Miracle-dz/UniFAR-FTUs huggingface dataset:
The TREM2 H157Y Variant Influences Microglial Phagocytosis, Polarization, and Inflammatory Cytokine Release

Previously, we reported that H157Y, a rare coding variant on exon 3 of the triggering receptor expressed on myeloid cells 2 gene (TREM2), was associated with Alzheimer’s disease (AD) risk in a Han Chinese population. To date, how this variant increases AD risk has remained unclear. In this study, using CRISPR-Cas9-engineered BV2 microglia, we tried to investigate the influence of the Trem2 H157Y variant on AD-related microglial functions. For the first time, we revealed that the Trem2 H157Y variant inhibits microglial phagocytosis of amyloid-β, promotes M1-type polarization of microglia, and facilitates microglial release of inflammatory cytokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor-α. These findings provide new insights into the cellular mechanisms by which the TREM2 H157Y variant elevates the risk of AD.
Identification of Key Genes Involved in Polycystic Ovary Syndrome in Obese Patients: A Bioinformatics Study

Women of reproductive age are commonly affected with Polycystic ovary syndrome (PCOS), a heterogeneous disorder. PCOS is associated chiefly with polycystic ovaries, hirsutism, and ovulation failure. Impact of obesity on phenotype of PCOS and reproductive functions has both genetic and environmental influences. Recently, many different research strategies deciphered the molecular mechanisms of PCOS, which includes in silico approaches also, such as high throughput RNA sequencing. Therefore, to find the Differential gene expression (DEGs), we will be using secondary clinical data of microarray datasets for clinical studies of PCOS patients. Our study showed vigorous gene signatures in obese PCOS conditions. It elucidates that the dysregulations of these genes were connected with the development and progression of obesity followed by PCOS. Furthermore, up-regulated genes FRZB, MYH1, RBM3, NMRK2, NANOS1, and IRX3 and down-regulated genes LDHB, LPL, SLC38Al, and MAFF as the hub genes might has important in the diagnosis and treatment for obesity-induced PCOS.
How Well the Constructs of Health Belief Model Predict Vaccination Intention: A Systematic Review on COVID-19 Primary Series and Booster Vaccines

This systematic review synthesizes the findings of quantitative studies examining the relationships between Health Belief Model (HBM) constructs and COVID-19 vaccination intention. We searched PubMed, Medline, CINAHL, Web of Science, and Scopus using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and identified 109 eligible studies. The overall vaccination intention rate was 68.19%. Perceived benefits, perceived barriers, and cues to action were the three most frequently demonstrated predictors of vaccination intention for both primary series and booster vaccines. For booster doses, the influence of susceptibility slightly increased, but the impact of severity, self-efficacy, and cues to action on vaccination intention declined. The impact of susceptibility increased, but severity’s effect declined sharply from 2020 to 2022. The influence of barriers slightly declined from 2020 to 2021, but it skyrocketed in 2022. Conversely, the role of self-efficacy dipped in 2022. Susceptibility, severity, and barriers were dominant predictors in Saudi Arabia, but self-efficacy and cues to action had weaker effects in the USA. Susceptibility and severity had a lower impact on students, especially in North America, and barriers had a lower impact on health care workers. However, cues to action and self-efficacy had a dominant influence among parents. The most prevalent modifying variables were age, gender, education, income, and occupation. The results show that HBM is useful in predicting vaccine intention.
The Role of Sperm Membrane Potential and Ion Channels in Regulating Sperm Function

During the last seventy years, studies on mammalian sperm cells have demonstrated the essential role of capacitation, hyperactivation and the acrosome reaction in the acquisition of fertilization ability. These studies revealed the important biochemical and physiological changes that sperm undergo in their travel throughout the female genital tract, including changes in membrane fluidity, the activation of soluble adenylate cyclase, increases in intracellular pH and Ca2+ and the development of motility. Sperm are highly polarized cells, with a resting membrane potential of about −40 mV, which must rapidly adapt to the ionic changes occurring through the sperm membrane. This review summarizes the current knowledge about the relationship between variations in the sperm potential membrane, including depolarization and hyperpolarization, and their correlation with changes in sperm motility and capacitation to further lead to the acrosome reaction, a calcium-dependent exocytosis process. We also review the functionality of different ion channels that are present in spermatozoa in order to understand their association with human infertility.
End-to-end brain tumor detection using a graph-feature-based classifier

Brain tumors are caused by abnormal cell growth and can cause pain and reduced survival rates. The early detection of brain tumors is pivotal in improving outcomes. Recently, magnetic resonance imaging (MRI) has been widely deployed in clinics to diagnose brain lesions non-invasively and prevent patients from receiving radiation doses of diagnostic imaging modalities. Traditionally, medical oncologists and radiologists diagnose brain tumors as benign or malignant using visual analysis of MRI images. The decision-making process is labor intensive, and relies on the expertise level of physicians. Recently, deep learning has dramatically changed the landscape of oncology by enabling automatic and accurate diagnosis. While the backbones of most state-of-the-art architectures are convolutional neural networks or vision transformers, the application of graph neural networks in radiation oncology has not yet been explored. To the authors' knowledge, this is the first demonstration of using fully-automated graph-feature-based classifiers for end-to-end brain tumor detection, indicating an overall classification accuracy of 94.89%. The proposed graph-feature-based classifiers are accessible for clinical implementation and could potentially assist radiation oncologists to precisely and accurately diagnose and prognosticate brain lesions.
Deep-Learning-Equipped Iterative Solution of Electromagnetic Scattering From Dielectric Objects

Deep-learning (DL)-equipped iterators are developed to accelerate the iterative solution of electromagnetic scattering problems. In proposed iterators, DL blocks consisting of U-nets are employed to replace the nonlinear process of the traditional iterators, i.e., the conjugate gradient (CG) method and the generalized minimal residual (GMRES) method. New implementations of the complex-valued batch normalization in the U-net are proposed and investigated in terms of the DL-equipped iterators. Numerical results show that the DL-equipped iterators outperform their traditional counterparts in terms of computational time under comparable accuracy since the phase information of the currents, fields, and permittivity is properly handled.
Enhanced Biosafety of the Sleeping Beauty Transposon System by Using mRNA as Source of Transposase to Efficiently and Stably Transfect Retinal Pigment Epithelial Cells

Neovascular age-related macular degeneration (nvAMD) is characterized by choroidal neovascularization (CNV), which leads to retinal pigment epithelial (RPE) cell and photoreceptor degeneration and blindness if untreated. Since blood vessel growth is mediated by endothelial cell growth factors, including vascular endothelial growth factor (VEGF), treatment consists of repeated, often monthly, intravitreal injections of anti-angiogenic biopharmaceuticals. Frequent injections are costly and present logistic difficulties; therefore, our laboratories are developing a cell-based gene therapy based on autologous RPE cells transfected ex vivo with the pigment epithelium derived factor (PEDF), which is the most potent natural antagonist of VEGF. Gene delivery and long-term expression of the transgene are enabled by the use of the non-viral Sleeping Beauty (SB100X) transposon system that is introduced into the cells by electroporation. The transposase may have a cytotoxic effect and a low risk of remobilization of the transposon if supplied in the form of DNA. Here, we investigated the use of the SB100X transposase delivered as mRNA and showed that ARPE-19 cells as well as primary human RPE cells were successfully transfected with the Venus or the PEDF gene, followed by stable transgene expression. In human RPE cells, secretion of recombinant PEDF could be detected in cell culture up to one year. Non-viral ex vivo transfection using SB100X-mRNA in combination with electroporation increases the biosafety of our gene therapeutic approach to treat nvAMD while ensuring high transfection efficiency and long-term transgene expression in RPE cells.
A Common Mode Insensitive Process Tolerant Sense Amplifier Design for In Memory Compute Applications in 65nm LSTP Technology

Data transfer between processor and memory hierarchy constrains throughput of applications like Convolutional Neural Networks (CNNs) due to Von-Neumann bottleneck. In-Memory Compute (IMC) bypasses this bottleneck and enables much higher throughputs at constrained power budgets. Mixed Signal IMC SRAM architectures involve multiple wordline selection to enhance throughput and energy efficiency. Consequently bitlines discharge to much lower levels than a conventional SRAM. Conventional Sense Amplifiers (SAs) associated with bitlines are designed to operate at high common mode voltage and their offset increases as common mode voltage is reduced. In this work, we propose a common mode insensitive SA (CMI-SA) that has offset and delay that are independent of common mode voltage. The CMI-SA proposed in this work has a 3-sigma offset of 20–25 mV and a output delay of 200ps across a common mode range of 0.4V to 1.2V in an industrial 65nm Low Standby Power (LSTP) technology.
Pediococcus acidilactici pA1c® Improves the Beneficial Effects of Metformin Treatment in Type 2 Diabetes by Controlling Glycaemia and Modulating Intestinal Microbiota

Type 2 diabetes (T2D) is a complex metabolic disease, which involves maintained hyperglycemia, mainly due to the development of an insulin resistance process. Metformin administration is the most prescribed treatment for diabetic patients. In a previously published study, we demonstrated that Pediococcus acidilactici pA1c® (pA1c) protects from insulin resistance and body weight gain in HFD-induced diabetic mice. The present work aimed to evaluate the possible beneficial impact of a 16-week administration of pA1c, metformin, or the combination of pA1c and metformin in a T2D HFD-induced mice model. We found that the simultaneous administration of both products attenuated hyperglycemia, increased high-intensity insulin-positive areas in the pancreas and HOMA-β, decreased HOMA-IR and also provided more beneficial effects than metformin treatment (regarding HOMA-IR, serum C-peptide level, liver steatosis or hepatic Fasn expression), and pA1c treatment (regarding body weight or hepatic G6pase expression). The three treatments had a significant impact on fecal microbiota and led to differential composition of commensal bacterial populations. In conclusion, our findings suggest that P. acidilactici pA1c® administration improved metformin beneficial effects as a T2D treatment, and it would be a valuable therapeutic strategy to treat T2D.
Load forecast based on GARCH model with non-Gaussian distribution

A short-term load forecast method based on GARCH(Generalized AutoRegressive Conditional Heteroscedasticity) model with non -Gaussian distributions is proposed.Based on the discussion of ARCH effect existence,the Gaussian conditional distribution assumption of standard GARCH model is generalized to non -Gaussian alternative distribution(t distribution,generalized error distribution). The model parameter estimations are obtained by Maximum Likelihood Estimation and the improved GARCH models with non -Gaussian distribution,such as GARCH -t and GARCH -GED,are established.The forecast performances are compared among ARMA,standard GARCH,GARCH-t and GARCH-GED,and their mean forecast errors and maximum forecast errors are analyzed,which indicate GARCH-GED is superior to other models.Calculation example shows that the load forecast method based on GARCH model with non -Gaussian distribution is efficient and feasible.
Time Perspective Latent Profile Analysis and Its Meaning for School Burnout, Depression, and Family Acceptance in Adolescents

This study aimed to apply latent profile analysis (LPA) to (a) empirically identify adolescents’ profiles based on their time perspective (TP), using a person-centered approach; (b) outline the identified profiles across student burnout, depression, and perceived family acceptance; and (c) establish differences between pre-COVID-19 and post-COVID-19 students. Cross-sectional data were collected through an online survey of 668 adolescents. The participants completed the Kutcher Adolescent Depression Scale (KADS), Student School Burnout Scale (SSBS), Time Perspective Inventory (TPI), and Perceived Family Acceptance (PFA) items. Five subtypes of TP were found: hedonistic youth focused mainly on the present time; hedonistic youths focused on the present and future time; fatalistic youths focused on the present and negative past time; future-oriented youths with a positive perception of the past; and hedonistic youths focused on the present with a mild past-negative time perspective. These five profiles were compared for the variables of student burnout, depression, and perceived family acceptance. Scores from SSBS, KADS, and PFA showed a statistical difference across the five subtypes, with the most intensive mental health, social, and educational problems in profile 5. The level of SSBS was significantly different in the pre-COVID-19 and post-COVID-19 samples; however, no significant differences were found in KADS and PFA. Thus, perspective should be emphasized in adolescents with burnout and depression symptoms.
Tree competition indexes based on weighted Voronoi diagram.

We took the 25-year-old Cunninghamia lanceolata ecological forest in Fushoushan National Forest Park of Hunan Province as the research object. Analysis revealed that there was a negative correlation between competition index and the diameter at breast height( DBH). Based on this,it was confirmed that the weight of weighted Voronoi diagram was the reciprocal of DBH. Competition unit was determined by using weighted Voronoi diagram tool in Arcgis software,and the W_ V_ Hegyi competition index was proposed based on weighted Voronoi diagram. Finally,the comparative analysis among Hegyi,V_Hegyi and W_ V_ Hegyi competition indexes revealed that: 1) the correlation between competition index and DBH ranked from strong to weak as W_V_Hegyi V_Hegyi Hegyi; 2) the relationship between the three competition indexes and DBH followed the power function,and the order of curve fitting degree was in W_V_Hegyi V_Hegyi Hegyi; 3) the sorting of average competition index in sample plots was in V_Hegyi W_V_Hegyi Hegyi. The above comparative analysis proves that W_V_Hegyi competition index is more accurate than Hegyi and V_Hegyi in reflecting the competitive relationship among trees.
Research on keywords extraction technology in intelligent evaluation scheme for airport emergency plan

Airport emergency plan is difficult for airport security professionals to write because of its professionalism and high standardization. At the same time, the evaluation of airport emergency plan also consumes a lot of time and energy for airport security professionals. This paper proposes an intelligent evaluation method for airport emergency plans, in detail, first, it extracts the important indicators related to chapters of emergency plan from existing classic emergency plans, then it hits these indicators with the plans written by professionals, formulates hit rules, and at last it obtains the scores of evaluated plans in each indicator. According to evaluation results, the emergency plan is gradually improved. In process of emergency plan evaluation, keywords extraction technology is essential, because keywords can more intensively express the indicators of plans, it also can improve the accuracy of hit efficiency, and it can speed up the evaluation process. Therefore, this paper studies keywords extraction technology of airport emergency plan based on machine learning to help emergency plan writers save time and energy. The experimental results show that TF-IDF extraction technology has obvious advantages over other technologies in intelligent evaluation of emergency plans.
Modeling and Inverse Kinematics of Kuka Manipulator

In this paper the focus will be on the kinematics and inverse kinematics modeling for seven degree’s of freedom Kuka manipulator robotic arm. Robot kinematics refers to the analytical study of the motion of a robot manipulator. Formulating the suitable kinematics models for a robot mechanism is very crucial for analyzing the behavior of industrial manipulators. The formulation of the problem is based on finding the D-H parameters of the arm. The inverse kinematics problem of the system is redundant. This problem has no general closed form solutions or analytical solutions for the 7-DOF manipulator. The DH convention method is used to form the kinematical equation of the resultant structure.
N-Polar GaN HEMTs in a High-Uniformity 100-mm Wafer Process With 43.6% Power-Added Efficiency and 2 W/mm at 94 GHz

We report the fabrication of N-polar GaN metal–insulator– semiconductor high-electron-mobility transistors (MIS-HEMTs) with a power-added efficiency (PAE) of 43.7% from an 8-V supply measured by load pull at 94 GHz. The devices are fabricated on 100-mm SiC substrates and exhibit excellent uniformity, constituting the first report on $W$ -band power performance across a 100-mm N-polar GaN sample. Compared with the available efficiency of N-polar GaN on SiC at 94 GHz, the devices presented here demonstrate among the highest PAE performance, while delivering a power density of 2 W/mm, competitive with highly scaled, and previously reported both Ga and N-polar devices using an 8-V power supply.
Application of mutual information and improved support vector machine in power load forecasting

So as to improve the accuracy of power load-forecast, the author first uses mutual information to determine the correlation between power load and other factors, and then uses Skyhawk optimization algorithm to optimize the kernel function and penalty coefficient in the support vector machine, which is verified by experiments. The results show that the power load has a strong correlation with temperature and atmospheric pressure, and the mutual information values are 1.5638 and 1.1042 respectively; Taking these two variables as the input characteristics of the forecasting model, the R2 value of power load-forecast is above 0.9, and good results are achieved. This research method provides a new idea for power load-forecast.
Performance measurement model for technical universities – case study

PurposeThe main purpose of this paper is twofold: to present a proposal for a model of educational quality management system within a process approach context for technical universities, and a conceptual model of a performance measurement system (PMS) towards the assessment of the quality level of management, core and support processes.Design/methodology/approachThe paper encompasses two main parts: a theoretical portion and a case study. Within the theoretical background, the authors discuss the issue of educational quality management supported on a process approach perspective as well as performance measurement system in high education (HE). The case study reports the development of the concept of performance measurement system for technical universities.FindingsThe proposed system of educational quality management supported on a process approach, together with a conceptual model of the performance measurement system, can be implemented in every technical university. The identification of processes in the education quality management system permitted the development of the PMS. The model covers 32 key performance indicators (KPIs) for management processes, 39 for core processes and 19 for supporting ones.Research limitations/implicationsThe proposed performance measurement system is limited in its focus on educational processes and support of these processes. The evaluation of scientific and research activity and aspects related to financial resources is not pursued.Originality/valueElaboration of a conceptual model of a performance measurement system towards the assessment of the quality level of management, core and support processes is dedicated to technical universities.
Direct Modulation Bandwidth Improvement in Two-section DFB Lasers Based on the Detuned Loading Effect

A directly modulated two-section distributed feedback (TS-DFB) semiconductor laser is proposed and investigated. The reconstruction-equivalent-chirp (REC) technique is used to realize the detuning of the gratings in the two sections. The modulation bandwidth is improved by the detuned loading effect. The device has potential applications in optical communications and microwave photonics.
Characterization of fluorescence lifetime of organic fluorophores for molecular imaging in the shortwave infrared window

Abstract. Significance Fluorescence lifetime imaging in the shortwave infrared (SWIR) is expected to enable high-resolution multiplexed molecular imaging in highly scattering tissue. Aim To characterize the brightness and fluorescence lifetime of commercially available organic SWIR fluorophores and benchmark them against the tail emission of conventional NIR-excited probes. Approach Characterization was performed through our established time-domain mesoscopic fluorescence molecular tomography system integrated around a time-correlated single-photon counting-single-photon avalanche diode array. Brightness and fluorescence lifetime were measured for NIR and SWIR probes >1000 nm. Simultaneous probe imaging was then performed to assess their potential for multiplexed studies. Results The NIR probes outperformed SWIR probes in brightness while the mean fluorescence lifetimes of the SWIR probes were extremely short. The phantom study demonstrated the feasibility of lifetime multiplexing in the SWIR window with both NIR and SWIR probes. Conclusions Long-tail emission of NIR probes outperformed the SWIR probes in brightness >1000 nm. Fluorescence lifetime was readily detectable in the SWIR window, where the SWIR probes showed shorter lifetimes compared to the NIR probes. We demonstrate the feasibility of lifetime multiplexing in the SWIR window, which paves the way for in vivo multiplexed studies of intact tissues at improved resolution.