Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated analysis of long non-coding RNAs and mRNAs associated with glaucoma in vitro

Introduction In recent years, the biological functions and important roles of long non-coding RNAs (lncRNAs) have been widely reported in many diseases. Although glaucoma is the leading cause of blindness worldwide, the specific mechanisms of lncRNAs in the pathogenesis and progression of glaucoma remain unclear. Our research aims to elucidate the differentially expressed lncRNAs and mRNAs in glaucoma and to provide a basis for further exploration of the specific mechanism of action of lncRNAs in the progression of glaucoma. Methods We performed RNA sequencing on samples from a pressurized model of R28 cells and performed bioinformatics analyses on the sequencing results. The expression consistency of lncRNAs in clinical samples from patients with glaucoma or cataracts was detected using real-time quantitative polymerase chain reaction (RT-qPCR). Results RNA sequencing results showed that lncRNAs in cluster 5 were upregulated with increasing stress after typing all significantly altered lncRNAs using k-means in a cellular stress model. KEGG analysis indicated that they were associated with neurodegenerative diseases. Differentially expressed lncRNAs were verified by RT-qPCR, and the lncRNA expression levels of AC120246.2 and XLOC_006247 were significantly higher in the aqueous humor (AH) of patients with glaucoma than in those with cataracts. For LOC102551819, there was almost no expression in the AH and trabecular meshwork in patients with glaucoma but high expression was observed in the iris. Conclusion Our research proposes potential diagnostic or intervention targets for clinical applications as well as a theoretical basis for more in-depth research on the function of lncRNAs in glaucoma.
Family-Work-School Conflicts, Self-Regulation, and Well-Being Among Transfer Students During the COVID-19 Pandemic

Using multigroup structural equation modeling, this study examines whether family-work-school conflicts directly or indirectly predict transfer students’ well-being (via self-regulation) across the periods of pre-, during, and post-COVID-19 lockdown in the Chicago metropolitan area in Illinois, United States. The results indicate that family-school conflict predicted well-being regardless of lockdown status, but work-school conflict predicted well-being only during the lockdown period. This study also discusses the importance of cognitive regulation for transfer students’ transition in the middle of the pandemic.
3D finite-difference frequency-domain modeling of controlled-source electromagnetic data: Direct solution and optimization for high accuracy

Three-dimensional modeling of marine controlled-source electromagnetic (CSEM) data is vital to improve the understanding of electromagnetic (EM) responses collected in increasingly complex geologic settings. A modeling tool for simulating 3D marine CSEM surveys, based on a finite-difference discretization of the Helmholtz equation for the electric fields, has been developed. Optimizations for CSEM simulations include the use of a frequency-domain technique, a staggering scheme that reduces inaccuracies especially for horizontal electric-dipole sources located near the seafloor, and a new interpolation technique that provides highly accurate EM field values for receivers located in the immediate vicinity of the seafloor. Source singularities are eliminated through a secondary-field approach, in which the primary fields are computed analytically for a homogeneous or a 1D layered background; the secondary fields are computed using the finite-difference technique. Exploiting recent advances in computer technology and algorithmic developments, the system of finite-difference equations is solved using the MUMPS direct-matrix solver. In combination with the other optimizations, this allows accurate EM field computations for moderately sized models on small computer clusters. The explicit availability of matrix factorizations is advantageous for multisource modeling and makes the algorithm well suited for future use within an inversion scheme. Comparisons of simulated data for (1) 1D models to data generated using a 1D reflectivity technique and (2) 3D models to published 3D data demonstrate the accuracy and benefits of the approach.
A denoising method for phase-retrieval-based imaging

Denoising is significant in many fields, especially for computational imaging. Coherent diffraction imaging and speckle correlation imaging are regarded as the most promising computational imaging techniques. The above two imaging techniques can be classified as phase-retrieval-based imaging due to the phase-retrieval is a vital procedure for object reconstruction. However, the acquisition process would generate unavoidable noise and participate in the iteration process of phase-retrieval. Hence, it is necessary to denoising after obtained the original reconstruction image. Here, a denoising method that based on connected domain is proposed for phase-retrieval method. We experimentally demonstrate the denoising results and quantitatively analyze the effect. Comparison of the classical median filter, wiener filter and bilateral filter, our method shows a satisfactory denoising effect. Our results prove that connected domain denoising is useful and promising, which provides a new post-processing denoising method for phase-retrieval-based imaging.
Neuropsychological Performance and Cardiac Autonomic Function in Blue- and White-Collar Workers: A Psychometric and Heart Rate Variability Evaluation

The 21st century has brought a growing and significant focus on performance and health within the workforce, with the aim of improving the health and performance of the blue- and white-collar workforce. The present research investigated heart rate variability (HRV) and psychological performance between blue and white-collar workers to determine if differences were evident. A total of 101 workers (n = 48 white-collar, n = 53 blue-collar, aged 19–61 years) underwent a three lead electrocardiogram to obtain HRV data during baseline (10 min) and active (working memory and attention) phases. The Cambridge Neuropsychological Test Automated Battery, specifically the spatial working memory, attention switching task, rapid visual processing and the spatial span, were used. Differences in neurocognitive performance measures indicated that white-collar workers were better able to detect sequences and make less errors than blue-collar workers. The heart rate variability differences showed that white-collar workers exhibit lower levels of cardiac vagal control during these neuropsychological tasks. These initial findings provide some novel insights into the relationship between occupation and psychophysiological processes and further highlight the interactions between cardiac autonomic variables and neurocognitive performance in blue and white-collar workers.
Pharmacist Administration of Long-Acting Injectable Antipsychotics to Community-Dwelling Patients: A Scoping Review

Long-acting injectable antipsychotics (LAIAs) have demonstrated positive outcomes for people with serious mental illnesses. They are underused, and access to LAIAs can be challenging. Pharmacies could serve as suitable environments for LAIA injection by pharmacists. To map and characterize the literature regarding the administration of LAIAs by pharmacists, a scoping review was conducted. Electronic-database searches (e.g., PsycINFO, Ovid Medline, Scopus, and Embase) and others including ProQuest Dissertations & Theses Global and Google, were conducted. Citation lists and cited-reference searches were completed. Zotero was used as the reference-management database. Covidence was used for overall review management. Two authors independently screened articles and performed full-text abstractions. From all sources, 292 studies were imported, and 124 duplicates were removed. After screening, 13 studies were included for abstraction. Most articles were published in the US since 2010. Seven studies used database and survey methods, with adherence and patient satisfaction as the main patient-outcomes assessed. Reporting of pharmacists’ and patients’ perspectives surrounding LAIA administration was minimal and largely anecdotal. Financial analyses for services were also limited. The published literature surrounding pharmacist administration of LAIAs is limited, providing little-to-no guidance for the development and implementation of this service by others.
Comparative Genomics of Halobacterium salinarum Strains Isolated from Salted Foods Reveals Protechnological Genes for Food Applications

Archaeal cell factories are becoming of great interest given their ability to produce a broad range of value-added compounds. Moreover, the Archaea domain often includes extremophilic microorganisms, facilitating their cultivation at the industrial level under nonsterile conditions. Halophilic archaea are studied for their ability to grow in environments with high NaCl concentrations. In this study, nine strains of Halobacterium salinarum were isolated from three different types of salted food, sausage casings, salted codfish, and bacon, and their genomes were sequenced along with the genome of the collection strain CECT 395. A comparative genomic analysis was performed on these newly sequenced genomes and the publicly available ones for a total of 19 H. salinarum strains. We elucidated the presence of unique gene clusters of the species in relation to the different ecological niches of isolation (salted foods, animal hides, and solar saltern sediments). Moreover, genome mining at the single-strain level highlighted the metabolic potential of H. salinarum UC4242, which revealed the presence of different protechnological genes (vitamins and myo-inositol biosynthetic pathways, aroma- and texture-related features, and antimicrobial compounds). Despite the presence of genes of potential concern (e.g., those involved in biogenic amine production), all the food isolates presented archaeocin-related genes (halocin-C8 and sactipeptides).
Statistical issues in first‐in‐man studies

Preface. In March 2006 a first‐in‐man trial took place using healthy volunteers involving the use of monoclonal antibodies. Within hours the subjects had suffered such adverse effects that they were admitted to intensive care at Northwick Park Hospital. In April 2006 the Secretary of State for Health announced the appointment of Professor (now Sir) Gordon Duff, who chairs the UK's Commission on Human Medicines, to chair a scientific expert group on phase 1 clinical trials. The group reported on December 7th, 2006 ( Expert Scientific Group on Clinical Trials, 2006a ). Clinical trials have a well‐established regulatory basis both in the UK and worldwide. Trials have to be approved by the regulatory authority and are subject to a detailed protocol concerning, among other things, the study design and statistical analyses that will form the basis of the evaluation. In fact, a cornerstone of the regulatory framework is the statistical theory and methods that underpin clinical trials. As a result, the Royal Statistical Society established an expert group of its own to look in detail at the statistical issues that might be relevant to first‐in‐man studies. The group mainly comprised senior Fellows of the Society who had expert knowledge of the theory and application of statistics in clinical trials. However, the group also included an expert immunologist and clinicians to ensure that the interface between statistics and clinical disciplines was not overlooked. In addition, expert representation was sought from Statisticians in the Pharmaceutical Industry (PSI), an organization with which the Royal Statistical Society has very close links. The output from the Society's expert group is contained in this report. It makes a number of recommendations directed towards the statistical aspects of clinical trials. As such it complements the report by Professor Duff's group and will, I trust, contribute to a safer framework for first‐in‐man trials in the future. Tim Holt (President, Royal Statistical Society)
The Bias of Estimating Equations with Application to the Error Rate of Logistic Discrimination

Abstract The logistic regression classification method uses parameter estimates that are the solution of an estimating equation. This article derives a convenient expression for the bias of a vector estimator defined by estimating equations. The expression and the results of O'Neill are used to derive the bias and the error or misclassification rate of logistic regression classification in two examples where the assumed model for logistic regression does not hold. Logistic regression classification is found to be very robust for the departures considered.
Resolvability Analysis of Single Azimuth Seismic Moment Tensor Inversion

Summary Microseismicity monitoring of hydraulic fractures is frequently done with a single azimuth observational geometry with receivers deployed in a vertical array. The latter restricts the solutions that we are capable to retrieve and can hide the existence of non-deviatoric sources (Vavrycuk, 2007). The complete recovering of the source mechanisms is an important aspect for the understanding of induced fracturing and can add key information for the characterization of the fracturing process (i.e., whether the fracture is created as a propagating tensile opening or via multiple shearing fractures). In this presentation we analyze the resolution matrix of the Seismic Moment Tensor (SMT) inversion algorithm and analyze the resolvability problem as a function of azimuth for different type of mechanisms.
Protective effect of Buddha’s Temple extract against tert-butyl hydroperoxide stimulation-induced oxidative stress in DF-1 cells

Objective This study aimed to determine the protective efficacy of Buddha’s Temple (BT) extract against tert-butyl hydroperoxide (t-BHP)-induced oxidative stress in Gallus gallus chicken embryo fibroblast cell line (DF-1) and its effects on the cell lipid metabolism. Methods In this experimental study, Gallus gallus DF-1 fibroblast cells were pretreated with BT 10−7 for 24 hours, followed by their six-hour exposure to t-BHP (100 μM). Water-soluble tetrazolium salt-8 (WST-8) assays were performed, and the growth curve was computed. The intracellular gene expression changes caused by BT extract were confirmed through quantitative polymerase chain reaction (qPCR). Flow cytometry, oil red O staining experiment, and thin-layer chromatography were performed for the detection of intracellular metabolic mechanism changes. Results The WST-8 assay results showed that the BT pretreatment of Gallus gallus DF-1 fibroblast cell increased their cell survival rate by 1.08%±0.04%, decreased the reactive oxygen species (ROS) level by 0.93%±0.12% even after exposure to oxidants, and stabilized mitochondrial activity by 1.37%±0.36%. In addition, qPCR results confirmed that the gene expression levels of tumor necrosis factor α (TNFα), TIR domain-containing adapter inducing IFN-beta (TICAM1), and glucose-regulated protein 78 (GRP78) were regulated, which contributed to cell stabilization. Thin-layer chromatography and oil red O analyses showed a clear decrease in the contents of lipid metabolites such as triacylglycerol and free fatty acids. Conclusion In this study, we confirmed that the examined BT extract exerted selective protective effects on Gallus gallus DF-1 fibroblast cells against cell damage caused by t-BHP, which is a strong oxidative inducer. Furthermore, we established that this extract significantly reduced the intracellular ROS accumulation due to oxidative stress, which contributes to an increase in poultry production and higher incomes.
Deep learning-based EEG emotion recognition: Current trends and future perspectives

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human–computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.
Narrative Memory Work of Employees in Family Businesses: How Founding Stories Shape Organizational Identification

This study is concerned with how founding stories are sustained across multiple generations of employees in family firms and how these stories influence organizational identification. Drawing on a social memory perspective and narrative memory work, we explore the retold founding stories of employees in a large agricultural family firm. Our study demonstrates that founding stories transform firsthand memories into collective memory across multiple generations through intertwining intradiegetic storytelling with material and relational processes. The effortful work of remembering together across familial and social relations, spaces, and embodied ways explains how successive generations understand their belongingness to the organization.
Performance bounds with precoding matrices compliant with standardized 5G-NR for MIMO transmission

Advanced multiple-input multiple-output (MIMO) beamforming techniques are crucial in 5G New Radio (NR) to achieve the expected data rate values. Therefore, the 3rd Generation Partnership Project (3GPP) has proposed a codebook-based MIMO precoding strategy to provide high diversity, array gain, and spatial multiplexing. The main goal is to obtain a tradeoff between performance, signal overhead, and complexity. The precoding matrix is selected from a set of predefined codebooks based on the knowledge that the 5G-NR base station (gNB) acquires about the channel status. In this work, a detailed study of the precoding matrix design is provided following the guidelines reported in the technical specifications 38-211 and 38-214 of the 3GPP. An analysis of the performance in terms of spectral efficiency (SE) achieved by the 5G-NR precoding matrices is illustrated for a single-user MIMO scenario. These results are contrasted against the optimal singular value decomposition (SVD) solution in order to explore the gap between the standardized precoding proposal and the optimal one. Several values of signal-to-noise ratio (SNR) and different antenna array configurations are considered. Moreover, the multiplexing gain for a different number of parallel data streams is evaluated. Numerical results show the SE bounds that can be obtained with the 5G-NR precoding matrices. These insights are of key importance for practical implementation of precoding strategies in 5G-NR systems and beyond.
The Chamaeleon Infrared Nebula: A Polarization Study with High Angular Resolution

We performed high-angular resolution imaging polarimetry of the Chamaeleon Infrared Nebula (Cha IRN) in the J and H bands. In this Letter, we present a polarization map in the H band. These new measurements, together with previous polarization data, are interpreted using a Monte Carlo model for radiative transfer. As a result of these calculations, we derive properties of the circumstellar dust configuration of Cha IRN.
THEORY OF THE SUPERCONDUCTING TRANSITION TEMPERATURE AND ENERGY GAP FUNCTION OF SUPERPOSED METAL FILMS

A calculation is made of the transition temperature of a film sandwich composed of two thin superposed films of different metals, only one of which is superconducting in bulk. Good quantitative agreement is obtained with existing measurements of the dependence of the transition temperature on the thicknesses of the two component films. The problem is shown to be mathematically equivalent to the simple one of the energy levels of a particle in a onedimensional square potential well. (auth)
First Characterization of Novel Silicon Carbide Detectors with Ultra-High Dose Rate Electron Beams for FLASH Radiotherapy

Ultra-high dose rate (UHDR) beams for FLASH radiotherapy present significant dosimetric challenges. Although novel approaches for decreasing or correcting ion recombination in ionization chambers are being proposed, applicability of ionimetric dosimetry to UHDR beams is still under investigation. Solid-state sensors have been recently investigated as a valuable alternative for real-time measurements, especially for relative dosimetry and beam monitoring. Among them, Silicon Carbide (SiC) represents a very promising candidate, compromising between the maturity of Silicon and the robustness of diamond. Its features allow for large area sensors and high electric fields, required to avoid ion recombination in UHDR beams. In this study, we present simulations and experimental measurements with the low energy UHDR electron beams accelerated with the ElectronFLASH machine developed by the SIT Sordina company (IT). The response of a newly developed 1 × 1 cm2 SiC sensor in charge as a function of the dose-per-pulse and its radiation hardness up to a total delivered dose of 90 kGy, was investigated during a dedicated experimental campaign, which is, to our knowledge, the first characterization ever done of SiC with UHDR-pulsed beams accelerated by a dedicated ElectronFLASH LINAC. Results are encouraging and show a linear response of the SiC detector up to 2 Gy/pulse and a variation in the charge per pulse measured for a cumulative delivered dose of 90 kGy, within ±0.75%.
Response Function of Active Thermal Neutron Detector for Boron Neutron Capture Therapy Using Back-Illuminated Thin Si PIN Diode

A lithium fluoride (LiF)-silicon (Si)-based neutron detector was developed for real-time measurement of neutron beams for boron neutron capture therapy (BNCT). Detection efficiencies for thermal neutrons were measured at the national standard neutron field in Japan and simulated using a Monte Carlo simulation code to evaluate the absolute BNCT neutron fluence rates. The simulation neutron detection efficiencies agreed excellently with the experimental results, within an experimental error of 3%. The simulation results reproduced well the peaks of tritons and alpha particles emitted from the 6Li(n,t)4He reaction, peak of the protons produced by the <inline-formula> <tex-math notation="LaTeX">$^{14}\text{N}$ </tex-math></inline-formula>(n,p)<inline-formula> <tex-math notation="LaTeX">$^{14}\text{C}$ </tex-math></inline-formula> reaction in air, and distribution of alpha particles produced by the neutron capture reactions of dopant boron. The active BNCT neutron detector was characterized based on the experimental and simulation results.