Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological distress and work and social adjustment in the COVID-19 pandemic: a cross-Country analysis

We investigate the determinants of several measures of psychological distress and work and social adjustment, using data from a large survey covering six countries and three continents over the COVID-19 pandemic. Our analysis reveals substantial cross-country heterogeneity and identifies a strong effect of COVID-19 specific measures of distress onto generic ones, but not the other way around. The results confirm the importance of controlling for individual characteristics, which help explain some of the cross-country differences. Finally, they also highlight specific categories of individuals who have recorded extremely high levels of psychological distress.
Screen use and internet addiction among parents of young children: A nationwide Canadian cross-sectional survey

Objectives To establish the factorial structure and internal consistency of the Internet Addiction Test (IAT) in parents, the level and correlates of problematic internet use, and patterns and types of screen use. Study design Data were collected through an online questionnaire about preconception health among Canadian women and men with ≥1 child. The questionnaire included the IAT and questions about time spent on screens by device type, use of screens during meals and in the bedroom, and perceptions of overuse. Factor analysis was completed to determine the factorial structure of the IAT, with multivariable linear regression used to determine correlates of the IAT. Results The sample included 1,156 respondents (mean age: 34.3 years; 83.1% female). The IAT had two factors: “impairment in time management” and “impairment in socio-emotional functioning” of which respondents had more impairment in time management than socio-emotional functioning. Based on the original IAT, 19.4% of respondents would be classified as having a mild internet use problem with 3.0% having a moderate or severe issue. In the multivariable model, perceived stress (b = .28, SE = .05, p < .001) and depressive symptoms (b = .24, SE = .10, p = .017) were associated with higher IAT scores. Handheld mobile devices were the most common type of screen used (mean = 3 hours/day) followed by watching television (mean = 2 hours/day). Conclusion Parents spent a significant portion of their time each day using screens, particularly handheld mobile devices. The disruption caused by mobile devices may hinder opportunities for positive parent-child interactions, demonstrating the need for resources to support parents ever-growing use of technologies.
Efficacy of Intravenous Lidocaine for Pain Relief in the Emergency Department: A Systematic Review and Meta-Analysis

Objective To compare the efficacy of intravenous (IV) lidocaine with standard analgesics (NSAIDS, opioids) for pain control due to any cause in the emergency department. Methods The electronic databases of PubMed, Embase, ScienceDirect, CENTRAL, and Google Scholar were explored from 1st January 2000 to 30th March 2021 and randomized controlled trials (RCTs) comparing IV lidocaine with a control group of standard analgesics were included. Results Twelve RCTs including 1,351 patients were included. The cause of pain included abdominal pain, renal or biliary colic, traumatic pain, radicular low back pain, critical limb ischemia, migraine, tension-type headache, and pain of unknown origin. On pooled analysis, we found no statistically significant difference in pain scores between IV lidocaine and control group at 15 min (MD: −0.24 95% CI: −1.08, 0.61 I2 = 81% p = 0.59), 30 min (MD: −0.24 95% CI: −1.03, 0.55 I2 = 86% p = 0.55), 45 min (MD: 0.31 95% CI: −0.66, 1.29 I2 = 66% p = 0.53), and 60 min (MD: 0.59 95% CI: −0.26, 1.44 I2 = 75% p = 0.18). There was no statistically significant difference in the need for rescue analgesics between the two groups (OR: 1.45 95% CI: 0.82, 2.56 I2 = 41% p = 0.20), but on subgroup analysis, the need for rescue analgesics was significantly higher with IV lidocaine in studies on abdominal pain but not for musculoskeletal pain. On meta-analysis, there was no statistically significant difference in the incidence of side-effects between the two study groups (OR: 1.09 95% CI: 0.59, 2.02 I2 = 48% p = 0.78). Conclusion IV lidocaine can be considered as an alternative analgesic for pain control in the ED. However, its efficacy may not be higher than standard analgesics. Further RCTs with a large sample size are needed to corroborate the current conclusions.
A high-dose inoculum size results in persistent viral infection and arthritis in mice infected with chikungunya virus

Chikungunya virus (CHIKV) is an emerging mosquito-transmitted alphavirus that leads to acute fever and chronic debilitating polyarthralgia. To date, the mechanism underlying chronic recurrent arthralgia is unknown. In the present study, newborn wild-type C57BL/6 mice were infected with CHIKV, and the virological and pathological features of CHIKV infection were analyzed over a period of 50 days. Acute viral infection was readily established by footpad inoculation of CHIKV at doses ranging from 10 plaque forming unit (PFU) to 106 PFU, during which inoculation dose-dependent viral RNA and skeletal muscle damage were detected in the foot tissues. However, persistent CHIKV was observed only when the mice were infected with a high dose of 106 PFU of CHIKV, in which low copy numbers (103−104) of viral positive strand RNA were continuously detectable in the feet from 29 to 50 dpi, along with a low level and progressive reduction in virus-specific CD8+ T cell responses. In contrast, viral negative strand RNA was detected at 50 dpi but not at 29 dpi and was accompanied by significant local skeletal muscle damage at 50 dpi when mild synovial hyperplasia appeared in the foot joints, although the damage was briefly repaired at 29 dpi. These results demonstrated that a high viral inoculation dose leads to viral persistence and progression to chronic tissue damage after recovery from acute infection. Taken together, these results provide a useful tool for elucidating the pathogenesis of persistent CHIKV infection and viral relapse-associated chronic arthritis.
EFFECTS OF PORE PRESSURE ON SLOPE STABILITY OF SANITARY LANDIFLLS

This paper, developed by students of thediscipline Sanitary Landfills from the EnvironmentalEngineering Course, shows the effects of pore pressure onthe stability of sanitary landfills by means of numericalsimulation. The cross section of a high volume, 8-year oldstable sanitary landfill was analyzed, considering typicalvalues of strength parameters and specific weight ofBrazilian municipal solid wastes (MSW). The safety factorwas calculated by a numerical code for several assumptionsconcerning pore pressure: a leachate piezometric level inthe landfill, an independent leachate piezometric level ineach construction cell, and pore pressure parameters r uranging from 0.1 to 0.5. Results indicate that a simplifyinghypothesis i.e. pore pressure represented by r u parametersyield safety factors similar to those obtained by morerealistic assumptions. Students studied landfill design,searched for reference parameter values, used a new code,analyzed results and contributed effectively to the design ofMSW landfills.Index Terms ⎯ municipal solid waste, sanitary landfill,stability, pore pressure.
Cryptosporidiosis outbreak in Amazonia, French Guiana, 2018

Background Cryptosporidiosis outbreaks in South America are poorly documented. In March 2018, 51 cases of cryptosporidiosis were reported in Maripasoula, a village located in a remote forest area along the border between Surinam and French Guiana. Method To identify the origin of the epidemic, we performed epidemiological, microbiological, and environmental investigations. Only the cases involving diarrhoea and Cryptosporidium-positive stool were considered as bona fide, while cases involving diarrhoea and close contact with a confirmed case were classified as “possible”. Results We identified 16 confirmed cases and 35 possible ones. Confirmed cases comprised nine children (median age of 18 months, range: 6–21), one immunocompromised adult and six soldiers. One child required a hospitalisation for rehydration. All 16 Cryptosporidium stools were PCR positive, and sequencing of the gp60 gene confirmed only one Cryptosporidium hominis subtype IbA10G2. Tap water consumption was the only common risk factor identified. Contamination of the water network with Cryptosporidium parvum subtype IIdA19G2 was found. Conclusion Water quality is a major public health issue in Amazonian French Guiana, especially for population at risk (children, people with comorbidity, travelers). For them, alternative water supply or treatment should be implemented.
Structural organization and dynamics of FCHo2 docking on membranes

Clathrin-mediated endocytosis (CME) is a central trafficking pathway in eukaryotic cells regulated by phosphoinositides. The plasma membrane phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2) plays an instrumental role in driving CME initiation. The F-BAR domain-only protein 1 and 2 complex (FCHo1/2) is among the early proteins that reach the plasma membrane, but the exact mechanisms triggering its recruitment remain elusive. Here, we show the molecular dynamics of FCHo2 self-assembly on membranes by combining minimal reconstituted in vitro and cellular systems. Our results indicate that PI(4,5)P2 domains assist FCHo2 docking at specific membrane regions, where it self-assembles into ring-like-shaped protein patches. We show that the binding of FCHo2 on cellular membranes promotes PI(4,5)P2 clustering at the boundary of cargo receptors and that this accumulation enhances clathrin assembly. Thus, our results provide a mechanistic framework that could explain the recruitment of early PI(4,5)P2-interacting proteins at endocytic sites.
The Grape Gene Reference Catalogue as a Standard Resource for Gene Selection and Genetic Improvement

Effective crop improvement, whether through selective breeding or biotech strategies, is largely dependent on the cumulative knowledge of a species’ pangenome and its containing genes. Acquiring this knowledge is specially challenging in grapevine, one of the oldest fruit crops grown worldwide, which is known to have more than 30,000 genes. Well-established research communities studying model organisms have created and maintained, through public and private funds, a diverse range of online tools and databases serving as repositories of genomes and gene function data. The lack of such resources for the non-model, but economically important, Vitis vinifera species has driven the need for a standardised collection of genes within the grapevine community. In an effort led by the Integrape COST Action CA17111, we have recently developed the first grape gene reference catalogue, where genes are ascribed to functional data, including their accession identifiers from different genome-annotation versions (https://integrape.eu/resources/genes-genomes/). We present and discuss this gene repository together with a validation-level scheme based on varied supporting evidence found in current literature. The catalogue structure and online submission form provided permits community curation. Finally, we present the Gene Cards tool, developed within the Vitis Visualization (VitViz) platform, to visualize the data collected in the catalogue and link gene function with tissue-specific expression derived from public transcriptomic data. This perspective article aims to present these resources to the community as well as highlight their potential use, in particular for plant-breeding applications.
A case report of myocardial infarction with non-obstructive coronary arteries as the initial presentation of eosinophilic granulomatosis with polyangiitis

Abstract Background Eosinophilic granulomatosis with polyangiitis (EGPA) is a multisystem disorder commonly affecting the lung and skin, with cardiovascular involvement found in up to 60% of patients. We present a case of myocardial infarction with non-obstructive coronary arteries (MINOCA) as the initial presentation of EGPA. Case summary A 52-year-old female with past medical history of asthma, recurrent sinusitis, and peripheral neuropathy presented to our hospital with chest pain, rash, acute vision loss, elevated troponin, and peripheral eosinophilia. Electrocardiogram showed no ischaemic changes and coronary angiography displayed normal coronary anatomy. On a subsequent visit, cardiac magnetic resonance (CMR) showed predominant focal anteroseptal and inferoseptal akinesis with focal sub-endocardial delayed enhancement, indicative of a myocardial infarction involving the septal branches of the left anterior descending artery. Due to the focal findings on CMR, peripheral eosinophilia, and rash, the patient was evaluated for EGPA. Rheumatologic workup and skin biopsy were suggestive of small vessel vasculitis. The patient was diagnosed with multi-organ EGPA, involving the coronaries, which was ultimately thought to be the aetiology of her MINOCA. Following steroid and monoclonal antibody therapy, the patient experienced notable improvement in her cardiac function at follow-up appointments. Discussion This is a unique case MINOCA as the initial presentation of EGPA. Considering the heterogeneous disease presentation of those diagnosed with MINOCA, utilization of CMR is essential to guide diagnosis and management of such patients.
A Dynamical System Perspective for Lipschitz Neural Networks

The Lipschitz constant of neural networks has been established as a key quantity to enforce the robustness to adversarial examples. In this paper, we tackle the problem of building $1$-Lipschitz Neural Networks. By studying Residual Networks from a continuous time dynamical system perspective, we provide a generic method to build $1$-Lipschitz Neural Networks and show that some previous approaches are special cases of this framework. Then, we extend this reasoning and show that ResNet flows derived from convex potentials define $1$-Lipschitz transformations, that lead us to define the {\em Convex Potential Layer} (CPL). A comprehensive set of experiments on several datasets demonstrates the scalability of our architecture and the benefits as an $\ell_2$-provable defense against adversarial examples.
Double-Opportunity Estimation via Altruism

Based on the notion of altruism, we present an approach to cooperative parameter estimation in a system comprising two information-sharing agents. The underlying assumption is that the overall two-agent scheme can reach desired performance level even if only one of the agents performs satisfactorily, hence there exist two independent opportunities to estimate. The notion of altruism motivates a definition of cooperative estimation optimality that generalizes the common definition of minimum mean-squared error optimality. Fundamental equations are derived for two types of altruistic cooperative estimation problems, corresponding to heterarchical and hierarchical setups. Although these equations are, generally, hard to solve, their solution in the Gaussian case is straightforward and only entails the computation of the largest eigenvalue of the conditional covariance matrix and its corresponding eigenvector. Moreover, in the Gaussian case, the performance improvement of the two altruistic cooperative estimation techniques over the conventional (egoistic) estimation approach is shown to depend on the problem's dimensionality and statistical distribution. In particular, the performance improvement grows with the dispersion of the spectrum of the conditional covariance matrix, rendering the presented estimation approach especially appealing in ill-conditioned problems. The validity of the solution in the Gaussian case is illustrated numerically.
Chemical Feedback of Pebble Growth: Impacts on CO depletion and C/O ratios

Observations of protoplanetary disks have revealed them to be complex and dynamic, with vertical and radial transport of gas and dust occurring simultaneously with chemistry and planet formation. Previous models of protoplanetary disks focused primarily on chemical evolution of gas and dust in a static disk, or dynamical evolution of solids in a chemically passive disk. In this paper, we present a new 1D method for modeling pebble growth and chemistry simultaneously. Gas and small dust particles are allowed to diffuse vertically, connecting chemistry at all elevations of the disk. Pebbles are assumed to form from the dust present around the midplane, inheriting the composition of ices at this location. We present the results of this model after 1 Myr of disk evolution around a 1M ⊙ star at various locations both inside and outside the CO snowline. We find that for a turbulent disk (α = 10−3), CO is depleted from the surface layers of the disk by roughly 1–2 orders of magnitude, consistent with observations of protoplanetary disks. This is achieved by a combination of ice sequestration and decreasing UV opacity, both driven by pebble growth. Further, we find the selective removal of ice species via pebble growth and sequestration can increase gas phase C/O ratios to values of approximately unity. However, our model is unable to produce C/O values of ∼1.5–2.0 inferred from protoplanetary disk observations, implying selective sequestration of ice is not sufficient to explain C/O ratios >1.
Penalized estimation of frailty‐based illness–death models for semi‐competing risks

Semi‐competing risks refer to the time‐to‐event analysis setting, where the occurrence of a non‐terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi‐competing risks arise in a broad range of clinical contexts, including studies of preeclampsia, a condition that may arise during pregnancy and for which delivery is a terminal event. Models that acknowledge semi‐competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi‐competing risks in high dimensions are lacking. Moreover, in such settings researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility—in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap, we propose a novel penalized estimation framework for frailty‐based illness–death multi‐state modeling of semi‐competing risks. Our approach combines non‐convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non‐convex optimization, and prove statistical error rate results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record.
TableQuery: Querying tabular data with natural language

This paper presents TableQuery, a novel tool for querying tabular data using deep learning models pre-trained to answer questions on free text. Existing deep learning methods for question answering on tabular data have various limitations, such as having to feed the entire table as input into a neural network model, making them unsuitable for most real-world applications. Since real-world data might contain millions of rows, it may not entirely fit into the memory. Moreover, data could be stored in live databases, which are updated in real-time, and it is impractical to serialize an entire database to a neural network-friendly format each time it is updated. In TableQuery, we use deep learning models pre-trained for question answering on free text to convert natural language queries to structured queries, which can be run against a database or a spreadsheet. This method eliminates the need for fitting the entire data into memory as well as serializing databases. Furthermore, deep learning models pre-trained for question answering on free text are readily available on platforms such as HuggingFace Model Hub (7). TableQuery does not require re-training; when a newly trained model for question answering with better performance is available, it can replace the existing model in TableQuery.
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization

Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many concerns. However, the semantical information in language instructions is usually entangled with task-specific state information, which hampers the learning of semantically invariant and reusable representations. In this paper, we propose a method to learn such representations called element randomization, which extracts task-relevant but environment-agnostic semantics from instructions using a set of environments with randomized elements, e.g., topological structures or textures, yet the same language instruction. We theoretically prove the feasibility of learning semantically invariant representations through randomization. In practice, we accordingly develop a hierarchy of policies, where a high-level policy is designed to modulate the behavior of a goal-conditioned low-level policy by proposing subgoals as semantically invariant representations. Experiments on challenging long-horizon tasks show that (1) our low-level policy reliably generalizes to tasks against environment changes; (2) our hierarchical policy exhibits extensible generalization in unseen new tasks that can be decomposed into several solvable sub-tasks; and (3) by storing and replaying language trajectories as succinct policy representations, the agent can complete tasks in a one-shot fashion, i.e., once one successful trajectory has been attained.
Semantic Annotation and Querying Framework based on Semi-structured Ayurvedic Text

Knowledge bases (KB) are an important resource in a number of natural language processing (NLP) and information retrieval (IR) tasks, such as semantic search, automated question-answering etc. They are also useful for researchers trying to gain information from a text. Unfortunately, however, the state-of-the-art in Sanskrit NLP does not yet allow automated construction of knowledge bases due to unavailability or lack of sufficient accuracy of tools and methods. Thus, in this work, we describe our efforts on manual annotation of Sanskrit text for the purpose of knowledge graph (KG) creation. We choose the chapter Dhanyavarga from Bhavaprakashanighantu of the Ayurvedic text Bhavaprakasha for annotation. The constructed knowledge graph contains 410 entities and 764 relationships. Since Bhavaprakashanighantu is a technical glossary text that describes various properties of different substances, we develop an elaborate ontology to capture the semantics of the entity and relationship types present in the text. To query the knowledge graph, we design 31 query templates that cover most of the common question patterns. For both manual annotation and querying, we customize the Sangrahaka framework previously developed by us. The entire system including the dataset is available from https://sanskrit.iitk. ac.in/ayurveda/ . We hope that the knowledge graph that we have created through manual annotation and subsequent curation will help in development and testing of NLP tools in future as well as studying of the Bhavaprakasanighantu text.