knowledge about the target location. The performance of the proposed CNN approach is evaluated through numerical simulations. In particular, we show that it provides more accurate results than conventional support vector machines.
Constructing a One-time and a Few-time Digital Signature Schemes from the Hardness of Megrelishvili Vector-Matrix Problem

In this paper we develop a one-time and a few-time digital signature schemes by modifying the process of the authenticated Megrelishvili key exchange. The security of our schemes is based on the hardness of the Megrelishvili Vector-Matrix Problem (MVMP). We prove the correctness of our schemes mathematically and argue that they are at least as secure as the authenticated Megrelishvili protocol. Moreover, we demonstrate that both signature creation and verification take polynomial amount of finite field operations. Our few-time signature scheme allows a restriction to the message owner so that he can only certify at most n messages whenever the vector space $\mathbb{F}_q^n$ is used. We consider this feature valuable when we want to limit the number of messages that can be issued by the message owner.
A MatLab-Based Mean-Field-Type Games Toolbox: Continuous-Time Version

In this paper, we present a user-friendly MatLab-based Mean-Field-Type Games (MFTG) Toolbox that allows simulating a diversity of scalar-valued and matrix-valued MFTG problems for an arbitrary number of players, e.g., non-cooperative, fully-cooperative and co-opetitive approaches. We present details of each one of the developed tools together with the corresponding pseudo codes, and several illustrative examples. In addition, we provide the 20 functions composing the MFTG toolbox to freely download.
A Comparative Study of Hollywood Movie Successfulness Prediction Model

The movie industry is a highly competitive industry with a lot of new movies are queued to be released each year. Movie making is subject to potential profits or loss in the magnitude of billion of dollars making this industry very risky. Predicting the successfulness of movie based on its financial performance prior to the release date is valuable in order to reduce number of uncertainties faced by decision makers such as producers, distributors, and exhibitors. Using the concept of machine learning, we suggest a classification model to predict the successfulness of a Hollywood Movie using Artificial Neural Network, Naïve Bayes and Support Vector Machine. The objective of this research is to compare classification algorithms performance for predicting the successfulness of Hollywood movies before they are being released. Artificial Neural Network produces the best model in terms of performance in predicting a movie successfulness. Reaching 80% of accuracy and having above 80% of F-measure, precision, and recall suggests that Artificial Neural Network is a good model to assist producers, distributors and exhibitors assess risks.
Modelling Augmented Congklak using Cycle of Digital Augmentation Method

Traditional Congklak is considered capable for practicing psychomotor and cognitive abilities through their playing activities. Although Congklak is considered capable of exercising this ability, there are some difficulties that can interfere with the gameplay. Children need to be helped to overcome these difficulties, so they could play more effectively. In this study, the Cycle of Digital Augmentation is used to modeling the augmented Congklak by adding visual feedback technology to ease children's activities while playing Congklak, while keep maintaining their traditional ways of playing so that the benefits to practice psychomotor and cognitive abilities will be maintained. After that, the Relative Manipulation Time (RMT) method is used to measure the value of the interaction effectiveness. Compared to traditional Congklak the value of interaction effectiveness has increased by 21.81%. These results indicate that children are more effective interacting with game objects using augmented Congklak than using traditional Congklak.
Phase-Based Target Classification Using Neural Network in Automotive Radar Systems

In this paper, phase-based target classification using a neural network in automotive radar systems is proposed. The signals received at the array antenna contain amplitude and phase information whose values vary depending on the type of the target. In this study, the phase information of the signals reflected from pedestrians and vehicles are mainly considered as a classification criterion. Based on the phases of received signals, the phase difference is calculated by subtracting the phases of the signals received at each antenna element to adjacent antenna element. After extracting the phase and phase difference, the two different data sets are used as inputs to distinguish between the pedestrian and vehicle. Moreover, a deep neural network is used for the classification. After determining the proper number of nodes in a hidden layer and the number of hidden layers for the network, the performance of the proposed classification is evaluated. From our measured data, the proposed method with deep neural network by setting the number of nodes and hidden layers as 25 and 1 can classify the pedestrian and vehicle with more than 90% accuracy, regardless of which of the two input types is used.