Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in Chlamydia trachomatis IgG seroprevalence in the general population of the Netherlands over 20 years

Objectives To report sex and age-specific Chlamydia trachomatis (Ct) seroprevalence estimates in the general population of the Netherlands between 1996 and 2017 and identify risk factors associated with Ct seropositivity. Methods Participants (n=5158, aged 15–59 years) were included from three independent nationwide population-based serosurveillance studies in 1996, 2007 and 2017. Participants completed a questionnaire on demographics and sexual behaviour. Serum antibodies were analysed using Medac Ct IgG ELISA test. Census weights were assigned to achieve seroprevalence estimates representative of the general Dutch population. Weighted seroprevalence estimates were stratified by gender, age and birth cohort. Trends and risk factors in men and women were identified using multivariable logistic regression. Results Weighted overall Ct seroprevalence was 10.5% (95% CI: 9.2% to 12.0%) in women and 5.8% (95% CI: 4.7% to 7.0%) in men. Among women <25 years, there was a non-significant increase in seroprevalence from 5.9% (95% CI 3.7% to 9.2%) in 1996, to 7.6% (95% CI 5.1% to 11.1%) in 2007 and 8.8% (95% CI 5.5% to 13.9%) in 2017. Among women ≥25 years, the seroprevalence significantly decreased from 15.6% (95% CI: 12.2% to 19.7%) in 1996 to 9.5% (95% CI: 7.2% to 12.4%) in 2007 but did not further drop (11.2% (95% CI 8.1% to 15.3%) in 2017). In men, we did not observe trends between study rounds. In both men and women, having a non-Western migration background was a risk factor for seropositivity. In women, having had a prior sexually transmitted infection and ≥2 recent sex partners were risk factors for seropositivity as well. Conclusions We have not found evidence for a decrease in population seroprevalence in those under 25 years old despite decades of intensified testing-and-treatment efforts in the Netherlands. This suggests further monitoring of Ct burden in the general population is needed. If serum banks are used for this, specifically individuals <25 years old and with diverse migration backgrounds should be included.
A case for the importance of following antibiotic resistant bacteria throughout the soil food web

It is necessary to complement next‐generation sequencing data on the soil resistome with theoretical knowledge provided by ecological studies regarding the spread of antibiotic resistant bacteria (ARB) in the abiotic and, especially, biotic fraction of the soil ecosystem. Particularly, when ARB enter agricultural soils as a consequence of the application of animal manure as fertilizer, from a microbial ecology perspective, it is important to know their fate along the soil food web, that is, throughout that complex network of feeding interactions among members of the soil biota that has crucial effects on species richness and ecosystem productivity and stability. It is critical to study how the ARB that enter the soil through the application of manure can reach other taxonomical groups (e.g., fungi, protists, nematodes, arthropods, earthworms), paying special attention to their presence in the gut microbiomes of mesofauna‐macrofauna and to the possibilities for horizontal gene transfer of antibiotic resistant genes.
Fundament for a methodological standard to process hip accelerometer data to a measure of physical activity intensity in middle-aged individuals.

BACKGROUND There is a lack of a methodological standard to process accelerometer data to measures of physical activity, which impairs data quality and comparability. This study investigated the effect of different combinations of settings of multiple processing components, on the measure of physical activity and the association with measures of cardiometabolic health in an unselected population of middle-aged individuals. METHODS Free-living hip accelerometer data, aerobic fitness, body mass index, HDL:total cholesterol ratio, blood glucose, and systolic blood pressure were achieved from 4391 participants 50-64 years old included in The Swedish CArdioPulmonary bioImage Study (SCAPIS) baseline measurement (cross-sectional). Lab data were also included for calibration of accelerometers to provide comparable measure of physical activity intensity and time spent in different intensity categories, as well as to enhance understanding. The accelerometer data processing components were hardware recalibration, frequency filtering, number of accelerometer axes, epoch length, wear time criterium, time composition (min/24 h vs. % of wear time). Partial least regression and ordinary least regression were used for the association analyses. RESULTS The setting of frequency filter had the strongest effect on the physical activity intensity measure and time distribution in different intensity categories followed by epoch length and number of accelerometer axes. Wear time criterium and recalibration of accelerometer data were less important. The setting of frequency filter and epoch length also showed consistent important effect on the associations with the different measures of cardiometabolic health, while the effect of recalibration, number of accelerometer axes, wear time criterium and expression of time composition was less consistent and less important. There was a large range in explained variance of the measures of cardiometabolic health depending on the combination of processing settings, for example, 12.1%-20.8% for aerobic fitness and 5.8%-14.0% for body mass index. CONCLUSIONS There was a large variation in the physical activity intensity measure and the association with different measures of cardiometabolic health depending on the combination of settings of accelerometer data processing components. The results provide a fundament for a standard to process hip accelerometer data to assess the physical activity in middle-aged populations.
The effects of a music intervention on the autonomic nervous system during recovery from strenuous exercise

Abstract Objective To investigate the effect of music on heart rate recovery (HRR) and heart rate variability (HRV) after intense exertion. Methods Five hundred male students enrolled at Yongin University, Korea, underwent a cycling test to assess aerobic capacity; 180 students with equal scores were selected for a music intervention, which was conducted after vigorous exercise. The 180 participants were randomized into three music groups and a control group; the participants in each music group listened to music at three different tempos: slow (lento) (n = 45), moderate (moderato) (n = 45), and fast (allegretto) (n = 45). The control group did not listen to music (n = 45). After the test, data on cardiac recovery and HRV were gathered and modeled. Results The results revealed no significant variation in HRR and HRV indexes between the four cohorts (p > .05), and no significant differences were observed in the anaerobic power cycling indexes during strenuous exercise (p > .05). The music intervention had a significant impact on HR, low‐frequency power (LF), high‐frequency power (HF), normalized LF (LFnorm), normalized HF (HFnorm), and the LF/HF ratio during recovery (p < .05). Conclusion After rigorous activity, listening to allegretto music improved HRR and restored HRV equilibrium, which is critical to preventing and minimizing arrhythmias and sudden cardiac death.
Endohepatology: The endoscopic armamentarium in the hand of the hepatologist

Abstract Recent advances in the field of hepatology include new and effective treatments for viral hepatitis. Further effort is now being directed to other disease entities, such as non-alcoholic fatty liver disease, with an increased need for assessment of liver function and histology. In fact, with the evolving nomenclature of fat-associated liver disease and the emergence of the term “metabolic-associated fatty liver disease” (MAFLD), new diagnostic challenges have emerged as patients with histologic absence of steatosis can still be classified under the umbrella of MAFLD. Currently, there is a growing number of endoscopic procedures that are pertinent to patients with liver disease. Indeed, interventional radiologists mostly perform interventional procedures such as percutaneous and intravascular procedures, whereas endoscopists focus on screening for and treatment of esophageal and gastric varices. EUS has proven to be of value in many areas within the realm of hepatology, including liver biopsy, assessment of liver fibrosis, measurement of portal pressure, managing variceal bleeding, and EUS-guided paracentesis. In this review article, we will address the endoscopic applications that are used to manage patients with chronic liver disease.
CRISPR‐Cas9 technology: As an efficient genome modification tool in the cancer diagnosis and treatment

Cancer is the second most common cause of death globally and is a major public health concern. Managing this disease is difficult due to its multiple stages and numerous genetic and epigenetic changes. Traditional cancer diagnosis and treatment methods have limitations, making it crucial to develop new modalities to combat the increasing burden of cancer. The clustered regularly interspaced short palindromic repeats (CRISPR)‐CRISPR‐associated protein 9 (Cas9) system has transformed genetic engineering due to its simplicity, specificity, low cytotoxicity, and cost‐effectiveness. It has been proposed as an effective technology to enhance cancer diagnosis and treatment strategies. This article presents the most recent discoveries regarding the structure, mechanism, and delivery methods of the highly powerful genome editing tool, CRISPR‐Cas9. In terms of diagnosis, the article examines the role of CRISPR‐Cas9 in detecting microRNAs and DNA methylation, and discusses two popular gene detection techniques that utilize the CRISPR‐Cas system: DNA endonuclease‐targeted CRISPR trans reporter and specific high sensitivity enzymatic reporter unlocking. Regarding treatment, the article explores several genes that have been identified and modified by CRISPR‐Cas9 for effective tumorigenesis of common cancers such as breast, lung, and colorectal cancer. The present review also addresses the challenges and ethical issues associated with using CRISPR‐Cas9 as a diagnostic and therapeutic tool. Despite some limitations, CRISPR‐Cas9‐based cancer diagnosis has the potential to become the next generation of cancer diagnostic tools, and the continuous progress of CRISPR‐Cas9 can greatly aid in cancer treatment.
How Homophily Aects Learning and Diusion in Networks

We examine how three dierent communication processes operating through social networks are aected by homophily { the tendency of individuals to associate with others similar to themselves. Homophily has no eect if messages are broadcast or sent via shortest paths; only connection density matters. In contrast, homophily substantially slows learning based on repeated averaging of neighbors’ information and Markovian diusion processes such as the Google random surfer model. Indeed, the latter processes are strongly aected by homophily but completely independent of connection density, provided this density exceeds a low threshold. We obtain these results by establishing new results on the spectra of large random graphs and relating the spectra to homophily. We conclude by checking the theoretical predictions using observed high school friendship networks from the Adolescent Health dataset.
Conditional Modeling Based Automatic Video Summarization

The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story. Video summarization methods mainly rely on visual factors, such as visual consecutiveness and diversity, which may not be sufficient to fully understand the content of the video. There are other non-visual factors, such as interestingness, representativeness, and storyline consistency that should also be considered for generating high-quality video summaries. Current methods do not adequately take into account these non-visual factors, resulting in suboptimal performance. In this work, a new approach to video summarization is proposed based on insights gained from how humans create ground truth video summaries. The method utilizes a conditional modeling perspective and introduces multiple meaningful random variables and joint distributions to characterize the key components of video summarization. Helper distributions are employed to improve the training of the model. A conditional attention module is designed to mitigate potential performance degradation in the presence of multi-modal input. The proposed video summarization method incorporates the above innovative design choices that aim to narrow the gap between human-generated and machine-generated video summaries. Extensive experiments show that the proposed approach outperforms existing methods and achieves state-of-the-art performance on commonly used video summarization datasets.
Sensitivity analysis with multiple treatments and multiple outcomes with applications to air pollution mixtures

Understanding the health impacts of air pollution is vital in public health research. Numerous studies have estimated negative health effects of a variety of pollutants, but accurately gauging these impacts remains challenging due to the potential for unmeasured confounding bias that is ubiquitous in observational studies. In this study, we develop a framework for sensitivity analysis in settings with both multiple treatments and multiple outcomes simultaneously. This setting is of particular interest because one can identify the strength of association between the unmeasured confounders and both the treatment and outcome, under a factor confounding assumption. This provides informative bounds on the causal effect leading to partial identification regions for the effects of multivariate treatments that account for the maximum possible bias from unmeasured confounding. We also show that when negative controls are available, we are able to refine the partial identification regions substantially, and in certain cases, even identify the causal effect in the presence of unmeasured confounding. We derive partial identification regions for general estimands in this setting, and develop a novel computational approach to finding these regions.
The MEGARA view of outflows in LINERs

Feedback processes, in particular those driven by outflows, are believed to play a major role in galaxy evolution. Outflows are believed to be ubiquitous in all active galactic nuclei (AGNs), although their presence in low luminosity AGNs, in particular for Low-Ionisation Nuclear Emission line Regions (LINERs), has only started to be explored. Their properties (geometry, mass, and energetics) are still far from being properly characterised. The main goal is to use integral field spectroscopic data from the MEGARA instrument at the Gran Telescopio Canarias (GTC) to analyse a small sample of nine LINERs, candidates of hosting ionised gas outflows. We aim to study the main emission lines in the optical wavelength range to identify their properties and physical origin. We obtained data cubes in several bands at the lowest (Rsim 6000) and highest (Rsim 20000) spectral resolution of MEGARA. We modelled and subtracted the stellar continuum to obtain the ionised gas contribution, and then fitted the emission lines to extract their kinematics (velocity and velocity dispersion). We identified outflows as a secondary component in the emission lines and obtained their main properties. The primary component of the emission lines was typically associated with gas in the galactic disc. For some objects, there is an enhanced-sigma region typically co-spatial with the secondary component. We associated it to turbulent gas produced due to the interaction with the outflows. We find signatures of outflows in six LINERs, with mass outflow rates ranging from 0.004 to 0.4 $M_ sun $ and energy rates from sim \,1038 to sim $, extending to distances of sim \,400\,pc at an (absolute) velocity of sim $ (on average). They tend to be compact and unresolved, although for some sources they are extended with a bubble-like morphology. Our results confirm the existence of outflows in the best LINER candidates identified using previous long-slit spectroscopic and imaging data. These outflows do not follow the scaling relations obtained for more luminous AGNs. For some objects we discuss jets as the main drivers of the outflows.
Bridging Learnersourcing and AI: Exploring the Dynamics of Student-AI Collaborative Feedback Generation

This paper explores the space of optimizing feedback mechanisms in complex domains, such as data science, by combining two prevailing approaches: Artificial Intelligence (AI) and learnersourcing. Towards addressing the challenges posed by each approach, this work compares traditional learnersourcing with an AI-supported approach. We report on the results of a randomized controlled experiment conducted with 72 Master's level students in a data visualization course, comparing two conditions: students writing hints independently versus revising hints generated by GPT-4. The study aimed to evaluate the quality of learnersourced hints, examine the impact of student performance on hint quality, gauge learner preference for writing hints with or without AI support, and explore the potential of the student-AI collaborative exercise in fostering critical thinking about LLMs. Based on our findings, we provide insights for designing learnersourcing activities leveraging AI support and optimizing students' learning as they interact with LLMs.
Optimal Transport Divergences Induced by Scoring Functions

We employ scoring functions, used in statistics for eliciting risk functionals, as cost functions in the Monge-Kantorovich (MK) optimal transport problem. This gives raise to a rich variety of novel asymmetric MK divergences, which subsume the family of Bregman-Wasserstein divergences. We show that for distributions on the real line, the comonotonic coupling is optimal for the majority the new divergences. Specifically, we derive the optimal coupling of the MK divergences induced by functionals including the mean, generalised quantiles, expectiles, and shortfall measures. Furthermore, we show that while any elicitable law-invariant convex risk measure gives raise to infinitely many MK divergences, the comonotonic coupling is simultaneously optimal. The novel MK divergences, which can be efficiently calculated, open an array of applications in robust stochastic optimisation. We derive sharp bounds on distortion risk measures under a Bregman-Wasserstein divergence constraint, and solve for cost-efficient portfolio strategies under benchmark constraints.
Is mathematics a game?

We re-examine the old question to what extent mathematics may be compared to a game. Under the spell of Wittgenstein, we propose that the more refined object of comparison is a"motley of language games", the nature of which was (implicitly) clarified by Hilbert: via different language games, axiomatization lies at the basis of both the rigour and the applicability of mathematics. In the"formalist"game, mathematics resembles chess via a clear conceptual dictionary. Accepting this resemblance: like positions in chess, mathematical sentences cannot be true or false; true statements in mathematics are about sentences, namely that they are theorems (if they are). In principle, the certainty of mathematics resides in proofs, but to this end, in practice these must be"surveyable". Hilbert and Wittgenstein proposed almost oppositie criteria for surveyability; we try to overcome their difference by invoking computer-verified proofs. The"applied"' language game is based on Hilbert's axiomatization program for physics (and other scientific disciplines), refined by Wittgenstein's idea that theorems are yardsticks to which empirical phenomena may be compared, and further improved by invoking elements of van Fraassen's constructive empiricism. From this perspective, in an appendix we also briefly review the varying roles and structures of axioms, definitions, and proofs in mathematics. Our view is not meant as a philosophy of mathematics by itself, but as a coat rack analogous to category theory, onto which various (traditional and new) philosophies of mathematics (such as formalism, intuitionism, structuralism, deductivism, and the philosophy of mathematical practice) may be attached and may even peacefully support each other.
Biomechanical Analysis Of Breast Cancer Cells: A Comparative Study Of Invasive And Non-Invasive Cell Lines Using Digital Holographic Microscopy

This study investigates the biomechanical properties of breast cancer cells, focusing on the invasive MDA-MB-231 and non-invasive MCF-7 cell lines, using phase-shifting digital holographic microscopy and an acousto-holographic system for non-invasive strain measurement. Findings reveal higher strain values in MDA-MB-231 cells, indicative of their aggressive, metastatic nature, compared to lower strain values in MCF-7 cells. This variance in mechanical properties, potentially linked to distinct metabolic states and responses to external stimuli, underscores the role of cellular mechanics in cancer progression. The study advances understanding of breast cancer cell mechanics, highlighting biomechanical analysis as a crucial tool in cancer research and potential therapeutic interventions.
Regression-Based Analysis of Multimodal Single-Cell Data Integration Strategies

Multimodal single-cell technologies enable the simultaneous collection of diverse data types from individual cells, enhancing our understanding of cellular states. However, the integration of these datatypes and modeling the interrelationships between modalities presents substantial computational and analytical challenges in disease biomarker detection and drug discovery. Established practices rely on isolated methodologies to investigate individual molecular aspects separately, often resulting in inaccurate analyses. To address these obstacles, distinct Machine Learning Techniques are leveraged, each of its own kind to model the co-variation of DNA to RNA, and finally to surface proteins in single cells during hematopoietic stem cell development, which simplifies understanding of underlying cellular mechanisms and immune responses. Experiments conducted on a curated subset of a 300,000-cell time course dataset, highlights the exceptional performance of Echo State Networks, boasting a remarkable state-of-the-art correlation score of 0.94 and 0.895 on Multi-omic and CiteSeq datasets. Beyond the confines of this study, these findings hold promise for advancing comprehension of cellular differentiation and function, leveraging the potential of Machine Learning.
Contribution of Food GHG Emissions Reduction: Moving UBC Beyond Climate Neutral

3 INTRODUCTION.3 PROBLEM DEFINITION.4 VISION STATEMENT.6 IDENTIFICATION OF VALUE ASSUMPTIONS.7 METHODOLOGY.7 FINDINGS & DISCUSSIONS.8 CLIMATE CHANGE IN THE CONTEXT OF THE NATIONAL AND GLOBAL FOOD SYSTEMS.8 LINKAGE: CLIMATE CHANGE AND THE UBC FOOD SYSTEM.10 LINKAGE: WHAT OTHER INSTITUTIONS ARE DOING.11 REVISING THE TARGET.13 COMPOST STRATEGY: BEST PRACTICES.14 COMPOST STRATEGY: BUILDING DESIGN AND LAYOUT.16 ANALYSIS: FEASIBILITY.17 ANALYSIS: APPROPRIATENESS.19 ANALYSIS: COMMUNITY SUPPORT. 20 ANALYSIS: BARRIERS.21 RECOMMENDATIONS.22 CONCLUSION.23 REFERENCES. 25 APPENDICES. 28