Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning-based integration develops a neutrophil-derived signature for improving outcomes in hepatocellular carcinoma

Introduction The heterogeneity of tumor immune microenvironments is a major factor in poor prognosis among hepatocellular carcinoma (HCC) patients. Neutrophils have been identified as playing a critical role in the immune microenvironment of HCC based on recent single-cell studies. However, there is still a need to stratify HCC patients based on neutrophil heterogeneity. Therefore, developing an approach that efficiently describes "neutrophil characteristics" in HCC patients is crucial to guide clinical decision-making. Methods We stratified two cohorts of HCC patients into molecular subtypes associated with neutrophils using bulk-sequencing and single-cell sequencing data. Additionally, we constructed a new risk model by integrating machine learning analysis from 101 prediction models. We compared the biological and molecular features among patient subgroups to assess the model's effectiveness. Furthermore, an essential gene identified in this study was validated through molecular biology experiments. Results We stratified patients with HCC into subtypes that exhibited significant differences in prognosis, clinical pathological characteristics, inflammation-related pathways, levels of immune infiltration, and expression levels of immune genes. Furthermore, A risk model called the "neutrophil-derived signature" (NDS) was constructed using machine learning, consisting of 10 essential genes. The NDS's RiskScore demonstrated superior accuracy to clinical variables and correlated with higher malignancy degrees. RiskScore was an independent prognostic factor for overall survival and showed predictive value for HCC patient prognosis. Additionally, we observed associations between RiskScore and the efficacy of immune therapy and chemotherapy drugs. Discussion Our study highlights the critical role of neutrophils in the tumor microenvironment of HCC. The developed NDS is a powerful tool for assessing the risk and clinical treatment of HCC. Furthermore, we identified and analyzed the feasibility of the critical gene RTN3 in NDS as a molecular marker for HCC.
Non-Melting Phenomena of Snowflakes Observed in Subsaturated Air below Freezing Level

The analyses of the meteorological surface data at Wajima, Matsumoto, and Nikko Weather Stations in Japan were made in order to examine the effect of relative humidity of air on melting of snowflakes below freezing level in the atmosphere. The results showed that the types of precipitations (rain or snow) observed on the ground were closely dependent on surface relative humidity as well as surface air temperature. When surface relative humidity was below a certain critical humidity, even at a higher surface air temperature above 0* precipitations were snow. Using the regression analysis, the relations of the critical relative humidity RHcri to surface air temperature T could be obtained at each Weather Station in the following forms, RHcri=-7.5T+93, Wajima, RHcri= -7.3T+96, Mtsumoto, RHcri=-6.2T+91, Nikko. The relations thus obtained agreed well with that obtained previously from the theoretical calculations by Matsuo and Sasyo (1981).
The Sigma Receptors in Alzheimer’s Disease: New Potential Targets for Diagnosis and Therapy

Sigma (σ) receptors are a class of unique proteins with two subtypes: the sigma-1 (σ1) receptor which is situated at the mitochondria-associated endoplasmic reticulum (ER) membrane (MAM), and the sigma-2 (σ2) receptor, located in the ER-resident membrane. Increasing evidence indicates the involvement of both σ1 and σ2 receptors in the pathogenesis of Alzheimer’s disease (AD), and thus these receptors represent two potentially effective biomarkers for emerging AD therapies. The availability of optimal radioligands for positron emission tomography (PET) neuroimaging of the σ1 and σ2 receptors in humans will provide tools to monitor AD progression and treatment outcomes. In this review, we first summarize the significance of both receptors in the pathophysiology of AD and highlight AD therapeutic strategies related to the σ1 and σ2 receptors. We then survey the potential PET radioligands, with an emphasis on the requirements of optimal radioligands for imaging the σ1 or σ2 receptors in humans. Finally, we discuss current challenges in the development of PET radioligands for the σ1 or σ2 receptors, and the opportunities for neuroimaging to elucidate the σ1 and σ2 receptors as novel biomarkers for early AD diagnosis, and for monitoring of disease progression and AD drug efficacy.
Effects of virtual reality guided meditation in older adults: the protocol of a pilot randomized controlled trial

Background Virtual reality (VR) based meditation has been shown to help increase relaxation and decrease anxiety and depression in younger adults. However, this has not been studied in Randomized Controlled Trials (RCT) in the older adult population. The aim of this RCT is to assess the feasibility and acceptability of a VR-guided meditation intervention for community-dwelling older adults and its effect on stress and mental health. Methods We will recruit 30 participants aged ≥ 60 years, whose perceived stress score (PSS) is > 14 (moderate stress), and randomize them 1:1 to the intervention or control waitlist group. The intervention will involve exposure to eight 15-min VR-guided meditation sessions distributed twice weekly for 4-weeks. Two modalities will be offered: in-home and at the hospital. Data analysis Baseline and post-intervention assessments will evaluate perceived stress, anxiety, depression, sleep quality, quality of life, and mindfulness skills. Analyses will employ mixed methods repeated ANOVA tests. Qualitative analyses through semi-structured interviews and participant observation will be used to assess participants’ experiences. Study outcomes include: (A) feasibility and acceptability compared to a waitlist control (B) stress, using the Perceived Stress Scale (PSS); (C) anxiety, and depression, using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9); (D) insomnia, quality of life and mindfulness skills, using the Athens Insomnia Scale (AIS), Quality of Life Questionnaire (EQ-5D-5L) and Five Facets Mindfulness Questionnaire Short Forms (FFMQ-SF), respectively. We will also measure immersive tendencies, sickness and sense of presence using the Simulator Sickness Questionnaire (SSQ) and the Presence Questionnaire (PQ). Discussion Virtual reality-guided meditation could be an acceptable, feasible, safe, and cost-effective novel alternative health intervention for improving older adults’ mental health. Clinical trial registration: NCT05315609 at https://clinicaltrials.gov.
Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.
REGA-SIGN: Development of a Novel Set of NanoBRET-Based G Protein Biosensors

Despite G protein-coupled receptors (GPCRs) being important theapeutic targets, the signaling properties of many GPCRs remain poorly characterized. GPCR activation primarily initiates heterotrimeric G protein signaling. To detect ligand-induced G protein activation, Bioluminescence Resonance Energy Transfer (BRET)-based biosensors were previously developed. Here, we designed a novel set of Nanoluciferase (NLuc) BRET-based biosensors (REGA-SIGN) that covers all Gα protein families (i.e., Gαi/o, GαSs/L, Gα12/13 and Gαq/15). REGA-SIGN uses NLuc as a bioluminescent donor and LSS-mKATE2, a red-shifted fluorophore, as an acceptor. Due to the enhanced spectral separation between donor and acceptor emission and the availability of a stable substrate for NLuc, this donor–acceptor pair enables sensitive kinetic assessment of G protein activity. After optimization, the NLuc integration sites into the Gα subunit largely corresponded with previously reported integration sites, except for GαSs/L for which we describe an alternative NLuc insertion site. G protein rescue experiments validated the biological activity of these Gα donor proteins. Direct comparison between EGFP and LSS-mKATE2 as acceptor fluorophores revealed improved sensitivity for nearly all G protein subtypes when using the latter one. Hence, REGA-SIGN can be used as a panel of kinetic G protein biosensors with high sensitivity.
SOQ: Structural Reinforcement Learning for Constrained Delay Minimization With Channel State Information

The goal of this study is to minimize the average delay under the average energy consumption constraint in a single-queue and single-server wireless communication system with block fading channels. To this end, we formulate this problem as an infinite-horizon-constrained Markov decision process (CMDP). In our CMDP, we jointly consider the queue length and channel condition as the state. We apply the Lagrange multiplier method to transform the constrained optimization problem into an unconstrained optimization problem. Then, we prove that an optimal scheduling strategy is nondecreasing with respect to queue length and channel state. To obtain an optimal scheduling policy, an efficient reinforcement learning algorithm, the Structural-Optimistic <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning algorithm (SOQ), is proposed, which exploits the nondecreasing property of optimal policies by using policy projection. Finally, we analyze how to control the average energy consumption to satisfy a given energy consumption constraint. The simulation results show that the performance of the SOQ surpasses that of the traditional <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning algorithm in terms of the average cost during the learning phase.
Scale mixtures log-Birnbaum–Saunders regression models with censored data: a Bayesian approach

ABSTRACT The main objective of this paper is to develop a full Bayesian analysis for the Birnbaum–Saunders (BS) regression model based on scale mixtures of the normal (SMN) distribution with right-censored survival data. The BS distributions based on SMN models are a very general approach for analysing lifetime data, which has as special cases the Student-t-BS, slash-BS and the contaminated normal-BS distributions, being a flexible alternative to the use of the corresponding BS distribution or any other well-known compatible model, such as the log-normal distribution. A Gibbs sample algorithm with Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of the parameters. Moreover, some discussions on the model selection to compare the fitted models are given and case-deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback–Leibler divergence. The newly developed procedures are illustrated on a real data set previously analysed under BS regression models.
An Empirical Study on Authorship Verification for Low Resource Language Using Hyper-Tuned CNN Approach

Authorship verification is a crucial process employed to determine the authorship of a given text by analyzing distinct aspects of the writer’s style, such as vocabulary, syntax, and punctuation. This process has gained significant research attention in various domains, including intellectual property rights, plagiarism detection, cybercrime investigations, copyright infringement, and forensics. While extensive studies have been conducted on multiple languages worldwide, encompassing Western European languages like Italian and Spanish, as well as Asian languages such as Bengali and Chinese, the investigation of authorship verification in Urdu has been comparatively limited, despite its status as a prominent South Asian language. This limitation can be attributed to the intricate and distinctive morphology of Urdu, which necessitates specific methodologies that cannot be directly applied in the same manner as other languages. To bridge this gap, we propose an innovative approach for authorship verification in Urdu, leveraging Convolutional Neural Networks (CNNs) with three distinct hyper-tuned parameters: ADAM, SGD, and RMSProp. To facilitate the development of this approach, we have curated a new corpus called UAVC-22, specifically tailored for Urdu authorship verification. This corpus offers enhanced robustness in terms of authors’ classes and unique words. We have developed 9 authorship verification models, utilizing three different text embedding techniques, namely Word2Vec, GloVe, and FastText, we have performed a comparative analysis with traditional machine learning models such as Support Vector Machines (SVM) and Random Forest to assess the superiority and efficacy of the CNN-based approach. The optimized CNN-ADAM model with FastText achieved the highest accuracy of 98% for the Urdu dataset UAVC-22.
Parental Stress in the Parents of the Children Diagnosed with ADHD

Abstract Our study investigated factors associated with parental stress in the parents of children with ADHD. A total of 186 parents of children, with and without ADHD, were included. Acceptance and Action Questionnaire-II (AAQ-II), Parental Stress Index-Short Form (PSI-SF), Satisfaction with Life Scale (SWLS) were applied to both groups. Conners Parent Rating Scale-Revised Short Form (CPRS-RS) was given only to the case group. Analyses revealed AAQ-II and CPRS-Oppositional subscales were independent factors associated with PSI-SF (p < 0.001). The psychological flexibility of the parents and the level of oppositional symptoms were among the factors associated with parental stress; however, further research is required.
Two-wise and Three-wise Similarity, and Non-deductive Analogical Arguments

Analogical arguments have varying degrees of similarity, which helps us to understand their varying degrees of strength. Three-wise similarity claims – case C1 is more similar to C2 than it is to C3 – will be used to argue that assessing degrees of strength is both important and common in analogical arguments. Making the degree of argument strength a function of, among other things, degree of similarity, means the arguments are best understood non-deductively.
Two-dimensional discrete wavelet analysis of multiparticle event topology in heavy-ion collisions

The event-by-event analysis of multiparticle production in high energy hadron and nuclei collisions can be performed using the discrete wavelet transformation. The ring-like and jet-like structures in two-dimensional angular histograms are well extracted by wavelet analysis both for toy models and in Monte Carlo simulated events of nucleus–nucleus collisions at LHC energies. For the first time the method is applied to the jet-like events. The jet positions are located quite well by the discrete wavelet transformation of angular particle distribution even in the presence of the underlying soft process background in nucleus–nucleus collisions at LHC energies.
Cross-sectional Evaluation of Neurotology Fellowship Directors: A Present-day Snapshot of Leadership

Objective: To identify demographic, training, and career trends of neurotology fellowship directors (FDs). Study Design: Cross-sectional study. Setting: United States. Subjects: All 26 neurotology FDs identified using the American Neurotology Society (ANS) ACGME Accredited Neurotology Fellowship Program Directory, accessed November 2021. Main Outcome Measures: Data were collected via CVs, institutional biographies, and emailed questionnaires. Data collected includes age, gender, race and ethnicity, residency and fellowship training institution, time since training completion until FD, length of time as FD, and Hirsch-index (h-index). Results: Twenty-six FDs were identified, and 17/26 (65.4%) FDs responded to the questionnaire. The majority (23/26; 88.5%) were male. The mean age of male and female FDs was 56 versus 47 years, respectively. Of the 17 that responded to the survey, 82.4% (14/17) self-identified as Caucasian. The mean h-index was 25.4. Older age correlated with a higher h-index (r = 0.46, P = 0.019). The duration (mean ± SD, years) from fellowship graduation to FD appointment was 10.7 ± 8.1 and 6.3 ± 4.8 from institutional hire. Six (23.1%) FDs had secondary graduate degrees, and 9 (34.6%) held a leadership position at a national otolaryngology organization. Conclusion: This observational study assesses demographic data on current neurotology FDs in the United States with an analysis of gender disparities. The objective measures identified can provide a baseline for growth in FD leadership.
40-Year Incidence of Early-Onset Parkinson’s Disease in Southeast Minnesota

Background: Parkinson’s disease (PD) most commonly surfaces at middle age. An earlier onset is named early-onset Parkinson’s disease (EOPD), but the exact definition is a matter of ongoing scientific debate. Objective: To investigate 40-year EOPD incidence trends in a population-based cohort of parkinsonism in Olmsted County, Minnesota. Methods: We used the Rochester Epidemiology Project (REP) to identify all incident EOPD cases in Olmsted County, 1976–2015. A movement-disorder specialist reviewed all cases to confirm the EOPD diagnosis. For EOPD definition, we used two age cut-offs: motor-symptom onset at or before 50 and 55 years. Results: EOPD incidence was 1.43/100,000 person-years for ≤55 and 0.55/100,000 for ≤50 years. Men had a higher incidence in both groups [1.84 vs. 1.03 (p = 0.04); and 0.70 vs. 0.40 (p = 0.24), respectively]. EOPD incidence of patients with motor-symptom onset before age 55 increased from 1.02/100.000 person-year 1976–1985, to 1.32/100.000 person-year 2006–2015. A similar trend was observed when ≤50 years cut-off was used (0.28/100,000 person-years 1976–1985, to 0.59/100,000 person-year 2006–2015). However, negative binomial regression found no significant change in incidence per 10 years (RR = 1.04 and 1.24 in the two groups). Incidence was consistently higher in men than women. Median time from EOPD-symptom onset to death was shorter in the EOPD ≤55 group (21.9 years) compared to the EOPD ≤50 group (25.6 years). Conclusion: We observed an increased trend in the incidence of EOPD with both cut-off ages. Overall, incidence of EOPD was 1.43 (≤55) and 0.55 (≤50) cases per 100,000 person-years, higher in men.
Plasma Expansion Dynamics in Hydrogen Gas

Micro-plasma is generated in ultra-high-pure hydrogen gas, which fills the inside of a cell at a pressure of (1.08 ± 0.033) × 105 Pa by using a Q-switched neodymium-doped yttrium-aluminum-garnet (Nd:YAG) laser device operated at a fundamental wavelength of 1064 nm and a pulse duration of 14 ns. The micro-plasma emission spectra of the hydrogen Balmer alpha line, Hα, are recorded with a Czerny–Turner type spectrometer and an intensified charge-coupled device. The spectra are calibrated for wavelength and corrected for detector sensitivity. During the first few tens of nanoseconds after the initiation of optical breakdown, the significant Stark-broadened and Stark-shifted Hα lines mark the well-above hypersonic outward expansion. The vertical diameters of the spectrally resolved plasma images are measured for the determination of expansion speeds, which were found to decrease from 100 to 10 km/s for time delays of 10 to 35 ns. For time delays of 0.5 µs to 1 µs, the expansion speed of the plasma decreases to the speed of sound of 1.3 km/s in the near ambient temperature and pressure of the hydrogen gas.
Dielectric characterization and molecular interaction behavior in amide–amide binary mixture

ABSTRACT The frequency dependent dielectric relaxation spectra for binary mixture of dimethylacetamide with formamide and dimethylformamide at 11 different concentrations at 30°C over frequency range 10 MHz to 20 GHz is studied. The mixture exhibit a principle dispersion of the Davidson-Cole relaxation type. Dielectric constant (ϵ0), relaxation time (τ), excess dielectric constant (ϵE), excess inverse relaxation time (1/τ)E and Kirkwood correlation factor (g) have been determined and discussed in terms of the effect of NH2 and –CH3 side group on molecular dynamics and intermolecular hydrogen bonds. Conformational analysis of the formation of hydrogen bond is supported by the FTIR study.
Classification of Low- and High-Entropy File Fragments Using Randomness Measures and Discrete Fourier Transform Coefficients

This paper presents an approach to improve the file fragment classification by proposing new features for classification and evaluating them on a dataset that includes both low- and high-entropy file fragments. High-entropy fragments, belonging to compressed and encrypted files, are particularly challenging to classify because they lack exploitable patterns. To address this challenge, the proposed feature vectors are constructed based on the byte frequency distribution (BFD) of file fragments, along with discrete Fourier transform coefficients and several randomness measures. These feature vectors are tested using three machine learning models: Support vector machines (SVMs), artificial neural networks (ANNs), and random forests (RFs). The proposed approach is evaluated on the govdocs1 dataset, which is freely available and widely used in this field, to enable reproducibility and fair comparison with other published research. The results show that the proposed approach outperforms existing methods and achieves better classification accuracy for both low- and high-entropy file fragments.
Structural and Biochemical Insights into Bis(2-hydroxyethyl) Terephthalate Degrading Carboxylesterase Isolated from Psychrotrophic Bacterium Exiguobacterium antarcticum

This study aimed to elucidate the crystal structure and biochemically characterize the carboxylesterase EaEst2, a thermotolerant biocatalyst derived from Exiguobacterium antarcticum, a psychrotrophic bacterium. Sequence and phylogenetic analyses showed that EaEst2 belongs to the Family XIII group of carboxylesterases. EaEst2 has a broad range of substrate specificities for short-chain p-nitrophenyl (pNP) esters, 1-naphthyl acetate (1-NA), and 1-naphthyl butyrate (1-NB). Its optimal pH is 7.0, losing its enzymatic activity at temperatures above 50 °C. EaEst2 showed degradation activity toward bis(2-hydroxyethyl) terephthalate (BHET), a polyethylene terephthalate degradation intermediate. We determined the crystal structure of EaEst2 at a 1.74 Å resolution in the ligand-free form to investigate BHET degradation at a molecular level. Finally, the biochemical stability and immobilization of a crosslinked enzyme aggregate (CLEA) were assessed to examine its potential for industrial application. Overall, the structural and biochemical characterization of EaEst2 demonstrates its industrial potency as a biocatalyst.