Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Catalpol on Alzheimer's Disease and Its Mechanisms

Alzheimer's disease (AD) is a degenerative disease of the central nervous system characterized by memory loss and cognitive dysfunction. With the increasing aging of the population, the incidence of AD and the number of patients are also increasing year by year, causing more and more heavy burdens to the family and society. Catalpol, an iridoid glycoside compound, is one of the main active components of Rehmannia glutinosa. At present, a large number of experimental studies in vivo and in vitro have confirmed that catalpol has antioxidant, anti-inflammatory, antiapoptotic, and other neuroprotective effects, and it plays a significant role in the prevention and treatment of AD, with very small side effects and high safety. Therefore, it may be an ideal drug for the treatment of AD. Based on this, the role and mechanism of catalpol in AD will be comprehensively reviewed in the following.
The usefulness of inner ear magnetic resonance imaging in patient with Ménière’s disease: A narrative review

Ménière’s disease (MD) is a multifactorial disorder with typical symptoms of recurrent vertigo, tinnitus, fluctuating hearing loss, and sensations of ear fullness. This disease greatly reduces the quality of life for patients. Unfortunately, it is difficult to diagnose and predictthe prognosis using only diagnostic methods, including audiometry. Therefore, since the mid-2000s, various efforts have been made to directly identify endolymphatic hydrops (EH), a histologic hallmark of MD, through magnetic resonance imaging (MRI) of the inner ear. Various studies have revealed significant correlation among degree of EH on inner ear MRI, patient symptoms, and test results. Although there are some limitations, inner ear MRI is expected to be widely used for differential diagnosis of MD, recurrent low-frequency hearing loss, non-specific vertigo, and vestibular migraine. In addition, as an automated analysis system of EH using the convolutional neural network algorithm has been developed,the usefulness of inner ear MRI is increasing. This algorithm can generate results that are highly consistent with those generated by manual calculation and can do so more quickly. Although there are some limitations to be overcome, inner ear MRI is expected to be widely used for differential diagnosis of various EH-related diseases in the not-too-distantfuture.
VIBRATION ANALYSIS OF DISCRETE ONE AND TWO DEGREE OF FREEDOM SYSTEMS

The two systems studied in this lab represent examples of the simplest dynamical systems which might be encountered in engineering practice. Although each system is composed of continuous elements (spring steel strips, and aluminum blocks), the nature in which theses elements are combined allows the combination to be modeled as a discrete system[1]. That is, a system whose motion may be accurately described in terms of a finite number of discrete degrees of freedom. In spite of the simplicity of these systems, however, much useful information can be gained from their study. In fact, the behavior of continuous (infinite degree of freedom systems) can usually be well modeled by the behavior of a finite number of discrete one-degree-of-freedom (DOF) systems. This is the principle behind modal analysis, which is used constantly in industry to minimize noise emitted by, or prevent fatigue failure in structures.
Deciphering of a Putative GPER Recognition Domain in ERα and ERα36

Twenty-five years ago, a class A (rhodopsin-like) G protein-coupled receptor (GPCR) was reported to participate in the rapid physiological responses to the mammalian steroid hormone 17b-estradiol (E2). Initially found in human B cells (1), it was further identified in breast carcinoma cells (2). As no endogenous ligand was identified, this membrane receptor was called GPR30, following the numbering system of orphan receptors. In 2005, two independent groups demonstrated an E2 interaction with GPR30 characterized by a Kd value in the nanomolar range (3, 4). Thus, it was renamed GPER for G protein-coupled estrogen receptor. The GPER structure differing strongly from the canonical estrogen receptors ERa and b and the 46 kDa ERa truncated isoform (ERa46), impassioned debates focusing on its intracellular localization, its exact role regarding nuclear and membrane signaling pathways and its physical interaction with ERa were carried out (5, 6). Shortly afterwards, a direct interaction of GPER with activated ERa and the epidermal growth factor receptor (EGFR), which both support transactivation, was evidenced through biochemical methods (7–9). GPER participating in breast cancer development including triple negative breast cancer (TNBC) and in tamoxifen resistance (10–12), it would be interesting to define the ERa region involved in ERa/GPER interaction. Such a finding could lead to innovative therapeutic strategies targeting aggressive breast tumors. Based on recently published data, we report here a putative GPER-binding domain located in the hinge region of ERa and also found in the 36 kDa ERa truncated isoform (ERa36).
Properties of Mechatronic System for Hand Rehabilitation

The article describes an innovative mechatronic device for hand rehabilitation, which enables diagnostics, comprehensive exercises and reporting the rehabilitation results of individual fingers of people who have lost their full efficiency as a result of past illnesses (i.a. stroke) and orthopedic injuries. The basic purpose of the device is to provide controlled, active exercises of the individual fingers, to widen the range of their movements, and to increase their precision of movement. The developed mechatronic device works with original software for PCs containing a diagnostic module, reporting module and a set of virtual reality exercises using biofeedback. The device uses auditory and visual biofeedback, and electromyography (EMG).
Cross-Layer Resource Management for Downlink BF-NOMA-OFDMA Video Transmission Systems and Supervised/Unsupervised Learning Based Approach

Ali et al. proposed a physical (PHY) layer resource management for single-carrier N-antenna beamforming (BF) non-orthogonal multiple access (NOMA) systems. Cross PHY/application (APP) layer resource allocation and adaptive source encoding rate for single-antenna NOMA/ orthogonal frequency division multiple access (OFDMA) video communications was also recently proposed. These prior works did not deal with joint subcarrier/ BF cluster assignment in a cross-layer fashion. In this paper, we propose a joint adaptive source encoding rate and iterative cross-PHY/APP layer resource management for BF-NOMA-OFDMA video communications to support 2N users on one subcarrier and improves the average video quality peak-signal-to-noise ratio (PSNR). The proposed scheme outperforms Ali et al. PHY layer resource management by 4.18dB in the numerical results. Considering the high computational complexity of the iterative resource allocation, we further propose replacing the iterative cross-layer resource allocation by a supervised learning-based approach. We propose a novel and problem-specific post-processing procedure to guarantee the constraints that every user holds at least one subcarrier and a subcarrier support 2N users. The proposed supervised learning-based approach reduces the execution time by 98% in a GPU-based platform, comparable with 91-99% execution time reduction of supervised learning based communication algorithms in the literature, at the cost of 4% PSNR loss. Finally, we propose a supervised/unsupervised learning -based approach with minus PSNR as the cost function for continuous learning in an unsupervised learning fashion after supervised learning is done. The numerical results show that the proposed hybrid supervised/unsupervised learning approach can reduce the PSNR loss to just 1%.
A General CA-CFAR Performance Analysis for Weibull-Distributed Clutter Environments

In this letter, we provide a general cell-averaging constant false alarm rate (CA-CFAR) performance analysis considering homogeneous Weibull-distributed clutter environments. To do so, we provide generalized expressions for the probability of detection (PD) and probability of false alarm (PFA). Notably, these expressions permit arbitrary values for the shape parameter of the Weibull interference. So far, all CA-CFAR performance analyses were carried out by setting the shape parameter of the Weibull samples within the CFAR window equal to one, which narrows the overall evaluation spectrum. In this study, we allow the shape parameter to take values greater than or equal to one—regime of paramount importance in many practical applications—and still manage to derive manageable and easy-to-implement solutions. All our expressions are confirmed via Monte-Carlo simulations. Numerical results indicate that radar detection is improved as the shape parameter increases.
A comparison of reciprocal teaching and scientific approaches for improving pupils' mathematical understanding

This study is intended to result in instruction that will help pupils improve their mathematics understanding skills. The similarity of strategies in both the scientific and reciprocal teaching approaches makes it difficult to choose which approach is better. This investigation aims to investigate and explain (1) Improving pupils' mathematical understanding through comparison of scientific approaches and Reciprocal Teaching based on PAM; (2) The impact of the interaction between learning approaches with pupils' PAM on enriching pupils' mathematics understanding ability. It is a quasi-experimental research project that uses two experimental classes. The results of this study are (1) Pupils who learn through the scientific approach and pupils who learn through reciprocal teaching strategies based on PAM pupils have different mathematical thinking abilities, with the average value of improvement in the scientific approach class being more significant than the reciprocal teaching approach class; (2) Learning and pupils' PAM have no interaction effect. It means that learning in both experimental classes applies to all pupils overall in improving pupils' mathematical understanding ability. Based on the results of this study, this study implies that teachers can employ learning using a scientific approach as one of the learning options for pupils to increase their mathematical understanding abilities.
Development and Validation of a Novel Ferroptosis-Related Gene Signature for Prognosis and Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a cancer that is sensitive to ferroptosis, and immunotherapy has emerged as a promising treatment for HCC patients. However, the prognostic potential of ferroptosis-related genes (FRGs) and the effect of ferroptosis on the tumor immune microenvironment in HCC remain largely obscure. Here, we analyzed the expression pattern of FRGs using the TCGA, ICGC and GEO databases. The expression of most FRGs was upregulated in HCC tissues compared with normal liver tissues. Three independent clusters were determined by consensus clustering analysis based on FRG expression in HCC. Cluster 3 exhibited higher expression, unfavorable prognosis, and higher histological tumor stage and grade than clusters 1 and 2. CIBERSORT analysis indicated different infiltrating levels of various immune cells among the three clusters. Moreover, most immune checkpoint genes were highly expressed in cluster 3. Univariate Cox regression and LASSO regression analyses were performed to develop a five FRG-based prognostic risk model using the TCGA and ICGC datasets. Kaplan–Meier analysis and ROC curves were performed to verify the prognostic potential of the risk model. A nomogram containing independent prognostic factors was further developed. Compared with low-risk patients, high-risk HCC patients exhibited worse overall survival (OS). In addition, this risk model was significantly correlated with the infiltrating levels of six major types of immune cells in HCC. Finally, the relationships between the five FRGs and drug sensitivity were investigated. The present study suggests that the five FRGs could elucidate the molecular mechanisms of HCC and lead to a new direction for the improvement of predictive, preventive, and personalized medicine for HCC.
Study on Microstructural Evolution of DP Steel Considering the Interface Layer Based on Multi Mechanism Strain Gradient Theory

A multi-mechanism constitutive model is proposed in this paper to better describe the effect of the local hardening behavior of the interface layer on the mechanical heterogeneity of dual-phase (DP) steel. The constitutive equations considering the geometrically necessary dislocations (GNDs) and back stress at grain level and sample level were established. Based on the finite element simulation results, the influences of local hardening and microstructure characteristics on the strain–stress evolution, statistical storage dislocations, GNDs, and back stress of DP steel were studied and discussed. Due to the local hardening effect, the ferrite phase was treated as an inhomogeneous matrix reinforced by some small islands of martensite in the simulation. The simulation results show that the thickness of the interface layer has a significant effect on the macroscopic hardening property of DP steel, while the number of interface layers has little effect. Meanwhile, the GNDs and back stress at the grain level also have little effect on the strengthening of DP steel. The contribution of GNDs at the sample level to the flow stress is about 47%.
Ultra-fast Coherent Diffraction Imaging of Single Particles, Clusters and Biomolecules

Theoretical studies and simulations predict that with a very short and very intense coherent X-ray pulse a single diffraction pattern may be recorded from a large macromolecule, a virus, or a cell without the need for crystalline periodicity. Measurement of the over-sampled X-ray diffraction pattern permits phase retrieval and hence structure determination. Although individual samples will be destroyed by the very intense X-ray pulse, a three-dimensional data set could be assembled when copies of a reproducible sample are exposed to the beam one by one. The challenges in carrying out such an experiment requires an interdisciplinary approach, drawing upon structural biology, atomic and plasma physics, mathematics, statistics, and XFEL physics. The potential for breakthrough science in this area is great with impact not only in the biological areas but wherever structural information at or near atomic resolution on the nanoscale is valuable.
Smartphone LiDAR Technologies for Surveying and Reality Modelling in Urban Scenarios: Evaluation Methods, Performance and Challenges

The aim of the research was to evaluate the performance of smartphone depth sensors (Time of Flight Camera(ToF) and Light Detection and Ranging (LiDAR)) from Android (Huawei P30 Pro) and iOS (iPhone 12 Pro and iPAD 2021 Pro) devices in order to build a 3D point cloud. In particular, the smartphones were tested in several case studies involving the scanning of several objects: 10 building material samples, a statue, an interior room environment and the remains of a Doric column in a major archaeological site. The quality of the point clouds was evaluated through visual analysis and using three eigenfeatures: surface variation, planarity and omnivariance. Based on this approach, some issues with the point clouds generated by smartphones were highlighted, such as surface splitting, loss of planarity and inertial navigation system drift problems. In addition, it can finally be deduced that, in the absence of scanning problems, the accuracies achievable from this type of scanning are ~1–3 cm. Therefore, this research intends to describe a method of quantifying anomalies occurring in smartphone scans and, more generally, to verify the quality of the point cloud obtained with these devices.
Carbon fiber damage evolution under flame attack and the role of impurities

Carbon fibers (CFs) are prone to extensive oxidation under fire attack, for instance, in an aircraft fire scenario. This work addresses the damage mechanisms observed on polyacrylonitrile (PAN)‐based CFs with different microstructure exposed to open flames. A fixed‐point technique was developed to follow‐up individual CFs by means of time‐controlled insertion into premixed methane/air flames, followed by scanning electron microscopy (SEM) and energy‐dispersive X‐ray spectroscopy (EDS) analyses. Besides diameter reduction, three localized damage mechanisms were discerned in the presence of impurities, which were quantified by neutron activation analysis (NAA). Severe pitting was ascribed to catalytic oxidation mainly caused by alkali and alkaline earth metals. After an initial period where catalytic reactions between impurities and the carbon surface dominate, the flame stoichiometry governed the CF gasification process, with lean flames being much more aggressive than rich ones. A second mechanism, channeling, was caused by mobile metallic impurities. Some impurities showed an opposite effect, lowering reactivity and thus preventing further catalysis. Amorphous damage with a skin‐peeling effect is believed to be the result of localized impurities at high concentrations and microstructural variations. Hindrance or synergistic effects between impurities are discussed. Finally, apparent axial pit growth rates were determined and compared with other carbonaceous materials, revealing a strong influence of impurities and the flame reactive atmosphere on CF oxidation.
Uso de Python para detecção de fake news sobre a covid-19: desafios e possibilidades

Este trabalho tem como objetivo relatar estratégias para coleta de um conjunto de dados em português para treinamento de modelos de Inteligência Artificial com vistas a identificar de forma automática fake news sobre covid-19 disseminadas durante a pandemia, a partir de código Python. Analisamos um método de detecção de fake news baseado em uma Rede Neural Recorrente e de aprendizagem supervisionada. Selecionamos um corpus com 7,2 mil textos coletados em websites e agências de notícias por Monteiro et al. (2018) com cada um previamente catalogado como verdadeiro ou falso como conjunto de dados de treino e validação. O modelo foi usado para detecção de fake news sobre covid-19 em um conjunto de notícias coletadas e classificadas pelos autores deste trabalho. O índice de acerto foi de 70%, ou seja, essa foi a taxa de sucesso da detecção dos itens catalogados.
Whole-Exome Sequencing Uncovers Specific Genetic Variation Difference Based on Different Modes of Drug Resistance in Small Cell Lung Cancer

The poor survival rate of small cell lung cancer (SCLC) is mainly related to the condition that patients with SCLC often have good responses to first-line chemotherapy initially, but later on, most of these patients relapse rapidly due to resistance to further treatment. In this study, we attempted to analyze whole-exome sequencing data based on the largest sample size to date, to develop a classifier to predict whether a patient will be chemorefractory or chemosensitive and to explicate the risk of recurrence that affects the prognosis of patients. We showed the different characteristics of somatic mutational signatures, somatic mutation genes, and distinct genome instability between chemorefractory and chemosensitive SCLC patients. Amplified mutations in the chemosensitive group inhibited the regulation of the cell cycle process, transcription factor binding, and B-cell differentiation. Analysis of deletion mutation also suggested that detection of the chromosomal-level variation might influence our treatment decisions. Higher PD-L1 expressions (based on TPS methods) were mostly present among chemosensitive patients (p = 0.026), while there were no differences in PD-L1 expressions (based on CPS methods) and CD8+ TILs between the two groups. According to the model determined by logistic regression, each sample was endowed with a predictive probability value (PV). The samples were divided into a high-risk group (>0.55) and a low-risk group (≤0.55), and the survival analysis showed obvious differences between the two groups. This study provides a reference basis to translate this knowledge into practice, such as formulating personalized treatment plans, which may benefit Chinese patients with SCLC.
Role of Social Capital in Women-owned Businesses in Sri Lanka: A Relational Perspective

Structural dimensions and gender comparison in social capital dominate the entrepreneurship literature; however, little research has focused on women entrepreneurs and the relational aspect of social capital. Therefore, the objective of this study is to explore the role of social capital in women-owned businesses in Sri Lanka, focusing on the relational dimension. Social constructionists focus on interaction, and narrative design is particularly appropriate for exploring experiences of female entrepreneurs. For this purpose, fifteen women were selected using the purposive sampling method, and narrative interviews and observations were used to collect data, which were analysed using thematic analysis. The findings show that these women favour strong ties, accumulating social capital through multiplex ties. Family ties support businesswomen more than the support received from friends and via professional links. Overall, the study further extends the dynamic view of the relational dimension of social capital with evidence from Sri Lankan women entrepreneurs.
Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM

As the fast development of wearable devices and Internet of things technologies, real-time monitoring of ECG signals is quite critical for cardiovascular diseases. However, dynamic ECG signals recorded in free-living conditions suffered from extremely serious noise pollution. Presently, most algorithms for ECG signal evaluation were designed to divide signals into acceptable and unacceptable. Such classifications were not enough for real-time cardiovascular disease monitoring. In the study, a wearable ECG quality database with 50,085 recordings was built, including A/B/C (or high quality/medium quality/low quality) three quality grades (A: high quality signals can be used for CVD detection; B: slight contaminated signals can be used for heart rate extracting; C: heavily polluted signals need to be abandoned). A new SQA classification method based on a three-layer wavelet scattering network and transfer learning LSTM was proposed in this study, which can extract more systematic and comprehensive characteristics by analyzing the signals thoroughly and deeply. Experimental results ( mACC = 98.56%, mF 1 = 98.55%, Se A = 97.90%, Se B = 98.16%, Se C = 99.60%, + P A = 98.52%, + P B = 97.60%, + P C = 99.54%, F 1A = 98.20%, F 1B = 97.90%, F 1C = 99.60%) and real data validations proved that this proposed method showed the high accuracy, robustness, and computationally efficiency. It has the ability to evaluate the long-term dynamic ECG signal quality. It is advantageous to promoting cardiovascular disease monitoring by removing contaminating signals and selecting high-quality signal segments for further analysis.