Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Different Forms of Child Maltreatment With Peer Victimization in Mexican Children and Adolescents

Objective: To examine the relationship between exposure to multiple forms of child abuse and neglect within the family context and peer victimization at school, accounting for the moderator effect of sex and educational level. Methods: Two thousand four hundred fifteen children and adolescents, aged 9 to 15 years, attending public schools in Mexico completed the Childhood Trauma Questionnaire-Short Form and a modified version of the Olweus' Bully/Victim Questionnaire. We used linear regression models to assess the association of five different forms of child abuse (emotional, physical, and sexual abuse, and emotional and physical negligence) with three forms of peer victimization (direct, indirect, and cyberbullying). Results: Direct forms of child abuse within the family (i.e., emotional, physical, and sexual abuse), but not neglect, were significantly and positively associated with a risk for peer victimization. In the fully adjusted models, emotional abuse was significantly associated with the three types of peer victimization: [indirect b = 0.48, t = 6.75, p < 0.001, direct (b = 0.47, t = 4.89, p < 0.001), and cyberbullying (b = 0.85, t = 5.45, p < 0. 001)]; while physical abuse was positive and significantly associated with direct victimization (b = 0.29, t = 3.28, p < 0.001). Boys suffering from sexual abuse within the family context showed higher levels of all subtypes of peer victimization. Students attending secondary school who suffered from sexual abuse showed higher levels of indirect victimization than did students attending primary schools. Conclusion: Child abuse within the family context seems to be associated with the risk of peer victimization. Preventive strategies to address bullying and promote resilience should take family factors into account. Interventions for high-risk families might be useful to prevent child multi-victimization.
Differential Effects of Ethical Education, Physical Hatha Yoga, and Mantra Meditation on Well-Being and Stress in Healthy Participants—An Experimental Single-Case Study

Traditionally, yoga is a multicomponent practice consisting of postures, breathing techniques, meditation, mantras, and ethics. To date, only a few studies have tried to dismantle the effects of each of these components and their combinations. To fill this gap, we examined the incremental effects of ethical education and physical Hatha yoga on mantra meditation using a single-case multiple-baseline design. This study was part of a project evaluating the new mind–body program Meditation-Based Lifestyle Modification. Fifty-seven healthy participants with no regular yoga or meditation practice were randomly assigned to three baselines (7, 14, and 21 days) and four conditions using a random number generator. The conditions were mantra meditation alone (MA), meditation plus physical yoga (MY), meditation plus ethical education (ME), and meditation plus yoga and ethical education (MYE). All the interventions lasted for 8 weeks and were run consecutively according to baseline length. During the baseline and treatment phases, participants received daily questionnaires measuring their well-being (WHO-5 Well-Being Index), stress (Perceived Stress Scale), and subjective experiences. Forty-two participants completed the treatment and were entered in the analyses. We analyzed our data using visual inspection, effect size estimation (Tau-U), and multilevel modeling. Almost all participants showed a longitudinal increase in well-being. Regarding between-group differences, participants who received ethical education exhibited the largest increases in well-being (Tau-U = 0.30/0.23 for ME/MYE), followed by participants in the MY condition (Tau-U = 0.12). Conversely, participants in the MA condition showed no change (Tau-U = 0.07). There was a tendency for the combined treatments to decrease stress. This tendency was strongest in the MY condition (Tau-U = –0.40) and reversed in the MA condition (Tau-U = 0.17). These results emphasize the incremental and differential effects of practicing meditation in combination with other practices from the eight-fold yoga path. This approach is valuable for better understanding the multifaceted practice of yoga. Clinical Trial Registration: www.ClinicalTrials. gov, identifier: NCT04252976.
Unpicking the Roles of DNA Damage Protein Kinases in Trypanosomatids

To preserve genome integrity when faced with DNA lesions, cells activate and coordinate a multitude of DNA repair pathways to ensure timely error correction or tolerance, collectively called the DNA damage response (DDR). These interconnecting damage response pathways are molecular signal relays, with protein kinases (PKs) at the pinnacle. Focused efforts in model eukaryotes have revealed intricate aspects of DNA repair PK function, including how they direct DDR pathways and how repair reactions connect to wider cellular processes, including DNA replication and transcription. The Kinetoplastidae, including many parasites like Trypanosoma spp. and Leishmania spp. (causative agents of debilitating, neglected tropical infections), exhibit peculiarities in several core biological processes, including the predominance of multigenic transcription and the streamlining or repurposing of DNA repair pathways, such as the loss of non-homologous end joining and novel operation of nucleotide excision repair (NER). Very recent studies have implicated ATR and ATM kinases in the DDR of kinetoplastid parasites, whereas DNA-dependent protein kinase (DNA-PKcs) displays uncertain conservation, questioning what functions it fulfills. The wide range of genetic manipulation approaches in these organisms presents an opportunity to investigate DNA repair kinase roles in kinetoplastids and to ask if further kinases are involved. Furthermore, the availability of kinase inhibitory compounds, targeting numerous eukaryotic PKs, could allow us to test the suitability of DNA repair PKs as novel chemotherapeutic targets. Here, we will review recent advances in the study of trypanosomatid DNA repair kinases.
Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

Background The pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling. Methods From the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets. Results Two molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors. Conclusion This research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.
Conservation of S20 as an Ineffective and Disposable IFNγ-Inducing Determinant of Plasmodium Sporozoites Indicates Diversion of Cellular Immunity

Despite many decades of research to develop a malaria vaccine, only one vaccine candidate has been explored in pivotal phase III clinical trials. This candidate subunit vaccine consists of a portion of a single Plasmodium antigen, circumsporozoite protein (CSP). This antigen was initially identified in the murine malaria model and shown to contain an immunodominant and protective CD8+ T cell epitope specific to the H-2Kd (BALB/c)-restricted genetic background. A high-content screen for CD8+ epitopes in the H2Kb/Db (C57BL/6)-restricted genetic background, identified two distinct dominant epitopes. In this study, we present a characterization of one corresponding antigen, the Plasmodium sporozoite-specific protein S20. Plasmodium berghei S20 knockout sporozoites and liver stages developed normally in vitro and in vivo. This potent infectivity of s20(-) sporozoites permitted comparative analysis of knockout and wild-type parasites in cell-based vaccination. Protective immunity of irradiation-arrested s20(-) sporozoites in single, double and triple immunizations was similar to irradiated unaltered sporozoites in homologous challenge experiments. These findings demonstrate the presence of an immunogenic Plasmodium pre-erythrocytic determinant, which is not essential for eliciting protection. Although S20 is not needed for colonization of the mammalian host and for initiation of a blood infection, it is conserved amongst Plasmodium species. Malarial parasites express conserved, immunogenic proteins that are not required to establish infection but might play potential roles in diverting cellular immune responses.
Orthotropic Non-hysteretic Permeability Model as a Lookup-table for FEA

Within this paper, we present an appropriate approach to prepare the data of orthotropic nonlinear soft magnetic materials for use in finite element analyses. Based on the co-energy density, an elliptic model for the interpolation between the principal directions is derived and extended to laminated materials. Instead of adapting the FE-code to incorporate the fundamental equations, a workflow is proposed to generate a lookup-table of the anisotropic nonlinear permeability which can be easily integrated in FE-programs like COMSOL Multiphysics. The modelled behaviour is shown for typical material classes and the lamination model is numerically validated.
Identification and Analysis of Limb Rehabilitation Signal Based on Wavelet Transform

The development of science and technology has promoted the extensive application of surface electromyography (sEMG) collection technique in real-time exercise testing, assistive judgment of rehabilitation therapy, and assessment of intelligent artificial limb application. However, there is a severe lacking of studies on pattern recognition based on effective signal, and evaluation of limb rehabilitation status. To make up for the gap, this paper explores the identification and analysis of limb rehabilitation signal based on wavelet transform. Specifically, the authors detailed the basic flow of sEMG signal generation in motor unit during limb rehabilitation exercise, and proposed a limb EMG pattern recognition method. Then, support vector machine (SVM) was selected to recognize the pattern of the EMG signal extracted from the limb rehabilitation exercise of patients, and to judge the rehabilitation status. Finally, wavelet thresholding was combined with total variation denoising (TVD) to effectively remove the noise from EMG signal. The proposed method was proved effective through experiments.
Synaptic Suppression Triplet‐STDP Learning Rule Realized in Second‐Order Memristors

The synaptic weight modification depends not only on interval of the pre‐/postspike pairs according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing of the preceding spike (triplet‐STDP). Triplet‐STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short‐term suppression effect of preceding spikes. Second‐order memristors with one state variable possessing short‐term dynamics work in a way similar to the biological system. In this work, the suppression triplet‐STDP learning rule is faithfully demonstrated by experiments and simulations using second‐order memristors. Furthermore, a leaky‐integrate‐and‐fire (LIF) neuron is simulated using a circuit constructed with second‐order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet‐STDP rule, are realized. The realized weight‐dependent pair‐ and triplet‐STDP rules are clearly in line with findings in biology. The physically realized triplet‐STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second‐order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain‐inspired computation systems.
Analysis of Normal and Adventitious Lung Sound Signals Using Empirical Mode Decomposition and Central Tendency Measure

Received: 20 January 2021 Accepted: 17 June 2021 Diagnosing chronic obstructive pulmonary disease (COPD) from lung sounds is time consuming, onerous, and subjective to the expertise of pulmonologists. The preliminary diagnosis of COPD is often based on adventitious lung sounds (ALS). This paper proposes to objectively analyze the lung sound signals associated with COPD. Specifically, empirical mode decomposition (EMD), a data adaptive signal decomposition technique suitable for analyzing non-stationary signals, was adopted to decompose non-stationary lung sound signals. The use of EMD on lung sound signal results in intrinsic mode functions (IMFs), which are symmetric and band limited. The analytic IMFs were then computed through the Hilbert transform, which reveals the instantaneous frequency content of each IMF. The Hilbert transformed signal is analytic, and has a complex representation containing real and imaginary parts. Next, the central tendency measure (CTM) was introduced to quantify the circular shape of the analytical IMF plot. The result was taken as a useful feature to distinguish normal lung sound signal with ALS. Simulation results show that the CTM of analytic IMFs has a strong ability to distinguish between normal lung sound signals and ALS.
Farm Animals' Behaviors and Welfare Analysis with IA Algorithms: A Review

Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.
Characterization of turbulent structures in tokamaks

The goal of this project is to use machine learning to analyze GPI measures and estimate the Z-axis velocity of the structures. To perform this task, we have implemented a convolutional neural network (CNN) to predict the speed of the plasma, on a synthetic dataset that we generated ourselves and real data provided to us by the lab. We have tuned some hyperparameters to optimize the models and obtain the best results possible. We were able to successfully train and optimize a CNN for each dataset, and overall we were able to have good estimation of the speeds on the real data.
Phenomena of Loneliness and Fear Caused by the Mass Media Threat in the Situation of COVID-19 Pandemic

Recent research shows significant effects of the pandemic COVID-19 situation information in mass media on the population’s mental health. These effects have to be impartially assessed and thoroughly analyzed in order to prevent mental and psychosomatic diseases, maladjustment to restrictions caused by a lockdown. Social media platforms are among the most widely used sources of information in the world, the easy and inexpensive access to the internet and a large number of social networks make them the most effective ways to disseminate information. The problem is that this information may be truthful or fake. Mass media play a crucial role in people’s lives in regular situation and is especially significant in the situation of lockdown due to COVID-19 spreading. It is obvious that for complete understanding of people’s anxiety and unexplainable fear social surveys using standard psychological tools should be used, although special scales for evaluating pandemic effects on people’s lives have not been created yet. The authors emphasize the need to take into account qualitative analysis of the entire completeness of stress effects in a situation of pandemic (virtual threat and information stress, deprivation, self-isolation, loneliness, socio-economic stress) and suggest a means to prevent stress-associated mental and psychosomatic disorders. Copyright © 2021 by Academic Publishing House Researcher s.r. o.
In-situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources

The Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in-situ power transfer to recharge UAV batteries hold potential in extending their mission time. In this paper, we study the use of far-field wireless power transfer (WPT) technique from specialized, transmitter UAVs (tUAVs) carrying Multiple Input Multiple Output (MIMO) antennas for transferring wireless power to receiver UAVs (rUAVs) in a mission. The tUAVs can fly and adjust their distance to the rUAVs to maximize energy transfer. The use of MIMO antennas further boost the energy reception by narrowing the energy beam toward the rUAVs. The complexity of their dynamic operating environment increases with the growing number of tUAVs, and rUAVs with varying levels of energy consumption and residual power. We propose an intelligent trajectory selection algorithm for the tUAVs based on a deep reinforcement learning model called Proximal Policy Optimization (PPO) to optimize the energy transfer gain. Simulation results demonstrate that with the use of PPO, the system achieves a tenfold flight time extension compared to no wireless recharging. Further, PPO outperforms the benchmark movement strategies of ’Traveling Salesman Problem’ and ’Low Battery First’ when used by the tUAVs.
Hard Marriage with Heavy Burdens: Labor Unions as Takeover Deterrents *

We examine the causal effect of unionization on a firm’s takeover exposure and merger gains. To establish causality, we use a regression discontinuity design that relies on “locally” exogenous variation generated by elections that pass or fail by a small margin of votes. Barely passing a union election leads to a significant reduction in a firm’s probability of receiving takeover bids. Conditional on receiving a takeover bid, barely unionized targets enjoy a lower announcement return, receive a lower offer premium, and experience longer bid duration. The negative effect of unions on targets’ takeover exposure and merger gains is more pronounced when the mergers are horizontal, the firms have large unions, and the unions locate in states without right-to-work laws or states with more union-friendly successor statutes. Bidders of unionized targets seem to have more experience in making merger deals, possess a higher bargaining power, and face less union threat by themselves. Our paper provides new insights into the real effects of unionization regarding the market for corporate control.
Towards Ultra-scale Exact Optimization Using Chapel

Tree-based search algorithms applied to combinatorial optimization problems are highly irregular and timeconsuming when it comes to solving big instances. Due to their parallel nature, algorithms of this class have been revisited for different architectures over the years, and these parallelization efforts have always been guided by the performance objective setting aside productivity. However, dealing with scalability implicitly induces the heterogeneity issue, which means that different programming models/languages, runtimes and libraries need to be employed together for efficiently exploiting all levels of parallelism of large-scale systems. As a consequence, efforts towards productivity are crucial for harnessing the future generation of supercomputers. In this talk proposal, we present our efforts towards productivity-aware ultra-scale tree search using the Chapel language. Four topics are covered in this document: the design and implementation of tree-search using Chapel, improving intra-node efficiency, the use of GPUs and future perspectives.
Automatic detection of available area for rooftop solar panel installations

—In this work, we used a state-of-the-art convolutional neural network architecture for automatic detection of available rooftop surfaces on aerial images. We labelled data and tuned on multiple hyper-parameters. Our best-tuned model has a F 1 -score of 0.77 and an intersection-over-union (IoU) of 0.62 on an unprocessed dataset, where many images might exhibit no available area.
Time to Homeownership and Mortgage Design: Income Sharing and Saving Incentive

Accessibility to homeownership in western countries, especially for middle to low income earners has decreased over time due to several factors such as stringent covenants, pressure of rental growth on household income expenditure and a negative gap between wage and house price growth. Moreover, young households face higher accumulated student loans and, in a steadily rising and strong rental market, they are not able to generate enough savings to cover the initial deposit necessary to become homeowners. We design an income sharing mortgage product where borrowers accept to pledge a portion of their future income to anticipate the time necessary to become homeowners by obtaining a higher LTV (up to 100%). Our analysis finds that this mortgage may be useful for lower income households in periods of higher uncertainty and that it may become less expensive in a high interest rate environment. Finally, this product also embeds an incentive to save, with potential benefits for the overall systemic risk of the banking sector. As a consequence we find that the default risk is not higher than a plain vanilla mortgage with lower LTV.