Miracle-dz/UniFAR-FTUs huggingface dataset:
Incapacidade relacionada à dor lombar crônica: prevalência e fatores associados

A incapacidade relacionada a dor lombar cronica (DLC) e um fenomeno complexo e multifatorial. O objetivo desse estudo foi identificar a prevalencia e os fatores associados a incapacidade em pacientes com dor lombar cronica. Estudo transversal com amostra composta por 177 pacientes com DLC, de tres servicos de saude; que responderam ao formulario com dados demograficos, ao Inventario de Depressao de Beck, as Escalas Oswestry Disability Index, de autoeficacia para dor cronica, Tampa de Cinesiofobia e de Fadiga de Piper. A prevalencia de incapacidade foi de 65% (IC95%: 57,5 - 72,0) e era de moderada a grave em 80,7% dos pacientes. O modelo de regressao multipla identificou tres fatores independentemente associados a incapacidade: ausencia de trabalho remunerado, autoeficacia baixa e depressao. Os fatores associados a incapacidade identificados sao modificaveis. Intervencoes como recolocacao no trabalho, tratamento para a depressao e reconceitualizacao da crenca de autoeficacia podem ter um impacto importante na prevencao e reducao de incapacidade.
Validity and reliability of two pain assessment tools in Brazilian children and adolescents.

AIMS The aim of this research is to examine the validity and reliability of the Brazilian version of the Revised Faces Pain Scale and the Face, Legs, Activity, Cry, Consolability scale. BACKGROUND Several self-report and behavioural pain tools have been shown to have good psychometric properties for the evaluation of pain in children and adolescents. This study was designed to analyse the correlation between two pain scales in school-age children and adolescents. DESIGN This is a validation study. METHODS This research studied 90 children between 7-17 years old. They received care at the outpatient and the inpatient departments of the National Cancer Institute of Brazil. A self-report tool, the Revised Faces Pain Scale, was used by children and adolescents to measure their pain, while the observational Face, Legs, Activity, Cry, Consolability scale was used by the healthcare providers to measure pain. RESULTS The Face, Legs, Activity, Cry and Consolability scale presented a good internal consistency (Cronbach α coefficient = 0·76). There was a moderate-to-good correlation between the Face, Legs, Activity, Cry and Consolability scale and the Revised Faces Pain Scale scores (Spearman's coefficient = 0·74). CONCLUSION Findings support the reliability and the validity of the Face, Legs, Activity, Cry, Consolability scale and the Revised Faces Pain Scale as a measure of pain in the Brazilian population. RELEVANCE TO CLINICAL PRACTICE The validity and the reliability of both scales will improve pain evaluation and treatment in Brazilian children and adolescents, leading to a better pain control.
Conocimiento, actitud y práctica del uso de preservativos por presidiarias: prevención de las DST/HIV en el ámbito penitenciário

Se objetivo evaluar el conocimiento, la actitud y la practica de presidiarias respecto del uso del preservativo masculino y femenino como medida preventiva a las DST/HIV. Investigacion cuantitativa, evaluatoria del tipo Conocimiento, Actitud y Practica (CAP), involucrando 155 presidiarias. Datos recogidos de enero a marzo 2010 en la carcel femenina del Estado de Ceara. Aunque hubiesen oido hablar y/o supiesen la finalidad del uso, apenas 35 mujeres (22,63%) tenian conocimiento adecuado sobre el preservativo masculino y 11 (7,1%) sobre el femenino. Las actitudes fueron menos favorables respecto al uso en el sexo oral. La practica adecuada presento escasa representatividad, especialmente del femenino. La homo/bisexualidad, las cuestiones de genero, la falta de conocimiento y el dificil acceso a los preservativos representaron obstaculos a ser considerados en la promocion de la salud sexual del grupo estudiado.
University of Birmingham Oscillations and episodic memory

Brain oscillations are one of the core mechanisms underlying episodic memory. However, while some studies highlight the role of synchronized oscillatory activity, others highlight the role of desynchronized activity. We here describe a framework to resolve this conundrum and integrate these two opposing oscillatory behaviors. Speci ﬁ cally, we argue that the synchronization and desynchronization re ﬂ ect a division of labor between a hippocampal and a neocortical system, respectively. We describe a novel oscillatory framework that integrates synchronization and desynchronization mechanisms to explain how the two systems interact in the service of episodic memory. The Synchronization, Desynchronization Conundrum Brain oscillations (see Glossary) provide temporal windows for neural ﬁ ring and shape synaptic plasticity by synchronizing and desynchronizing neural assemblies. Oscillations therefore have a particularly high potential to shed light on the mechanisms underlying episodic memory [1,2]. However, over the past few years a conundrum has emerged regarding how brain oscillations relate to memory [3]. Whereas some studies highlight the role of synchronized activity, mainly in the theta ( (cid:1) 3 – 8 Hz) and gamma ( (cid:1) 40 – 80 Hz) frequencies [1], others highlight the role of desynchronized activity, mainly in the lower-frequency ranges (<20 Hz [4]). This conundrum exists at both an empirical and a theoretical level. Based on Hebb's seminal idea – neurons that ﬁ re together, wire together – a strong case can be made that synchrony is required for memory formation [1,2]. However, mathematical information theory, alongside physiological studies in animals, postulates that high synchrony, especially in the lower-frequency ranges (<20 Hz), reduces information coding [4 – 6]. We here aim to integrate these two seemingly incompatible concepts and present a separate) event details into rich associative memory traces [15]. How is this feat accomplished mechanistically? oscillations, at below ms, have recently been proposed as the prime candidate to facilitate learning-related synaptic changes such as spike-timing-dependent plasticity (STDP)/long-term potentiation (LTP) [1]. Does the hippocampus recruit such gamma oscillations in the service of episodic memory formation and retrieval? signals from the MTL [11]. Although it might be dif ﬁ cult for EEG/MEG studies to disentangle hippocampal from other activity within the MTL (e.g., parahippocampal gyrus, EC), oscillatory interactions between the MTL and the neocortex in principle can be studied. Additionally, multivariate analysis approaches using temporal and spatial pattern analysis can be utilized to test for the representation of information, which can then be linked to cortical desynchronization.
Comparisons of CRISPRs and viromes in human saliva reveal bacterial adaptations to salivary viruses

David T. Pride1, Julia Salzman2, and David A. Relman3,4,5 1Departments of Pathology and Medicine, University of California, San Diego 2Departments of Biochemistry and Statistics, Stanford University School of Medicine, Stanford, CA 3Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA 4Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 5Veterans Affairs Palo Alto Health Care System, Palo Alto, CA
The influence of medium-chain triglycerides supplementation in ultra-endurance exercise performance

In this type of competition there are the superand ultramarathons (from 84 km on), the Ironman Triathlon (3.8 km swimming; 180 km cycling; and 42 km running), contests that last more than 24 hours, such as the Ultraman Triathlon (10 km swimming; 421 km cycling; and 84 km running), cycling competitions that last up to 30 days (Tour de France, Vuelta Ciclista a España, Giro d’Italia, Race Across America), and, most recently, the Adventure Races, which include a number of “extreme sports”. The energetic expenditure in “ultra-endurance” competitions may vary from 5,000 kcal (triathlon with 2 km swimming; 90 km cycling; and 21 km running) up to 18,000 kcal (a 24-hour race)2. The average energetic expenditure at the Tour de France is estimated to be 6,500 kcal/day, reaching 9,000 kcal/day at the mountains. The energetic expenditure in a 5-day, 1,000 km ultra-marathon is of 59,079 kcal in average, with daily expenditure ranging from 8,600 and 13,770 kcal3. In a study carried out in Rio de Janeiro with participants of a triathlon competition (3.8 km swimming; 180 km cycling, and 42 km running) the energetic expenditure was estimated in 8,171.1 kcal ± 716.74. To face such high energetic demand, different strategies to improve performance have been developed. Recently, medium-chain triglycerides (MCT) supplementation combined or not with carbohydrates (CHO) has been investigated. MCT supplementation is intended to optimize the use of free fatty acids (FFA) as source of energy, and spare endogenous glycogen reserves for the final stages of the competition. It is suggested that the ability to sustain exercise may be enhanced with the increase of lipid supply3,5. Thus, the purpose of such review is to clarify how MCT may influence performance in long-duration contests.
Resiliência em idosos atendidos na Rede de Atenção Básica de Saúde em município do nordeste brasileiro

Objetiva-se avaliar a capacidade de resiliencia, a auto-estima e o apoio social em idosos atraves de um estudo exploratorio com uma amostra por conveniencia constituida por idosos usuarios da rede publica de saude do municipio de Natal-RN, Brasil, os quais responderam ao questionario e as Escalas de Resiliencia, Auto-estima e Apoio Social. Participaram 65 idosos com idade media de 71 anos, sendo 81% do sexo feminino, com baixas condicoes socio-economicas. Identificou-se que os idosos apresentam caracteristicas resilientes, auto-estima positiva e apoio social percebido como satisfatorio, mesmo diante das perdas e declinios vivenciados na velhice. Alem disso, verificaram-se correlacoes moderadas e positivas entre resiliencia e auto-estima, ja que na medida em que uma aumenta, a outra tambem se eleva. Assim, o ato de viver bem esse periodo critico parece ser favorecido pela resiliencia enquanto recurso propiciador do desenvolvimento e pelos fatores de protecao, importantes indicadores de saude.
Contribution à la planification de mouvement pour robots humanoïdes

This thesis is related to motion control and planning algorithms for humanoid robots. For such highly-parameterized systems, numerical methods are well adapted and have thus been the enter of increasing attention in the recent years. Among the prominent numerical schemes, we recognized the prioritized inverse kinematics and dynamics frameworks to hold key features to plan motion for humanoid robots, such as the possibility to control the motion while enforcing a strict priority order among tasks. We have, however, identified a lack of support of strict priority enforcement when inequality constraints are to be accounted for in the numerical schemes and we were successful in proposing a solution to this shortcoming. We also considered the problem of planning bipedal locomotion according to any given tasks. We proposed to model this problem as an inverse kinematics problem, by considering the kinematic structure of the robot and its walk path as a single unified structure that captures both the degrees of freedom of the robot which are actuated (motorized joints) and those which are not (position and orientation in space). The presented algorithms, which will be abundantly illustrated, have been implemented within the HPP (Humanoid Path Planner) project and validated on the humanoid robot HRP-2.
An Introduction to symbolic data analysis

The main aim of the symbolic approach in data analysis is to extend problems, methods and algorithms used on standard data to more complex data called "symbolic objects" in order to distinguish them from objects (described by numerical or categorical variables) treated by standard data nalysis methods. Symbolic objects extend classical objects of data analysis in two ways : first in case of individuals by giving the possibility of introducing in their definition, structured information, second, in case of sets or classes, by being intentionally defined. In both cases in order to represent uncertainty knowledge, it may be useful to use probabilities, possibilities (in case of vagueness and imprecision for instance) belief (in case of probabilities only known on parts and to express ignorance) that why, we introduce several kinds of symbolic objetcs : boolean, possibilist, probabilist and belief. We briefly present some of their qualities and properties, three theorems, show how probability, possibility and evidences theories may be extended on these objects. Some mixture decomposition problems on these objects are settled. We show that in some cases, fractals are well adapted to represent duality between symbolic objects. Sets of symbolic objects are represented by categories of different kinds (hierarchies, pyramids and lattices). Four kinds of data analysis problems including the symbolic extension are illustrated by several algorithms which induce knowledge from classical data or from a set of symbolic objects. Finally, important steps of a symbolic data analysis are described and illustrate by an example concerning road accidents.
Applications of Mössbauer Spectroscopy in Meteoritical and Planetary Science, Part I: Undifferentiated Meteorites

Mössbauer (nuclear γ-resonance) spectroscopy is a powerful technique that is actively used in various fields, from physics and chemistry to biology and medicine. Rudolf L. Mössbauer, who observed nuclear γ-resonance and published his results in 1958, received a Nobel Prize in physics in 1961 for this discovery. The 57Fe is the most widely used nucleus in Mössbauer spectroscopy. Therefore, a large variety of compounds containing iron can be studied by Mössbauer spectroscopy. It is well known that planetary matter contains various iron-bearing phases and minerals. Therefore, the extraterrestrial material from different meteorites, asteroids, and planets can be studied using 57Fe Mössbauer spectroscopy as additional powerful technique. Two parts of this review consider the results of more than 50 years of experience of Mössbauer spectroscopy applied for the studies of various meteorites, soils and rocks from the Moon and recent investigation of the Mars surface using two rovers equipped with miniaturized Mössbauer spectrometers. Part I will discuss known results on Mössbauer spectroscopy of undifferentiated meteorites, which are the most primitive and formed with the solar system.
COVID-19 Social Distancing Measures and Loneliness Among Older Adults

Abstract Objectives In response to the coronavirus disease 2019 (COVID-19) pandemic, older adults are advised to follow social distancing measures to prevent infection. However, such measures may increase the risk of loneliness. The current study aimed to investigate (a) whether social distancing measures, particularly limiting close social interactions, are associated with loneliness among older adults, and (b) whether the association between social distancing measures and loneliness is moderated by sociodemographic characteristics. Method Data were from the fourth wave (April 29 to May 26, 2020) of the nationally representative Understanding America Study COVID-19 Survey. We used data on adults 50 years or older (N = 3,253). Logistic regression models of loneliness were performed. Five indicators of social distancing measures were considered: (a) avoiding public spaces, gatherings, or crowds; (b) canceling or postponing social activities; (c) social visits; (d) no close contact (within 6 feet) with people living together; and (e) with people not living together. Results Cancelling or postponing social activities and avoiding close contact with people living together were associated with 33% (odds ratio [OR] = 1.33, confidence interval [CI] = 1.06−1.68, p < .05) and 47% (OR = 1.47, CI = 1.09−1.99, p < .05) greater odds of loneliness, respectively. Furthermore, limiting close contact with coresidents increased the probability of loneliness more for males, non-Hispanic Whites, and those with higher levels of education and income. Discussion Efforts should be made to help older adults maintain social connectedness with close others by virtual communication methods. Our findings also call special attention to vulnerable groups at elevated risks of loneliness, emphasizing the need for tailored interventions.
Deep Learning Approaches to Grasp Synthesis: A Review

Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two “supporting methods” around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.
VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization

Fingerprinting-based approaches are particularly suitable for deploying indoor positioning systems for pedestrians with minimal infrastructure costs. The accuracy of the method, however, strongly depends on the quality of collected labeled fingerprints within the calibration phase, which is a tedious process when done manually in a static fashion. We present VI-SLAM2tag, a system for auto-labeling of dynamically collected fingerprints using the visual-inertial simultaneous localization and mapping (VI-SLAM) module of ARCore. ARCore occasionally updates its internal coordinate system. Mapping the entire trajectory to a target coordinate system via a single transformation thus results in large drift effects. To solve this, we propose a strategy for determining locally optimal subtrajectory transformations. Our system is evaluated with respect to the accuracy of the generated position labels using a geodetic tracking system. We achieve an average labeling error of roughly 50 cm for trajectories of up to 15 minutes, which is sufficient for fingerprinting-based localization. We demonstrate this by collecting a multi-floor dataset including WLAN and IMU data and show how it can be used to train neural network based models that achieve a positioning accuracy of roughly 2 m. VI-SLAM2tag and the dataset are made publicly available.
Comments on "Testing Conditional Independence of Discrete Distributions"

In this short note, we identify and address an error in the proof of Theorem 1.3 in Canonne et al. (2018), a recent breakthrough in conditional independence testing. After correcting the error, we show that the general sample complexity result established in Canonne et al. (2018) remains the same.
Graph Trees with Attention

When dealing with tabular data, models based on regression and decision trees are a popular choice due to the high accuracy they provide on such tasks and their ease of application as compared to other model classes. Yet, when it comes to graph-structure data, current tree learning algorithms do not provide tools to manage the structure of the data other than relying on feature engineering. In this work we address the above gap, and introduce Graph Trees with Attention (GTA), a new family of tree-based learning algorithms that are designed to operate on graphs. GTA leverages both the graph structure and the features at the vertices and employs an attention mechanism that allows decisions to concentrate on sub-structures of the graph. We analyze GTA models and show that they are strictly more expressive than plain decision trees. We also demonstrate the beneﬁts of GTA empirically on multiple graph and node prediction benchmarks. In these experiments, GTA always outperformed other tree-based models and often outperformed other types of graph-learning algorithms such as Graph Neural Networks (GNNs) and Graph Kernels. Finally, we also provide an explainability mechanism for GTA, and demonstrate it can provide intuitive explanations.
LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia

Prior work has explored the writing challenges experienced by people with dyslexia, and the potential for new spelling, grammar, and word retrieval technologies to address these challenges. However, the capabilities for natural language generation demonstrated by the latest class of large language models (LLMs) highlight an opportunity to explore new forms of human-AI writing support tools. In this paper, we introduce LaMPost, a prototype email-writing interface that explores the potential for LLMs to power writing support tools that address the varied needs of people with dyslexia. LaMPost draws from our understanding of these needs and introduces novel AI-powered features for email-writing, including: outlining main ideas, generating a subject line, suggesting changes, rewriting a selection. We evaluated LaMPost with 19 adults with dyslexia, identifying many promising routes for further exploration (including the popularity of the “rewrite” and “subject line” features), but also finding that the current generation of LLMs may not surpass the accuracy and quality thresholds required to meet the needs of writers with dyslexia. Surprisingly, we found that participants’ awareness of the AI had no effect on their perception of the system, nor on their feelings of autonomy, expression, and self-efficacy when writing emails. Our findings yield further insight into the benefits and drawbacks of using LLMs as writing support for adults with dyslexia and provide a foundation to build upon in future research.
RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion

The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments - a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously. In this work, we take an important step towards this goal by introducing the notion of dual-layer diversity to the problem of sensor fusion in multi-agent tracking. We demonstrate that the fusion of complementary tracking modalities, - passive/relative (e.g., visual odometry) and active/absolute tracking (e.g., infrastructure-assisted RF localization) offer a key first layer of diversity that brings scalability while the second layer of diversity lies in the methodology of fusion, where we bring together the complementary strengths of algorithmic (for robustness) and data-driven (for accuracy) approaches. RoVaR is an embodiment of such a dual-layer diversity approach that intelligently attends to cross-modal information using algorithmic and data-driven techniques that jointly share the burden of accurately tracking multiple agents in the wild. Extensive evaluations reveal RoVaR's multi-dimensional benefits in terms of tracking accuracy (median of 15cm), robustness (in unseen environments), light weight (runs in real-time on mobile platforms such as Jetson Nano/TX2), to enable practical multi-agent immersive applications in everyday environments.