Miracle-dz/UniFAR-FTUs huggingface dataset:
Dose response of running on blood biomarkers of wellness in the generally healthy

Exercise is effective toward delaying or preventing chronic disease, with a large body of evidence supporting its effectiveness. However, less is known about the specific healthspan-promoting effects of exercise on blood biomarkers in the disease-free population. In this work, we examine 23,237 generally healthy individuals who self-report varying weekly running volumes and compare them to 4,428 generally healthy sedentary individuals, as well as 82 professional endurance athletes. We estimate the significance of differences among blood biomarkers for groups of increasing running levels using analysis of variance (ANOVA), adjusting for age, gender, and BMI. We attempt and add insight to our observational dataset analysis via two-sample Mendelian randomization (2S-MR) using large independent datasets. We find that self-reported running volume associates with biomarker signatures of improved wellness, with some serum markers apparently being principally modified by BMI, whereas others show a dose-effect with respect to running volume. We further detect hints of sexually dimorphic serum responses in oxygen transport and hormonal traits, and we also observe a tendency toward pronounced modifications in magnesium status in professional endurance athletes. Thus, our results further characterize blood biomarkers of exercise and metabolic health, particularly regarding dose-effect relationships, and better inform personalized advice for training and performance.
Accelerometer-based sedentary behaviour and physical activity are associated with the gut microbiota in 8507 individuals from the population-based SCAPIS

Objective: Population-based studies investigating the relationship between physical activity and the gut microbiota composition have mainly relied on self-reported activity, potentially influenced by reporting bias. Here, we investigated associations of accelerometer-based sedentary behaviour and physical activity with the gut microbiota composition and functional profile in the large Swedish CArdioPulmonary bioImage Study. Methods: In 8507 participants aged 50-65, the proportion of time in sedentary (SED), moderate-intensity (MPA), and vigorous-intensity (VPA) physical activity were estimated with hip-worn accelerometer. The gut microbiota was profiled using shotgun metagenomics of fecal samples. We fitted multivariable regression models, and adjusted for sociodemographic, lifestyle, and technical covariates while also accounting for multiple testing. Results: Overall, SED and MPA were associated with microbiota species in opposite directions. For example, the strongest positive regression coefficient for MPA and the strongest negative for SED were with Prevotella copri, a plant-polysaccharide-degrading bacteria. Species associated with VPA aligned with the MPA associations, although with clear discrepancies. For instance, Phocaeicola vulgatus was negatively associated with MPA, while the association with VPA was non-significant and in the positive direction. Additional adjustment for dietary variables or adiposity attenuated some of the associations. For the functional profile, MPA and VPA were generally associated with lower capacity for amino acid degradation. Conclusion: Our findings suggest that sedentary behaviour and physical activity are associated with a similar set of gut microbiota species and functions, but in opposite directions. Furthermore, the intensity of physical activity may have specific effects on certain species of the gut microbiota.
The physiological and clinical-behavioral effects of HRV biofeedback in adolescents with autism: a pilot randomized controlled trial

Background Adolescents with autism present lower levels of cardiac vagal modulation in comparison to typically developing peers. These lower values are also associated with psychosocial and behavioral problems. Heart Rate Variability Biofeedback (HRVB) was therefore suggested as an interesting avenue for further exploration since it focusses on the up-regulation of cardiac vagal modulation and has demonstrated positive effects on mental health outcomes. However, scarce evidence was present regarding the effectiveness of HRVB in this population. It was hypothesized that HRVB would increase the level of cardiac vagal modulation in adolescents with autism which would result in positive effects on physiological outcome measures and psychosocial parameters. Furthermore, it was hypothesized that a home-based, non-supervised HRVB training would be feasible in this population. Methods A single-blind, randomized sham-controlled pilot trial was used. During the initial single-blind phase, adolescents with autism performed supervised HRVB (n=24) or a sham training (n=20) for five weeks with one guided session per week and home-based practice (20 min) on the remaining days. In the subsequent follow-up period of five weeks, half of the adolescents of each group received HRVB training at home, in a non-supervised manner, whereas the other subset did not practice during that period. A combination of physiological, cortisol and behavioral data was collected during standardized stress-provoking assessments before (T0) and after each training period (T1 and T2). Results Supervised HRVB resulted in a late increase in cardiac vagal modulation in adolescents with autism. Heart rate increased and cortisol decreased significantly immediately after the supervised HRVB training, but none of these effects remained after the follow-up period of five weeks. None of the outcome measures on psychosocial functioning and self-reported stress revealed a significant change following the supervised HRVB training. The home-based HRVB training was feasible in this population but with a significant decrease in compliance rate. However, adolescents reported significantly lower symptoms of stress after this training period, regardless of the prior training (sham or HRVB). Conclusion HRVB is feasible and effective in adolescents with autism as demonstrated by late-emerging increases in cardiac vagal modulation and less self-reported symptoms of stress. Researchers are encouraged to replicate this study with a larger sample size and to further explore the possible working mechanisms of HRVB. Keywords Heart Rate Variability Biofeedback, Autism Spectrum Disorder, Adolescence, Cardiac Vagal Modulation
Clinical and sociodemographic predictors of hearing aid use in infants aged 0-2 with permanent childhood hearing loss: a retrospective cohort single-site pilot study

Abstract Objectives: The aim of this study was to identify differences in hearing aid use among infants aged 0-2 years with respect to clinical and sociodemographic factors in an attempt to better predict which patients and families may benefit from extra support in the early stages of hearing aid fitting to encourage optimum usage. The secondary aim was to investigate how hearing aid use changed over the first two years post-fitting. Methods: A retrospective review of records was performed on 252 patients, aged 0-2 years with permanent childhood hearing loss from a single-site hospital who were fitted with hearing aids between 2005-2022. Ninety-six patients met the inclusion criteria. Datalogging values were collected for six different time points post fitting that coincided with their routine clinical follow-up appointments: 2 weeks, 6 weeks, 6 months, 12 months, 18 months and 24 months. Clinical and sociodemographic information was also collected for each participant. This included sex, average pure tone threshold, unilateral vs bilateral use, speech intelligibility score, additional disabilities, Index of Multiple Deprivation, Income Decile, Education and Skills Decile, Income Deprivation Affecting Children Index (IDACI), ethnicity and home language. Results: The datalogging results indicated a median average of 4.67 hours (3.0-7.3) hours per day use across patients and across the first two years post-fitting. Differences in datalogging according to IDACI Decile was significant (p=0.01), suggesting that infants from the more deprived groups (1-5) used their devices less. All other predictors did not reach statistical significance. There was insufficient data to investigate change in hearing aid use over the first two years post-fitting. Discussion: There were significant amounts of missing datalogging information. Some of the missing data was attributed by the clinical teams to lost hearing aids or unattended appointments. It is also believed that time restraints in clinic are the primary barrier. The datalogging values are lower than expected. It is considered that up to 12 hours per day use is necessary for good speech and language development. The findings highlighted that many families struggle to achieve optimal hearing aid usage in their infants. There was an association between daily device use and one socioeconomic status predictor. Conclusions: Average datalogging values for daily hearing device use were lower than considered optimal for 0-2 year olds with permanent childhood deafness. The only factor related to these findings was IDACI Decile indicating that infants from more deprived backgrounds may achieve lower hearing aid usage than those from the lesser deprived regions. This finding needs to be verified on a larger scale and better understood to explore potential approaches to overcome the problems. The role of multiple factors also needs to be explored with a larger sample size. This would require a multi-centre study to be conducted.
Blood RNA biomarkers for tuberculosis screening in people living with HIV prior to anti-retroviral therapy initiation: A diagnostic accuracy study

Background: Undiagnosed tuberculosis (TB) remains a major threat for people living with HIV (PLHIV). Multiple blood transcriptomic biomarkers have shown promise for TB diagnosis. We sought to evaluate their diagnostic accuracy and clinical utility for systematic pre-antiretroviral therapy (ART) TB screening. Methods: We enrolled consecutive adults referred to start ART at a community health centre in Cape Town, South Africa, irrespective of symptoms. Sputa were obtained (using induction if required) for two liquid cultures. Whole-blood RNA samples underwent transcriptional profiling using a custom Nanostring gene-panel. We measured the diagnostic accuracy of seven candidate RNA biomarkers for positive Mycobacterium tuberculosis culture reference standard, using area under the receiver-operating characteristic curve (AUROC) analysis, and sensitivity/specificity at pre-specified thresholds (two standard scores above the mean of healthy controls; Z2). Clinical utility was assessed using decision curve analysis. We compared performance to CRP (threshold [≥]5mg/L), World Health Organisation (WHO) four-symptom screen (W4SS) and the WHO target product profile for TB triage tests. Results: A total of 707 PLHIV were included, with median CD4 count 306 cells/mm3. Of 676 with available sputum culture results, 89 (13%) had culture-confirmed TB. The seven RNA biomarkers were moderately to highly correlated (Spearman rank coefficients 0.42-0.93) and discriminated TB culture-positivity with similar AUROCs (0.73-0.80), but none statistically better than CRP (AUROC 0.78; 95% CI 0.72-0.83). Diagnostic accuracy was similar across CD4 count strata, but lower among W4SS-negative (AUROCs 0.56-0.65) compared to W4SS-positive participants (AUROCs 0.75-0.84). The RNA biomarker with highest AUROC point estimate was a 4-gene signature (Suliman4; AUROC 0.80; 95% CI 0.75-0.86), with sensitivity 0.83 (0.74-0.90) and specificity 0.59 (0.55-0.63) at Z2 threshold. In decision curve analysis, Suliman4 and CRP had similar clinical utility to guide confirmatory TB testing, but both had higher net benefit than W4SS. In exploratory analyses, an approach combining CRP ([≥]5mg/L) and Suliman4 ([≥]Z2) had sensitivity of 0.80 (0.70-0.87), specificity of 0.70 (0.66-0.74) and higher net benefit than either biomarker alone. Interpretation: RNA biomarkers showed better clinical utility to guide confirmatory TB testing for PLHIV prior to ART initiation than symptom-based screening, but their performance did not exceed that of CRP, and fell short of WHO recommended targets. Interferon-independent approaches may be required to improve accuracy of host-response biomarkers to support TB screening pre-ART initiation.
A Survey of Graph-Based Deep Learning for Anomaly Detection in Distributed Systems

Anomaly detection is a crucial task in complex distributed systems. A thorough understanding of the requirements and challenges of anomaly detection is pivotal to the security of such systems, especially for real-world deployment. While there are many works and application domains that deal with this problem, few have attempted to provide an in-depth look at such systems. In this survey, we explore the potentials of graph-based algorithms to identify anomalies in distributed systems. These systems can be heterogeneous or homogeneous, which can result in distinct requirements. One of our objectives is to provide an in-depth look at graph-based approaches to conceptually analyze their capability to handle real-world challenges such as heterogeneity and dynamic structure. This study gives an overview of the State-of-the-Art (SotA) research articles in the field and compare and contrast their characteristics. To facilitate a more comprehensive understanding, we present three systems with varying abstractions as use cases. We examine the specific challenges involved in anomaly detection within such systems. Subsequently, we elucidate the efficacy of graphs in such systems and explicate their advantages. We then delve into the SotA methods and highlight their strength and weaknesses, pointing out the areas for possible improvements and future works.
Decomposition of fuzzy relations: An application to fuzzy preferences

In this article, working in the spirit of the classical Arrovian models in the fuzzy setting and their possible extensions, we go deeper into the study of some type of decompositions defined by t-norms and t-conorms. This allows us to achieve characterizations of existence and uniqueness for such decompositions and we provide rules to obtain them under some specific conditions. We conclude by applying such results to the study of fuzzy preferences, a key concept in Fuzzy Social Choice.
Exploration on HuBERT with Multiple Resolutions

Hidden-unit BERT (HuBERT) is a widely-used self-supervised learning (SSL) model in speech processing. However, we argue that its fixed 20ms resolution for hidden representations would not be optimal for various speech-processing tasks since their attributes (e.g., speaker characteristics and semantics) are based on different time scales. To address this limitation, we propose utilizing HuBERT representations at multiple resolutions for downstream tasks. We explore two approaches, namely the parallel and hierarchical approaches, for integrating HuBERT features with different resolutions. Through experiments, we demonstrate that HuBERT with multiple resolutions outperforms the original model. This highlights the potential of utilizing multiple resolutions in SSL models like HuBERT to capture diverse information from speech signals.
The Fisher Geometry and Geodesics of the Multivariate Normals, without Differential Geometry

Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling measure of statistical distance, and paths of shorter distance improve sampling techniques that leverage a sequence of distributions in their operation. Unfortunately, even for a distribution as generally tractable as the multivariate normal distribution, this information geometry proves unwieldy enough that closed-form solutions for shortest-distance paths or their lengths remain unavailable outside of limited special cases. In this review we present for general statisticians the most practical aspects of the Fisher geometry for this fundamental distribution family. Rather than a differential geometric treatment, we use an intuitive understanding of the covariance-induced curvature of this manifold to unify the special cases with known closed-form solution and review approximate solutions for the general case. We also use the multivariate normal information geometry to better understand the paths or distances commonly used in statistics (annealing, Wasserstein). Given the unavailability of a general solution, we also discuss the methods used for numerically obtaining geodesics in the space of multivariate normals, identifying remaining challenges and suggesting methodological improvements.
Comparative Study on the Effects of Noise in ML-Based Anxiety Detection

Wearable health devices are ushering in a new age of continuous and noninvasive remote monitoring. One application of this technology is in anxiety detection. Many advancements in anxiety detection have happened in controlled lab settings, but noise prevents these advancements from generalizing to real-world conditions. We seek to progress the field by studying how noise impacts model performance and developing models that are robust to noisy, real-world conditions and, hence, attuned to the commotion of everyday life. In this study we look to investigate why and how previous methods have failed. Using the wearable stress and affect detection (WESAD) dataset, we compare the effect of various intensities of noise on machine learning models classifying levels of physiological arousal in the three-class classification problem: baseline vs. stress vs. amusement. Before introducing noise, our baseline model performance reaches 98.7%, compared to Schmidt 2018's 80.3%. We discuss potential sources of this discrepancy in results through a careful evaluation of feature extraction and model architecture choices. Finally, after the introduction of noise, we provide a thorough analysis of the effect of noise on each model architecture.
Estimating WebRTC Video QoE Metrics Without Using Application Headers

The increased use of video conferencing applications (VCAs) has made it critical to understand and support end-user quality of experience (QoE) by all stakeholders in the VCA ecosystem, especially network operators, who typically do not have direct access to client software. Existing VCA QoE estimation methods use passive measurements of application-level Real-time Transport Protocol (RTP) headers. However, a network operator does not always have access to RTP headers in all cases, particularly when VCAs use custom RTP protocols (e.g., Zoom) or due to system constraints (e.g., legacy measurement systems). Given this challenge, this paper considers the use of more standard features in the network traffic, namely, IP and UDP headers, to provide per-second estimates of key VCA QoE metrics such as frames rate and video resolution. We develop a method that uses machine learning with a combination of flow statistics (e.g., throughput) and features derived based on the mechanisms used by the VCAs to fragment video frames into packets. We evaluate our method for three prevalent VCAs running over WebRTC: Google Meet, Microsoft Teams, and Cisco Webex. Our evaluation consists of 54,696 seconds of VCA data collected from both (1), controlled in-lab network conditions, and (2) real-world networks from 15 households. We show that the ML-based approach yields similar accuracy compared to the RTP-based methods, despite using only IP/UDP data. For instance, we can estimate FPS within 2 FPS for up to 83.05% of one-second intervals in the real-world data, which is only 1.76% lower than using the application-level RTP headers.
Study of the convergence of the Meshless Lattice Boltzmann Method in Taylor-Green and annular channel flows

The Meshless Lattice Boltzmann Method (MLBM) is a numerical tool that relieves the standard Lattice Boltzmann Method (LBM) from regular lattices and, at the same time, decouples space and velocity discretizations. In this study, we investigate the numerical convergence of MLBM in two benchmark tests: the Taylor-Green vortex and annular (bent) channel flow. We compare our MLBM results to LBM and to the analytical solution of the Navier-Stokes equation. We investigate the method's convergence in terms of the discretization parameter, the interpolation order, and the LBM streaming distance refinement. We observe that MLBM outperforms LBM in terms of the error value for the same number of nodes discretizing the domain. We find that LBM errors at a given streaming distance $\delta x$ and timestep length $\delta t$ are the asymptotic lower bounds of MLBM errors with the same streaming distance and timestep length. Finally, we suggest an expression for the MLBM error that consists of the LBM error and other terms related to the semi-Lagrangian nature of the discussed method itself.
Diurnal Climatology of the Boundary Layer in Southern California Using AMDAR Temperature and Wind Profiles

AbstractObservations from commercial aircraft [e.g., the Aircraft Meteorological Data Relay (AMDAR) automated weather reports] have been increasing dramatically. Two main applications of the aircraft data are use in short-term forecasts and assimilation into numerical weather prediction models. Now that more than 10 years of measurements exist, using this dataset to construct a description of the long-term climatological behavior (a “climatology”) of the lower atmosphere is explored with two main objectives. The first objective is to examine strengths and weaknesses of using the dataset to construct a climatology of the lower atmosphere. Unlike the traditional twice-daily radiosonde launches, the high frequency of observations at major airports allows for an unprecedented set of diurnal information at many locations globally. The second objective is to obtain a climatology of the lower atmosphere of Southern California, specifically at Los Angeles, San Diego, and Ontario, during the spring and summer when.
Influence Maximization with Fairness at Scale

In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes (e.g., gender, location, origin, race, etc.) are fairly affected, i.e., are proportionally similar between the original network and the affected users. Recent studies on this problem focused only on extremely small networks, hence the challenge remains on how to achieve a scalable solution, applicable to networks with millions or billions of nodes. We propose an approach that is based on learning node representations (embeddings) for fair spread from diffusion cascades, instead of the social connectivity, and in this way we can deal with very large graphs. We propose two data-driven approaches: (a) fairness-based participant sampling (FPS), and (b) fairness as context (FAC). Spread related user features, such as the probability of diffusing information to others, are derived from the historical information cascades, using a deep neural network. The extracted features are then used in selecting influencers that maximize the influence spread, while being also fair with respect to the chosen sensitive attributes. In FPS, fairness and cascade length information are considered independently in the decision-making process, while FAC considers these information facets jointly and takes into account correlations between them. The proposed algorithms are generic and represent the first policy-driven solutions that can be applied to arbitrary sets of sensitive attributes at scale. We evaluate the performance of our solutions on a real-world public dataset (Sina Weibo) and on a hybrid real-synthetic dataset (Digg), which exhibit all the facets that we exploit, namely diffusion network, diffusion traces, and user profiles. These experiments show that our methods outperform the state-the-art solutions in terms of spread, fairness, and scalability.