Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of the Role of Adipokines in Cardiometabolic Diseases

Obesity as an independent risk factor for cardiovascular diseases (CVDs) leads to an increase in morbidity, mortality, and a shortening of life span. The changes in heart structure and function as well as metabolic profile are caused by obese people, including those free of metabolic disorders. Obesity alters heart function structure and affects lipid and glucose metabolism, blood pressure, and increase inflammatory cytokines. Adipokines, specific cytokines of adipocytes, are involved in the progression of obesity and the associated co-morbidities. In the current study, we review the scientific evidence on the effects of obesity on CVDs, focusing on the changes in adipokines. Several adipokines have anti-inflammatory and cardioprotective effects comprising omentin, apelin, adiponectin, and secreted frizzled-related protein (Sfrp-5). Other adipokines have pro-inflammatory impacts on the cardiovascular system and obesity including leptin, tumor necrosis factor (TNF), retinol-binding protein4 (RBP-4), visfatin, resistin, and osteopontin. We found that obesity is associated with multiple CVDs, but can only occur in unhealthy metabolic patients. However, more studies should be designed to clarify the association between obesity, adipokine changes, and the occurrence of CVDs.
HIV-1 Gag Forms Ribonucleoprotein Complexes with Unspliced Viral RNA at Transcription Sites

The ability of the retroviral Gag protein of Rous sarcoma virus (RSV) to transiently traffic through the nucleus is well-established and has been implicated in genomic RNA (gRNA) packaging Although other retroviral Gag proteins (human immunodeficiency virus type 1, HIV-1; feline immunodeficiency virus, FIV; Mason-Pfizer monkey virus, MPMV; mouse mammary tumor virus, MMTV; murine leukemia virus, MLV; and prototype foamy virus, PFV) have also been observed in the nucleus, little is known about what, if any, role nuclear trafficking plays in those viruses. In the case of HIV-1, the Gag protein interacts in nucleoli with the regulatory protein Rev, which facilitates nuclear export of gRNA. Based on the knowledge that RSV Gag forms viral ribonucleoprotein (RNPs) complexes with unspliced viral RNA (USvRNA) in the nucleus, we hypothesized that the interaction of HIV-1 Gag with Rev could be mediated through vRNA to form HIV-1 RNPs. Using inducible HIV-1 proviral constructs, we visualized HIV-1 Gag and USvRNA in discrete foci in the nuclei of HeLa cells by confocal microscopy. Two-dimensional co-localization and RNA-immunoprecipitation of fractionated cells revealed that interaction of nuclear HIV-1 Gag with USvRNA was specific. Interestingly, treatment of cells with transcription inhibitors reduced the number of HIV-1 Gag and USvRNA nuclear foci, yet resulted in an increase in the degree of Gag co-localization with USvRNA, suggesting that Gag accumulates on newly synthesized viral transcripts. Three-dimensional imaging analysis revealed that HIV-1 Gag localized to the perichromatin space and associated with USvRNA and Rev in a tripartite RNP complex. To examine a more biologically relevant cell, latently infected CD4+ T cells were treated with prostratin to stimulate NF-κB mediated transcription, demonstrating striking localization of full-length Gag at HIV-1 transcriptional burst site, which was labelled with USvRNA-specific riboprobes. In addition, smaller HIV-1 RNPs were observed in the nuclei of these cells. These data suggest that HIV-1 Gag binds to unspliced viral transcripts produced at the proviral integration site, forming vRNPs in the nucleus.
Genre-based Emoji Usage Analysis and Prediction in Video Comments

Emojis can be seen as a visual language inserted in texts to express emotions, attitudes, and situations. It is widely used in computer-mediated communication, e.g., video comments. The emojis can express more detailed information beyond text information, and their usage can improve interlocutors’ communication efficiency and emotions. The latest advances in natural language processing and deep learning have made it possible for chatbots to automatically add emojis in their dialogue. Precisely predicting emojis to be added is very challenging, especially in the video comments, where the use of emojis is complex, subtle, and associated with the cultural characteristics of video genres, e.g., anime and dancing. In this article, we first construct a benchmark dataset Bilibili comments dataset with more than 3.9 million comments that contain emojis in the video-sharing website Bilibili and then statistically analyze features of emoji’s usage of video genre, comment content, and where an emoji appears in a sentence. According to the analyzed results and the gated recurrent unit (GRU) neural network, we propose a novel model of genre-based multitask GRU (GM-GRU) and its attention-added edition (GM- GRU+) to predict an emoji’s category and position in a video comment. Our experiment and evaluation show that the proposed method can significantly increase the accuracy of predicted emojis for video comments.
Synthesis of Fused Heterocyclic Diamidines for the Treatment of Human African Trypanosomiasis and Fluorescence Studies of Selected Diamidines

A class of linear diamidines was synthesized for the evaluation as a treatment of Human African Trypanosomiasis. These fused heterocyclic compounds are thiazole[5,4-d]thiazoles and are of interest because the parent compound, 2,5-Bis(4-amidinophenyl)-thiazolo[5,4-d]thiazole HCl salt, which is also called DB 1929, has exhibited a low nanomolar IC50 value against Trypanosoma brucei rhodesiense and has shown selectivity for binding to the human telomere G-quadruplex over that of DNA duplex. A fluoro and a methoxy derivative have been synthesized and are currently undergoing testing for activity and binding affinity. In addition, fluorescence studies of selected diamidines were done to study the effect of structural variation on fluorescence. This data is useful since it can determine what types of moieties are needed to yield a compound that will fluoresce in the higher wavelengths (500 nm and above) of the visible spectrum, which would be advantageous in determining the uptake of the drug in the trypanosome within the endemic areas of Africa with a simple microscope. INDEX WORDS: Organic synthesis, Heterocyclic chemistry, Human African Trypanosomiasis, Sleeping sickness, Fused heterocycles, Linear diamidines, UV-Visible spectroscopy, Fluorescence spectroscopy, Trypanosoma brucei gambiense, Trypanosoma brucei rhodesiense SYNTHESIS OF FUSED HETEROCYCLIC DIAMIDINES FOR THE TREATMENT OF HUMAN AFRICAN TRYPANOSOMIASIS AND FLUORESCENCE STUDIES OF SELECTED DIAMIDINES
Differentially Coplanar-Fed Antenna for Millimeter-Wave Applications based on Integrated Substrate Gap Waveguide

In this letter, a differentially coplanar-fed antenna using integrated substrate gap waveguide (ISGW) is proposed for millimeter-wave applications, which has high gain and low cross polarization. The antenna consists of the differential power divider and radiation patch, and the power divider is deployed to excite the differential feeding antenna. The differential power divider is designed using rectangular-shaped coupling slot to achieve a stable 180° phase imbalance and 15% fractional operating bandwidth. The differentially coplanar-fed cross-shaped patch antenna has high gain and good radiation patterns. A fractional bandwidth of about 8.3% at the center frequency of 24 GHz and a peak gain of 10 dBi are obtained.
Assessing the analytical efficacy of TEX in diagnosing oral cancer using a systematic review approach.

The 5-year survival rates in OSCC depends on the stage at diagnosis. Patients have better survival and favorable outcomes if detected early, as compared to those diagnosed in advanced stages. Apart from biopsy and mucosal scraping examinations, exosomes from saliva and blood are emerging as an accessible source for diagnosis and providing additional information about the tumor's characteristics. Hence, the study of tumors-derived exosomal (TEX) biomarkers obtained from a liquid biopsy is emerging as a promising diagnostic tool.In this systematic review our effort is to assess the role of TEX as a biomarker.
Designed nanomolar small-molecule inhibitors of Ena/VASP EVH1 interaction impair invasion and extravasation of breast cancer cells

Significance Protein–protein interactions mediated by proline-rich motifs are involved in regulation of many important signaling cascades. These motifs belong to the most abundant recognition motifs in the eukaryotic genome and preferentially adopt a left-handed polyproline helix II, a secondary structure element that has been notoriously difficult to mimic with small molecules. Here, we present a structure-guided design effort yielding a toolkit of chemical entities that enables rational construction of selective small molecule inhibitors for these protein domains. We succeeded in developing an inhibitor for the Ena/VASP protein family that is active in vivo and reduces extravasation of invasive breast cancer cells in a zebrafish model. Battling metastasis through inhibition of cell motility is considered a promising approach to support cancer therapies. In this context, Ena/VASP-depending signaling pathways, in particular interactions with their EVH1 domains, are promising targets for pharmaceutical intervention. However, protein–protein interactions involving proline-rich segments are notoriously difficult to address by small molecules. Hence, structure-based design efforts in combination with the chemical synthesis of additional molecular entities are required. Building on a previously developed nonpeptidic micromolar inhibitor, we determined 22 crystal structures of ENAH EVH1 in complex with inhibitors and rationally extended our library of conformationally defined proline-derived modules (ProMs) to succeed in developing a nanomolar inhibitor (Kd=120 nM,MW=734 Da). In contrast to the previous inhibitor, the optimized compounds reduced extravasation of invasive breast cancer cells in a zebrafish model. This study represents an example of successful, structure-guided development of low molecular weight inhibitors specifically and selectively addressing a proline-rich sequence-recognizing domain that is characterized by a shallow epitope lacking defined binding pockets. The evolved high-affinity inhibitor may now serve as a tool in validating the basic therapeutic concept, i.e., the suppression of cancer metastasis by inhibiting a crucial protein–protein interaction involved in actin filament processing and cell migration.
Muscle Strength and Physical Performance in Patients Without Previous Disabilities Recovering From COVID-19 Pneumonia

Supplemental digital content is available in the text. Abstract In this cross-sectional study, we evaluated skeletal muscle strength and physical performance (1-min sit-to-stand and short physical performance battery tests), dyspnea, fatigue, and single-breath counting at discharge from a postacute COVID department, in patients recovering from COVID-19 pneumonia who had no locomotor disability before the infection. Quadriceps and biceps were weak in 86% and 73% of the patients, respectively. Maximal voluntary contraction for quadriceps was 18.9 (6.8) kg and for biceps 15.0 (5.5) kg (i.e., 54% and 69% of the predicted normal value, respectively). The number of chair rises in the 1-min sit-to-stand test was 22.1 (7.3 corresponding to 63% of the predicted normal value), whereas the short physical performance battery score was 7.9 (3.3 corresponding to 74% of the predicted normal value). At the end of the 1-min sit-to-stand test, 24% of the patients showed exercise-induced desaturation. The single-breath counting count was 35.4 (12.3) corresponding to 72% that of healthy controls. Mild-to-moderate dyspnea and fatigue were found during activities of daily living (Borg scale score, median value = 0.5 [0–2] and 1 [0–2]) and after the 1-min sit-to-stand (Borg scale score, median value = 3 [2–5] and 1 [0–3]). Significant correlations were observed between muscle strength and physical performance indices (R = 0.31–0.69). The high prevalence of impairment in skeletal muscle strength and physical performance in hospitalized patients recovering from COVID-19 pneumonia without previous locomotor disabilities suggests the need for rehabilitation programs after discharge.
MESO-TETRA-p-TOLYLPORPHYRIN: SYNTHESIS, CHARACTERISATION, ANTIOXIDANT ACTIVITY AND DETERMINATION OF BINDING PARAMETERS OF WITH SUPEROXIDE ANION RADICAL

In this paper we present the synthesis and the evaluation of the antioxidant activity of meso-tetra-p-tolylporphyrin (TPPH2(p-methyl)) using superoxide anion radical in DMF as a solvant. The binding parameters of the interaction between the synthesised compound and the radical were also measured using cyclic voltammetry techniques. TPPH2(p-methyl) shows lower antioxidant activity (30.7 ± 0.08 mg/mL) than that of α-tocopherol standard (3.04 ± 0.03 mg/mL). The value of the binding Gibbs energy of -8.15 KJ.mol-1 calculated for TPPH2(p-methyl) suggests an electrostatic interaction of with TPPH2(p-methyl) which has been found to be the dominant interaction mode. The kinetics of the interaction reaction was also mesured having second-order rate constant values equal to 0.2 M-1 s-1.
Improve monitoring system of interconnected oscillation power system based on PMU technology

Interconnected power systems have a very important role between several countries, the conditions of interconnections are strict in order to ensure the good quality of energy transmitted or received. The majority of the technologies that have been installed for system monitoring are based on the SCADA system, the latter does not give good results in real time and the error deviation is significant. This paper proposes a solution for the supervision problem based on the SCADA system; by using PMU technology to measure multiple parameters both with better accuracy and in real time. To validate the proposed approach, the developed PMU based monitoring system is implemented using Simulink/Matlab. Then, its performance is tested by the power system Simulink model under different operating conditions.
Providing Facilities in Health Care via Brain-Computer Interface and Internet of Things

Internet of Things (IoT) technologies are steadily growing. Millions of devices are now connected to each other and the internet; they communicate and share data. Another technology that has been taking traction is Brain-Computer Interface (BCI), which get brain signals and convert them into commands that are employed for control of different applications. There is then a clear link between IoT and BCI: the IoT device can receive the commands generated by the BCI and perform the desired action. This combination is useful in health care for supporting disabled patients and old people to have more autonomy. This research aims to assess the combination of the BCI and IoT, specially focusing on health care to facilitate everyday life activities by developing simple home appliances such as door opening and turning on the lights. In the present study, three voluntary candidates participated the experiment of controlling a light based on BCI and IoT systems. The average accuracy results obtained 87.33% and 73.33% with ANOVA P < 0. 05 for offline and real-time processing modes. Our results indicates that the speed of IoT is fast enough for a BCI real-time system for the case under study.
Hierarchical Transformer Encoder With Structured Representation for Abstract Reasoning

Abstract reasoning is one of the defining characteristics of human intelligence and can be estimated by visual IQ tests such as Raven’s Progressive Matrices. In this paper, we propose using a hierarchical Transformer encoder with structured representation that employs a novel neural network architecture to improve both perception and reasoning in a visual IQ test. For perception, we used object detection models to extract the structured features. For reasoning, we used the Transformer encoder in a hierarchical manner that fits the structure of Raven’s Progressive Matrices. Experimental results on the RAVEN dataset, which is one of the major large-scale datasets on Raven’s Progressive Matrices, showed that our proposed architecture achieved an overall accuracy of 99.62%, which is an improvement of more than 8% points over CoPINet, the present-day, state-of-the-art neural network model.
Electrical Peak Load Clustering Analysis Using K-Means Algorithm and Silhouette Coefficient

Nowadays, data analysis widely used in many fields especially in engineering. Clustering is one of data analysis methods to organize the amount of data into groups with similarity characteristics. One powerful analysis method to learn information by grouping data is clustering algorithms. The clustering advantages for electrical power utilities is to learn load behavior and provide information for power plant operation and also generation cost. In this paper, a simulation concept is proposed for analysis of peak load data by K-means clustering algorithm based on historical dataset. The results show electrical peak loads clustering by K-means algorithm are optimum classified into three clusters. This cluster evaluated by silhouette scores which high, intermediate, and low load level interpretation. One cluster has centroid during January, June, and July are relatively lower than another cluster caused by Indonesia national holiday. This concept also evaluates the load level affected by Covid-19 pandemic condition.
Development of a Comprehensive Tool for School Health Policy Evaluation: The WellSAT WSCC

ABSTRACT BACKGROUND Stakeholders increasingly recognize the role of policy in implementing Whole School, Whole Community, Whole Child (WSCC) frameworks in schools; however, few tools are currently available to assess alignment between district policies and WSCC concepts. The purpose of this study was to expand the Wellness School Assessment Tool (WellSAT) for evaluation of policies related to all 10 domains of the WSCC model. METHODS Developing the WellSAT WSCC was an iterative process that involved (1) identifying items for each domain based on key concepts and best practice recommendations; (2) expert review of the draft measure; (3) cognitive pre‐testing; (4) developing scoring criteria; and (5) pilot‐testing the measure. RESULTS Ratings from expert reviewers indicated that the tool included items that were both relevant and important to each of the 10 WSCC domains. Results of cognitive pre‐testing indicated that the items were understood as intended. Feedback from expert reviews, cognitive pre‐testing, and pilot‐testing was used to further revise and refine the measure and develop a final version of the tool. Acceptable interrater reliability was established for the final selection of items. CONCLUSIONS The WellSAT WSCC provides a reliable means for assessing integration and alignment between WSCC model concepts and district policies.
A Novel Reliability Analysis Approach With Collaborative Active Learning Strategy-Based Augmented RBF Metamodel

Metamodels in lieu of time-demanding performance functions can accelerate the reliability analysis effectively. In this paper, we propose an efficient collaborative active learning strategy-based augmented radial basis function metamodel (CAL-ARBF), for reliability analysis with implicit and nonlinear performance functions. For generating the suitable samples, a CAL function is first designed to constrain the new samples being generated in sensitivity region, near limit state surface and keep certain distances mutually. Then by adjusting the adjustment coefficient of CAL function, the CAL-ARBF is mathematically modeled and the corresponding reliability analysis theory is developed. The effectiveness of the proposed approach is validated by four numerical samples, including global nonlinear problem, local nonlinear problem, nonlinear oscillator and truss structure. Through comparison of several state-of-the-art methods, the proposed CAL-ARBF is demonstrated to possess the computational advantages in efficiency and accuracy for reliability analysis.
Bare Versus Hair: Do Pubic Hair Grooming Preferences Dictate the Urogenital Microbiome?

Objective The aim of the study was to determine whether the genitourinary microbiome differs between and within women with and without pubic hair. Methods Premenopausal women who self-identified into 2 groups were recruited: in the “hair” group, women did not remove any pubic hair, and in the “bare” group, all pubic hair was removed routinely. Participants submitted a vaginal swab, a voided urine sample, and a catheterized urine sample, and then “crossed over. ” The “hair” group removed all hair for 1 month, and the “bare” group grew hair for 2 months. After crossover, participants again submitted a vaginal swab, a voided urine sample, and a catheterized urine sample. Ten participants acted as controls. DNA was extracted, and the V4 region of 16S rRNA gene was amplified and sequenced using the MiSeq platform. Paired-end sequences were imported into QIIME2-2018.6. Alpha diversity (the number and proportion of species in an individual sample) and beta diversity (differences in microbial composition between samples) were evaluated. Results Forty-two participants were analyzed: 16 “bare to hair” crossovers, 16 “hair-to-bare” crossovers, and 10 controls. The microbiome varied by sample type: vaginal swabs had the lowest alpha diversity and catheterized urine had the highest (P < 0.001). At baseline, there were no differences in the alpha or beta diversity of urine or vaginal microbiomes between groups. Vaginal beta diversity at visit 2 was greater within crossovers than controls (P = 0.004), suggesting that altering hair status alters the microbiome composition. Urinary beta diversity was not different at visit 2 (P = 0.40). Conclusions Pubic hair status does not determine one’s baseline genitourinary microbiome, but women who change their hair status may alter their vaginal microbiome.
Modeling of the Maximum Induced Currents in Automotive Radiated Immunity Tests via Thevenin-based Metamodels

This paper presents three different metamodels for the prediction of the maximum current induced on key vehicle electronic units during an automotive radiated immunity test. The proposed modeling approach is based on a Thevenin circuital interpretation of the test setup which is estimated from a small set of measurements or simulations. The FFT-based trigonometric regression, the support vector machine and the Gaussian process regression are then applied to provide three different metamodels able of predicting the spectrum of the induced currents for any value of the incidence angle of the external EM field. The accuracy and the convergence of the proposed alternatives are investigated by comparing model predictions with the results obtained by means of a parametric full-wave electromagnetic simulation.