Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of internet use on depressive symptoms in middle-aged and older adults with functional disability: the mediating role of social isolation

Objective The popularization of the internet provides the possibility to improve the depressive symptoms (DS) and social isolation of middle-aged and older adults with functional disability. There was a significant correlation between internet use and DS in middle-aged and older adults with functional disability, but the relationship between internet use, social isolation, and DS remains to be confirmed. Methods Data were obtained from 7,955 middle-aged and older adults aged 45 years and older from the 2018 China Health and Retirement Longitudinal Study (CHARLS). Multiple linear regression models were used to analyze the association between internet use and DS, and the Sobel test was used to explore mediating models. Results Results showed that internet use was negatively associated with DS among Chinese middle-aged and older adults. Among them, social needs (B = −0.770, p = 0.041), and information reception (B = −1.067, p = 0.007) were significantly related to DS in middle-aged and older adults. Only information reception (B = −3.161, p = 0.031) was significantly related to DS among middle-aged and older adults with functional disability. Social isolation partially mediated the association between internet use and DS in middle-aged and older adults without functional disability, whereas it was fully mediated in middle-aged and older adults with functional disability. Conclusion This study indicates that when formulating health policies to promote the mental health of Chinese middle-aged and older adults, the use of the internet should not be overlooked.
Analysis of the Peritumoral Tissue Unveils Cellular Changes Associated with a High Risk of Recurrence

Simple Summary Recurrence in soft-tissue sarcomas represents a major drawback in the therapeutic management of patients. It may lead to amputation in cases of extreme STS or even to a therapeutical cul-de-sac in other localizations. The molecular identification of patients that are likely to develop a recurrence would represent a major breakthrough in adapting treatment and monitoring patients at risk. In this project, we identified two distinct cellular profiles of peritumoral tissue associated with different clinical behavior. This characterization could help clinicians to tailor neoadjuvant treatments based on a given patient’s risk. Abstract Background: The management of soft-tissue sarcoma (STS) relies on a multidisciplinary approach involving specialized oncological surgery combined with other adjuvant therapies to achieve optimal local disease control. Purpose and Results: Genomic and transcriptomic pseudocapsules of 20 prospective sarcomas were analyzed and revealed to be correlated with a higher risk of recurrence after surgery. Conclusions: A peritumoral environment that has been remodeled and infiltrated by M2 macrophages, and is less expressive of healthy tissue, would pose a significant risk of relapse and require more aggressive treatment strategies.
Foodborne Microbial Communities as Potential Reservoirs of Antimicrobial Resistance Genes for Pathogens: A Critical Review of the Recent Literature

Antimicrobial resistance (AMR) is a global and increasing threat to human health. Several genetic determinants of AMR are found in environmental reservoirs, including bacteria naturally associated with widely consumed fermented foods. Through the food chain, these bacteria can reach the gut, where horizontal gene transfer (HGT) can occur within the complex and populated microbial environment. Numerous studies on this topic have been published over the past decades, but a conclusive picture of the potential impact of the non-pathogenic foodborne microbial reservoir on the spread of AMR to human pathogens has not yet emerged. This review critically evaluates a comprehensive list of recent experimental studies reporting the isolation of AMR bacteria associated with fermented foods, focusing on those reporting HGT events, which represent the main driver of AMR spread within and between different bacterial communities. Overall, our analysis points to the methodological heterogeneity as a major weakness impairing determination or a causal relation between the presence of AMR determinants within the foodborne microbial reservoir and their transmission to human pathogens. The aim is therefore to highlight the main gaps and needs to better standardize future studies addressing the potential role of non-pathogenic bacteria in the spread of AMR.
Drivers for the deepening of severe European windstorms and their impacts on forecast quality

European windstorms are a high‐impact weather phenomenon, regularly inflicting substantial damages, both human and economic. This study examines a set of objectively selected intense European windstorms from the 1979–2015 period using re‐analysis and forecast products from the European Centre for Medium‐Range Weather Forecasts (ECMWF). The storms are first categorized with respect to their diabatic relative to baroclinic contribution to deepening using the pressure tendency equation, and secondly with respect to their track relative to the jet stream as the large‐scale element controlling storm deepening and propagation. As expected, baroclinic processes dominate the majority of storms, such that deepening is closely related to warm advection ahead of the cyclone centre. Contributions from diabatic processes vary strongly and exceed those from horizontal temperature advection in 10 out of 58 cases, with values of up to 60%. Remarkably, in several cases, planetary waves in the stratosphere appear to facilitate cyclogenesis but can also act to oppose deepening in a few cases. The diabatic contribution is significantly correlated with the time a given storm spends on the equatorward side of the jet, where there is greater potential for diabatic processes in the warm, moist air. In terms of forecast quality and consistent with previous studies, the storm core pressure is generally underestimated and storms tend to be too slow and shifted south in the forecast, particularly for longer lead times. These biases, however, reduce markedly with the improvement of the operational system over time. There is no systematic dependence of forecast behaviour on the diabatic contribution or track relative to the jet. In the future, some of these analyses should be repeated with homogeneous reforecast data to substantiate these findings better.
Behavioral and Sleep Disorders in Children and Adolescents following COVID-19 Disease: A Case-Control Study

Introduction: Recent studies show that neuropsychiatric disorders are the most frequent sequelae of COVID-19 in children. Purpose: Our work aimed to evaluate the impact of SARS-CoV-2 infection on behavior and sleep in children and adolescents. Materials and Methods: We enrolled 107 patients aged 1.5–18 years who contracted COVID-19 between one year and one month prior to data collection, referred to the University of Campania Luigi Vanvitelli in Italy. We asked their parents to complete two standardized questionnaires for the assessment of behavior (Child Behavior CheckList (CBCL)) and sleep (Sleep Disturbance Scale for Children (SLDS)). We analysed and compared the results with a control group (pre-COVID-19 pandemic). Results: In the COVID-19 group, the major results were found for sleep breathing disorders, sleep–wake transition disorders and disorders of initiating and maintaining sleep for the SDSC questionnaire, and internalizing scale, total scale and anxiety/depression for the CBCL questionnaire. The comparison of the CBCL results of the cases with the controls revealed statistically significant differences for the following items: internalizing scale, externalizing scale, somatic complaints, total score, thought problems [(p < 0.01)], anxious/depressed problems and withdrawn [(p < 0.001)]. Conclusions: COVID-19 has impacted children’s and adolescents’ mental health. Adolescents were the most affected patient group for internalizing problems, including anxiety and depression.
Anti-Aging Constituents from Pinus morrisonicola Leaves

Pinus morrisonicola Hayata is a unique plant species found in Taiwan. Previous studies have identified its anti-hypertensive, anti-oxidative, and anti-inflammatory effects. In this study, a bioactivity-guided approach was employed to extract 20 compounds from the ethyl acetate fraction of the ethanol extract of Pinus morrisonicola Hayata’s pine needles. The anti-aging effects of these compounds were investigated using HT-1080 cells. The structures of the purified compounds were confirmed through NMR and LC-MS analysis, revealing the presence of nine flavonoids, two lignans, one coumarin, one benzofuran, one phenylic acid, and six diterpenoids. Among them, PML18, PML19, and PML20 were identified as novel diterpene. Compounds 3, 4, and 5 exhibited remarkable inhibitory effects against MMP-2 and showed no significant cell toxicity at 25 μM. Although the purified compounds showed lower activity against Pro MMP-2 and Pro MMP-9 compared to the ethyl acetate fraction, we speculate that this is the result of synergistic effects.
Application and Resistance Mechanisms of Lenvatinib in Patients with Advanced Hepatocellular Carcinoma

Abstract Lenvatinib, a multitargeted tyrosine kinase inhibitor (TKI), is one of the preferred targeted drugs for the treatment of advanced hepatocellular carcinoma (aHCC). Since the REFLECT study showed that lenvatinib was noninferior to sorafenib in overall survival (OS), lenvatinib monotherapy has been widely used for aHCC. Moreover, lenvatinib combination therapy, especially lenvatinib combined with immune checkpoint inhibitors (ICIs), has shown more encouraging clinical results. However, drug development and comprehensive treatment have not significantly improved the prognosis, and lenvatinib resistance is often encountered in treatment. The underlying molecular mechanism of lenvatinib resistance is still unclear, and studies to solve drug resistance are ongoing. The molecular mechanisms of lenvatinib resistance in patients with aHCC include the regulation of signaling pathways, the regulation of noncoding RNAs, the impact of the immune microenvironment, tumor stem cell activation and other mechanisms. This review aims to (1) summarize the progress of lenvatinib in treating aHCC, (2) delineate the known lenvatinib resistance mechanisms of current therapy, and (3) describe the development of therapeutic methods intended to overcome these resistance mechanisms.
What is New in Metacognition Research? Answers from Current Literature

Metacognition has become a key element in learning processes. Its advantages include increasing awareness of one’s cognitive processes and promoting autonomous, critical, reflective, self-regulated, and self-directed learning. Given its increasing relevance in the academic and scientific community in recent years, this article maps the evolution of metacognition using the tree metaphor to establish the main research approaches emerging today. Through a network analysis of publications in WoS and Scopus and using tools such as Bibliometrix, ToS, and Gephi, the main documents, authors, countries, journals, and fundamental lines of work in metacognition research were identified: evaluation of metacognition in children, metacognition in student education, self-regulation skills, and metacognition in mathematical performance. The results suggest that the development of metacognitive skills has migrated to specific fields such as chemistry, mathematics, and arithmetic, with the latter two showing the most progress in implementing these practices to improve learning outcomes. Additionally, the findings highlight that while teachers consider metacognition necessary for their students’ learning, they do not always have the knowledge and skills to teach and apply it effectively.
Help Me Heal: A Reinforced Polite and Empathetic Mental Health and Legal Counseling Dialogue System for Crime Victims

The potential for conversational agents offering mental health and legal counseling in an autonomous, interactive, and vitally accessible environment is getting highlighted due to the increased access to information through the internet and mobile devices. A counseling conversational agent should be able to offer higher engagement mimicking the real-time counseling sessions. The ability to empathize or comprehend and feel another person’s emotions and experiences is a crucial quality that promotes effective therapeutic bonding and rapport-building. Further, the use of polite encoded language in the counseling reflects the nobility and creates a familiar, warm, and comfortable atmosphere to resolve human issues. Therefore, focusing on these two aspects, we propose a Polite and Empathetic Mental Health and Legal Counseling Dialogue System (Po-Em-MHLCDS) for the victims of crimes. To build Po-Em-MHLCDS, we first create a Mental Health and Legal Counseling Dataset (MHLCD) by recruiting six employees who are asked to converse with each other, acting as a victim and the agent interchangeably following a fixed stated guidelines. Second, the MHLCD dataset is annotated with three informative labels, viz. counseling strategies, politeness, and empathy. Lastly, we train the Po-Em-MHLCDS in a reinforcement learning framework by designing an efficient and effective reward function to reinforce correct counseling strategy, politeness and empathy while maintaining contextual-coherence and non-repetitiveness in the generated responses. Our extensive automatic and human evaluation demonstrate the strength of the proposed system. Codes and Data can be accessed at https://www.iitp. ac.in/ ai-nlp-ml/resources.html#MHLCD or https://github.com/Mishrakshitij/Po-Em-MHLCDS
Hydrodynamic behavior of boundary driven stochastic lattice gas models and interacting particle systems with conductances in random environments

We prove the hydrodynamic limit for a particle system in which particles may have different velocities. We assume that we have two infinite reservoirs of particles on the boundary: this is the so-called boundary driven process. The dynamics we considered consists of a weakly asymmetric simple exclusion process with collision among particles having different velocities. 1.
Six Decades of Spiral Density Wave Theory

The theory of spiral density waves had its origin approximately six decades ago in an attempt to reconcile the winding dilemma of material spiral arms in flattened disk galaxies. We begin with the earliest calculations of linear and nonlinear spiral density waves in disk galaxies, in which the hypothesis of quasi-stationary spiral structure (QSSS) plays a central role. The earliest success was the prediction of the nonlinear compression of the interstellar medium and its embedded magnetic field; the earliest failure, seemingly, was not detecting color gradients associated with the migration of OB stars whose formation is triggered downstream from the spiral shock front. We give the reasons for this apparent failure with an update on the current status of the problem of OB star formation, including its relationship to the feathering substructure of galactic spiral arms. Infrared images can show two-armed, grand design spirals, even when the optical and UV images show flocculent structures. We suggest how t.
AEMATOLOGICAL CHANGES IN MALARIA INFECTION AMONG PREGNANT WOMEN IN SOKOTO METROPOLIS NIGERIA

One of the most fatal illnesses that affects humans is malaria. This study was aimed at determining the haematological changes in pregnant malaria-infected women. One hundred blood samples were taken from pregnant women between the ages of 15 and 45. Samples were screened using standard methods. Out of 100 pregnant women examined, 28% had parasites whereas 72% did not. Based on age, women between the ages of 36 and 40 had the highest prevalence of malaria during pregnancy (33.3%). The haematological parameters of pregnant women with malaria infection and those who were not infected did not differ significantly, according to a Student T-test. Pregnant women's mean Packed cell volume was 28.43% compared to pregnant women who weren't sick of malaria. The mean value of Red blood cell in infected pregnant women 3.45x1012/l was lower than the non parasitaemic pregnant women 3.53x1012/l, the mean value of White blood cell 6.89x109/l was in the normal range for both infected and non-infected pregnant women, Red cell indices (MCH, MCHC) were low 27.00pg, 33.80pg in malaria infected pregnant women than non-infected pregnant women 28.23pg, 34.35pg, the mean value of MCV(82.02fl) was higher in pregnant women than the non- infected pregnant women (81.83fl), the mean Platelet value (217.1x109/l) was high in infected pregnant women than non-malaria pregnant women (213.1x109/l). Neutrophil and Lymphocyte were (60.92%, 27.49%) low in malaria infected pregnant women than the non parasitaemic (61.69%, 28.73%). Screening for malaria and haematological alterations in pregnancy will help in reducing the scourge of malaria infection.
A Bayesian hierarchical model for quantitative and qualitative responses

ABSTRACT In many science and engineering systems both quantitative and qualitative output observations are collected. If modeled separately the important relationship between the two types of responses is ignored. In this article, we propose a Bayesian hierarchical modeling framework to jointly model a continuous and a binary response. Compared with the existing methods, the Bayesian method overcomes two restrictions. First, it solves the problem in which the model size (specifically, the number of parameters to be estimated) exceeds the number of observations for the continuous response. We use one example to show how such a problem can easily occur if the design of the experiment is not proper; all the frequentist approaches would fail in this case. Second, the Bayesian model can provide statistical inference on the estimated parameters and predictions, whereas it is not clear how to obtain inference using the latest method proposed by Deng and Jin (2015), which jointly models the two responses via constrained likelihood. We also develop a Gibbs sampling scheme to generate accurate estimation and prediction for the Bayesian hierarchical model. Both the simulation and the real case study are shown to illustrate the proposed method.
Investigations of an Accelerometer-based Disturbance Feedforward Control for Vibration Suppression in Adaptive Optics of Large Telescopes

Adaptive Optics (AO) systems in large telescopes do not only correct atmospheric phase disturbances, but they also telescope structure vibrations induced by wind or telescope motions. Often the additional wavefront error due to mirror vibrations can dominate the disturbance power and contribute significantly to the total tip-tilt Zernike mode error budget. Presently, these vibrations are compensated for by common feedback control laws. However, when observing faint natural guide stars (NGS) at reduced control bandwidth, high-frequency vibrations (>5 Hz) cannot be fully compensated for by feedback control. In this paper, we present an additional accelerometer-based disturbance feedforward control (DFF), which is independent of the NGS wavefront sensor exposure time to enlarge the “effective servo bandwidth”. The DFF is studied in a realistic AO end-to-end simulation and compared with commonly used suppression concepts. For the observation in the faint (>13 mag) NGS regime, we obtain a Strehl ratio by a factor of two to four larger in comparison with a classical feedback control. The simulation realism is verified with real measurement data from the Large Binocular Telescope (LBT); the application for on-sky testing at the LBT and an implementation at the E-ELT in the MICADO instrument is discussed.
Delay-Optimal Computation Offloading in Large-Scale Multi-Access Edge Computing Using Mean Field Game

In large-scale multi-access edge computing (MEC) networks, each device should make the computation offloading decision distributively. In this paper, we target on a delay-optimal computation offloading problem in large-scale MEC systems, where each task has two properties: data size and computation amount. Because the detailed state information of massive devices are huge in large-scale systems, we propose a distributed computation offloading algorithm using the mean field game (MFG). To design the distributed computation offloading algorithm, we first formulate the delay-optimal computation offloading problem as a Markov decision process (MDP) and derive the Hamilton-Jaccobi-Bellman (HJB) equation with the unknown task allocation proportion, where the combined influence from other devices and MEC servers should be estimated. Based on MFG, we obtain the Fokker-Planck-Kolmogorov (FPK) equation to describe the evolution of the system’s collective behavior, with the influence from other devices and MEC servers formulated as the mean field. To solve the large-scale problem with the unknown allocation proportion, we propose a optimal computation offloading algorithm based on the generative adversarial networks (GAN) structure. For the generator, we generate the unknown task allocation proportion due to its non-calculability and insufficient dataset. For the discriminator, we train the value function, and propose a water-filling algorithm to prioritize the task offloading. Finally, the simulation results evaluate the performance of the proposed algorithm and show the performance gain compared to conventional algorithms.