Miracle-dz/UniFAR-FTUs huggingface dataset:
Health-related quality of life in family caregivers of autistic adults

Introduction Family members of autistic individuals often provide support for their autistic relative throughout the lifespan which can lead to massive burden themselves. Reduced health-related Quality of Life (HRQoL) in family caregivers is assumed; however, only a handful studies on the HRQoL of family caregivers providing care to adult relatives exist as opposed to autistic children. Thus, the current study aimed to (i) investigate the current state of physical and mental HRQoL of family caregivers of autistic adults compared to the general population, and (ii) examine caregiver-related (e.g., age, subjective caregiver burden) and care recipient-related variables (e.g., symptom severity, utilization of formal services) explaining variance in the caregivers’ HRQoL. Methods N = 149 family caregivers completed a nationwide online survey, including the Short-Form Health Survey (SF-8) in order to assess the HRQoL. T-tests were used to compare the HRQoL of family caregivers with the general population. Bivariate correlational and multiple linear regression analyses were conducted in order to identify predictors explaining variance in family caregivers’ HRQoL. Results Family caregivers of autistic adults reported significantly lower physical (M = 46.71, SD = 8.72, Cohen’s d = 0.42) and mental HRQoL (M = 40.15, SD = 11.28, Cohen’s d = 1.35) compared to the general population. Multiple linear regression with the mental HRQoL as the outcome showed a significant model (F(11, 95) = 5.53, p < . 001, adj. R2 = . 32) with increased subjective burden explaining most of the variance in mental HRQoL (ß = .32, GDW = .141, p < .001). Multiple linear regression analysis with the outcome physical HRQoL did not reveal a statistically significant model (F(11,95) = 1.09, p = . 38). However, bivariate analyses also showed a positive correlation with the subjective caregiver burden (r= .20, p < .05). Discussion Findings highlight the need to consider HRQoL (and caregiver burden) of family caregivers of autistic adults in several healthcare settings to monitor a potential comprised health status in early stages, with the long-term goal to improve family caregivers’ HRQoL.
Sensing biological materials with differential RF broadband and resonating microsensors

This paper presents two dielectric sensors dedicated to biological sample diagnostic. The first one is a resonating sensor for the characterization of small liquid sample. The second one is a broadband non-invasive sensor sub-mm spatial resolution for melanoma detection. In both cases, a differential approach is adopted in order to enhance the sensor sensitivity by limiting the impact of ambient factors (skin thickness, temperature, humidity…).
Efficiency of Obstetric Services in Germany—The Role of Variation and Overheads

The number of obstetric departments in German hospitals has declined in the last decades. In particular, rural hospitals are challenged to sustain their delivery services. In this paper, we analyse the role of variation and overheads of obstetric departments from the perspective of current and future German hospital financing. For this purpose, we develop a Monte Carlo simulation model that analyses the workload of the labour room and the obstetric ward. The results show that a hospital with less than 640 deliveries per year cannot break even. In order to offer services 24 h per day, 365 days per year, five nurses, five midwives, and five gynaecologists are needed. This results in high fixed costs. At the same time, the variation coefficient of the labour room and the obstetric ward declines with an increasing number of deliveries. Consequently, small hospitals have a higher risk of over- and under-utilization in the course of the year. This paper acknowledges that economics is not the only decision dimension. The quality of the institution and the transport to the hospital have to be considered, as well as the population’s wish for nearby services. However, the simulations clearly demonstrate that unless the hospital financing system is changed fundamentally, the decline in the number of hospitals offering delivery services will continue.
Spatial and temporal divergence of the torquatus species group of the subterranean rodent Ctenomys

Subterranean rodents of the genus Ctenomys have experienced an explosive radiation and rapidly colonized the southern cone of South America. The torquatus group, one of the main groups of the genus, comprises several species and species complexes which inhabit the eastern part of the distribution of Ctenomys including southern Brazil, northern and central Uruguay and north-eastern Argentina. This group has undergone a high chromosomal diversification with diploid numbers varying from 41 to 70. The aim of this study was to investigate the origins of the torquatus group as well as its diversification patterns in relation to geography and cladogenesis. Based on mitochondrial cytochrome b nucleotide sequences we conducted a Bayesian multi-calibrated relaxed clock analysis to estimate the ages of the torquatus group and its main lineages. Using the estimated evolutionary rate we performed a continuous phylogeographic analysis, using a relaxed random walk model to reconstruct the geographic diffusion of the torquatus group in a temporal frame. The torquatus group originated during the early Pleistocene between 1.25 and 2.32 million years from the present in a region that includes the northwest of Uruguay and the southeast of the Brazilian state of Río Grande do Sul. Most lineages have dispersed early towards their present distribution areas going through subsequent range expansions in the last 800,000 – 700,000 years. Ctenomys torquatus went through a rapid range expansion for the last 200,000 years, becoming the most widespread species of the group. The colonization of the Corrientes and Entre Ríos Argentinean provinces supposes at least two crossing events across the Uruguay River between 1.0 and 0.5 million years before the present, in the context of a cold and dry paleoenvironment. The resulting temporal and geographic frame enables the comprehension of the incidence of both, the amplitude of distribution areas and divergence times into the patterns of chromosomal diversification found in the group.
Predicting Credit Risk in European P2P Lending: A Case Study of “Bondora” Using Supervised Machine Learning Techniques

Peer-to-Peer (P2P) lending has emerged as an attractive investment option for higher returns, contrasting with conventional bank deposits. However, the inherent risk of loan and interest non-repayment necessitates a thorough understanding of credit risk factors. This research delves into the credit risk analysis of one of Europe's prominent P2P lending platforms, “Bondora. ” Leveraging publicly available loan application data, supervised machine learning techniques are employed to investigate and identify pivotal factors affecting credit risk. The study evaluates the performance of cutting-edge machine learning models, including Random Forest, Logistic Regression, XGBoost, Regularized Logistic Regression, Decision Tree, and Naïve Bayes, in predicting credit risk within the “Bondora” platform. Notably, XGBoost emerges as the most effective model, boasting the highest accuracy and AUC in loan default prediction. Additionally, Random Forest showcases exceptional recall in detecting default instances. Through this comprehensive analysis, the research facilitates the selection of the most suitable credit risk prediction method for P2P lending networks like “Bondora. ” The study's insights empower investors to make informed decisions when considering future investments in such platforms, aiding in the management of potential risks and optimizing investment strategies.
Spoof Detection using Voice Contribution on LFCC features and ResNet-34

Biometric authentication, especially in speaker verification, has seen significant advancements recently. Despite these significant strides, compelling evidence highlights the ongoing vulnerability to spoofing attacks, requiring specialized countermeasures to detect various attack types. This paper specifically focuses on detecting replay, speech synthesis, and voice conversion attacks. In our spoof detection strategy, we employed linear frequency cepstral coefficients (LFCC) for front-end feature extraction and ResNet-34 for distinguishing between genuine and fake speech. By integrating LFCC with ResNet-34, we evaluated the proposed method using the ASVspoof 2019 dataset, PA (Physical Access), and LA (Logical Access). In our approach, we contrast using the entire utterance for feature extraction in both PA and LA datasets with an alternative method that extracts features from a specific percentage of the voice segment within the utterance for classification. In addition, we conducted a comprehensive evaluation by comparing our proposed method with the established baseline techniques, LFCC-GMM and CQCC-GMM. The proposed method demonstrates promising performance with an equal error rate (EER) of 3.11% and 3.49% for replay attacks (PA) in the development and evaluation datasets. For voice conversion and speech synthesis attacks (LA), the method achieves EERs of 0.16% in the development dataset and 6.89% in the evaluation dataset. The proposed method shows promising results in identifying spoof attacks for both PA and LA attacks.
LA-GEM: imputation of gene expression with incorporation of Local Ancestry

Gene imputation and TWAS have become a staple in the genomics medicine discovery space; helping to identify genes whose regulation effects may contribute to disease susceptibility. However, the cohorts on which these methods are built are overwhelmingly of European Ancestry. This means that the unique regulatory variation that exist in non-European populations, specifically African Ancestry populations, may not be included in the current models. Moreover, African Americans are an admixed population, with a mix of European and African segments within their genome. No gene imputation model thus far has incorporated the effect of local ancestry (LA) on gene expression imputation. As such, we created LA-GEM which was trained and tested on a cohort of 60 African American hepatocyte primary cultures. Uniquely, LA-GEM include local ancestry inference in its prediction of gene expression. We compared the performance of LA-GEM to PrediXcan trained the same dataset (with no inclusion of local ancestry) We were able to reliably predict the expression of 2559 genes (1326 in LA-GEM and 1236 in PrediXcan). Of these, 546 genes were unique to LA-GEM, including the CYP3A5 gene which is critical to drug metabolism. We conducted TWAS analysis on two African American clinical cohorts with pharmacogenomics phenotypic information to identity novel gene associations. In our IWPC warfarin cohort, we identified 17 transcriptome-wide significant hits. No gene reached are prespecified significance level in the clopidogrel cohort. We did see suggestive association with RAS3A to P2RY12 Reactivity Units (PRU), a clinical measure of response to anti-platelet therapy. This method demonstrated the need for the incorporation of LA into study in admixed populations.
Usefulness of DECAF Score as a Predictor of 30-day Mortality in Patients with Dyspnea Aged 60 Years

Objective: In this study, we aimed to investigate the prognostic values of dyspnea, eosinopenia, consolidation, acidemia, and atrial fibrillation (DECAF), BAP-65, and CURB-65 scores in predicting hospitalization and 30-day mortality in elderly patients who received at least one of the diagnoses of chronic obstructive pulmonary disease (COPD), asthma, community-acquired pneumonia (CAP), and congestive heart failure (CHF). Methods: Data from patients hospitalized for acute exacerbations of COPD, asthma, CAP, and CHF within 6 months from November 15, 2018 were obtained from hospital medical records. Clinical and laboratory parameters were examined, and discharge or hospitalization, intensive care unit (ICU) admission, and 30-day mortality were recorded. DECAF, CURB-65, and BAP-65 scores were calculated. Results: This retrospective study included 369 patients aged 60 years. The DECAF score was found to be significant in predicting hospitalization according to BAP-65 and CURB-65 (odds ratio: 2.054, 1.263, 1.404, respectively). When we divided the patients into two groups, those who died within 30 days and those who did not, the DECAF scores were significantly higher in the group with mortality (p<0.001), whereas there was no significant difference between the two groups in terms of CURB-65 (p=0.329) and BAP-65 scores (p=0.678). Conclusion: Our study demonstrated that the DECAF score was an effective predictor of hospitalization, need for ICU, and 30-day mortality in patients aged 60 years who presented with dyspnea and received at least one of the following diagnoses: COPD, asthma, CAP and CHF.
A Short Survey on Deep Learning-Based Frameworks for Knowledge Graph Applications

Knowledge Graphs (KG) are repositories of structured, machine-readable data stored as relational triples. DBpedia, Freebase, and YAGO are examples of KGs that have been playing a vital role in many applications, including question answering systems, recommendation systems, and expert systems. Deep learning is a subset of machine learning techniques that consists of layers of artificial neural networks. It is a relatively young technology that, in a very short amount of time, has achieved state-of-the-art performance in many fields of science, business, and government. For instance, convolutional networks have achieved major breakthroughs in processing image, video, and audio files. Moreover, recurrent networks have a similar effect in processing sequential data, such as texts and time series [1]. In this survey, we shed light on the development deep learning has brought about in two KG tasks: entity summarization and entity linking. Experiments on standard performance measures and benchmark datasets show that deep learning-based models generally outperform other models by a considerable margin.
Comparing the occurrence rate of gestational hypertension during pregnancy with frozen embryo transfer and natural pregnancy

Introduction: Recent researches have indicated that pregnancies with frozen embryo transfer are associated with the increment of risk of maternal and neonatal complications, especially hypertension during pregnancy. The present study aimed to compare the occurrence rate of gestational hypertension in pregnancy with frozen embryo transfer and normal pregnancy. Materials and Methods: This research, as a retrospective cross-sectional study, was performed on pregnant women with frozen embryo transfer (n = 97) and women with normal pregnancies (n = 164) referring to medical centers under the supervision of Ahvaz University of Medical Sciences in 2021. Women aged 18–35 were included in the study after week 20th of pregnancy. Maternal and neonatal outcomes including hypertensive disorders of pregnancy (including gestational hypertension and preeclampsia), preterm birth (before the week 37th), low birth weight (lower than 2500 g), neonatal asphyxia (Apgar score >7 in minute 5th), intrauterine growth restriction (IUGR) and bleeding in the first trimester of pregnancy were evaluated. The association between frozen embryo transfer and pregnancy outcomes was evaluated using multiple logistic regressions. Results: The findings of this study indicated that pregnancy hypertension was observed in 23 people (23.7%) from the frozen embryo transfer group vs. 18 people (11.0%) from the normal pregnancy group (P = 0.006). Frozen embryo transfer pregnancy has a higher risk of gestational hypertension (OR = 2.521, 95% CI: 1.281-4.962; P = 0.007), preterm birth (OR = 2.264, 95% CI: 1.335-3.840; P = 0.002), and low birth weight (OR = 2.017, 95% CI: 1.178-3.455; P = 0.011). However, the incidence of birth asphyxia (P = 0.850), intrauterine growth restriction (P = 0.068), first-trimester bleeding (P = 0.809), and placenta accreta (P = 0.143) did not show a significant difference between two types of normal pregnancy and frozen embryo transfer pregnancy. Conclusion: Frozen embryo transfer pregnancy was associated with a higher risk of maternal and neonatal complications, hypertension, preterm birth, and low birth weight compared to natural and spontaneous pregnancies.
Encoder Signal Analysis and its Application in Gear Fault Detection

Gear fault detection based on encoder signals has attracted attention in recent years, in which it is important to capture the instantaneous angular speed (IAS) jitters caused by the gear faults. The central difference method (CDM) is widely used to calculate the IAS. However, due to the quantization error of the encoder and the measurement noise in practice, it is often hard to accurately estimate the jitters of the IAS caused by the gear fault using the CDM directly. To address this issue, the scheme of encoder signal reconstruction and the synchronous average merging is proposed to improve the estimation accuracy of the IAS. First, the encoder signal is reconstructed according to the square wavenumber of the encoder corresponding to each tooth of the gear. Then, the time-synchronous averaging (TSA) process is performed on the reconstructed signal. Third, the IAS signal is estimated by the CDM. Finally, the blind deconvolution based on the cyclostationarity maximization (CYCBD) method is used to process the IAS signal to enhance the jitters caused by the gear fault. The simulation and experimental results support the proposed method.
Hybrid Materials: A Metareview

The field of hybrid materials has grown so wildly in the last 30 years that writing a comprehensive review has turned into an impossible mission. Yet, the need for a general view of the field remains, and it would be certainly useful to draw a scientific and technological map connecting the dots of the very different subfields of hybrid materials, a map which could relate the essential common characteristics of these fascinating materials while providing an overview of the very different combinations, synthetic approaches, and final applications formulated in this field, which has become a whole world. That is why we decided to write this metareview, that is, a review of reviews that could provide an eagle’s eye view of a complex and varied landscape of materials which nevertheless share a common driving force: the power of hybridization.
Data and Knowledge Base on the Basis of the Expanded Matrix Model of Their Representation for the Intelligent System of Road-Climatic Zoning of Territories

An extended matrix model of data and knowledge representation on the investigated area, as well as a matrix model of data representation on the territory under investigation, are proposed for the intelligent system of road-climatic zoning of territories (RCZT) - the main information technology of RCZT. A part of the West Siberian region has been selected as the investigated territory. The extended matrix model of knowledge representation is filled out by knowledge engineers with participation of highly qualified experts in the field of RCZT. The matrix model of data representation on the territory under investigation is filled out by persons concerned in RCZT of the motor-roads management system.
Safe Reinforcement Learning for Automated Vehicles via Online Reachability Analysis

—Ensuring safe and capable motion planning is paramount for automated vehicles. Traditional methods are limited in their ability to handle complex and unpredictable trafﬁc situations. Model-free reinforcement learning (RL) addresses this challenge by generalizing across different trafﬁc situations without requiring explicit knowledge of all possible outcomes. However, it also poses challenges due to its inherent lack of safety guarantees. To bridge this gap, we integrate online reachability analysis into model-free RL to provide real-time safety guarantees. Reachability analysis helps to identify unsafe states and actions, enabling provably safe decision-making in automated vehicles. We evaluate the effectiveness of our approach through extensive numerical experiments. Our results demonstrate that we can efﬁciently provide safety guarantees without impairing the performance of the learned agent.
The Concept of Prosociality in Later Life and Its Dimensions: A Scoping Review

Objective: The aim of this study was to clarify various aspects and dimensions of the prosociality concept in later life as an important concept that gains significance in people as they age. This concept has been expressed through a variety of dimensions in different studies. Method : This is a scoping review of the relevant literature on the concept of prosociality and its dimensions in later life, including quantitative and qualitative studies. The required data were collected from Web of Science, Scopus, PubMed, ProQuest, and Google Scholar databases between the years 1987 and 2022. Results: First, 877 articles were identified, and after the screening phase, 57 eligible studies were reviewed. Two main categories, prosocial dispositions and prosocial behaviors, and seven subcategories were extracted. The subcategories of prosocial dispositions include empathy, prosocial norms, innate tendencies, and generative desires. Prosocial behaviors subcategories include informal spontaneous helping, formal planned helping, and pro-environmental behaviors. Conclusion: The various aspects and dimensions of the prosociality concept in later life identified in this study can be used as a basis for assessing and planning the promotion of prosociality among older adults.
Glutamate’s Effects on the N-Methyl-D-Aspartate (NMDA) Receptor Ion Channel in Alzheimer’s Disease Brain: Challenges for PET Radiotracer Development for Imaging the NMDA Ion Channel

In an effort to further understand the challenges facing in vivo imaging probe development for the N-methyl-D-aspartate (NMDA) receptor ion channel, we have evaluated the effect of glutamate on the Alzheimer’s disease (AD) brain. Human post-mortem AD brain slices of the frontal cortex and anterior cingulate were incubated with [3H]MK-801 and adjacent sections were tested for Aβ and Tau. The binding of [3H]MK-801 was measured in the absence and presence of glutamate and glycine. Increased [3H]MK-801 binding in AD brains was observed at baseline and in the presence of glutamate, indicating a significant increase (>100%) in glutamate-induced NMDA ion channel activity in AD brains compared to cognitively normal brains. The glycine effect was lower, suggesting a decrease of the co-agonist effect of glutamate and glycine in the AD brain. Our preliminary findings suggest that the targeting of the NMDA ion channel as well as the glutamate site may be appropriate in the diagnosis and treatment of AD. However, the low baseline levels of [3H]MK-801 binding in the frontal cortex and anterior cingulate in the absence of glutamate and glycine indicate significant hurdles for in vivo imaging probe development and validation.