Miracle-dz/UniFAR-FTUs huggingface dataset:
Density-based Community Detection in Geo-Social Networks

We propose a density-based model to detect communities of users in geo-social networks that are both socially and spatially cohesive. After formally defining the model and the geo-social distance measure it relies on, we present an algorithm that correctly identifies the underlying communities. We assess the effectiveness of our method using novel quantitative measures on the quality of the discovered communities. We also perform a visual evaluation of the discovered communities, using both real and synthetic datasets. Our results show that the proposed model produces geo-social communities with strong social and spatial cohesiveness, which can not be captured by existing graph or spatial clustering methods.
Trajectory-aware Load Adaption for Continuous Traffic Analytics

We introduce a framework for online monitoring of moving objects, which takes into account their evolving trajectories and copes smoothly with fluctuating demands of multiple continuous queries for limited system resources. This centralized scheme accepts streaming positional updates from numerous objects, but it only examines recent trajectory segments with expectedly higher utility in query evaluation, shedding the rest as immaterial. We focus on adaptive processing under extreme load conditions, opting to retain salient trajectory segments and possibly sacrifice smaller, frequently observed paths in favor of longer, distinctive routes. We propose heuristics for incremental, yet approximate, query evaluation in order to provide up-to-date traffic analytics using windows that abstract particular regions and time intervals of interest. Finally, we conduct a comprehensive experimental study to validate our approach, demonstrating its benefits in result accuracy and efficiency for almost real-time response to trajectory-based aggregates.
Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

Abstract Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.
Determinants of increased central excess pressure in dialysis: role of dialysis modality and arteriovenous fistula.

BACKGROUND Arterial reservoir-wave analysis (RWA) - a new model of arterial hemodynamics - separates arterial wave into reservoir pressure (RP) and excess pressure (XSP). The XSP integral (XSPI) has been associated with increased risk of clinical outcomes. The objectives of the present study were to examine the determinants of XSPI in a mixed cohort of hemodialysis (HD) and peritoneal dialysis (PD) patients, to examine whether dialysis modality, and presence of an arteriovenous fistula (AVF) are associated with increased XSPI. METHOD In a cross-sectional study, 290 subjects (232 HD, and 130 with AVF) underwent carotid artery tonometry (calibrated with brachial diastolic and mean blood pressure). The XSPI was calculated through RWA using pressure-only algorithms. Logistic regression was used for determinants of XSPI above median. Through forward conditional linear regression, we examined whether treatment by HD or presence of AVF is associated with higher XSPI. RESULTS Patients with XSPI> median were older, had a higher prevalence of diabetes and cardiovascular disease, had a higher body mass index and were more likely to be on HD. After adjustment for confounders, HD was associated with a higher risk of higher XSPI (OR=2.39, 95%CI:1.16-4.98). In a forward conditional linear regression analysis, HD was associated with higher XSPI (standardized coefficient: 0.126, P=0.012), but upon incorporation of AVF into the model, AVF was associated with higher XSPI (standardized coefficient: 0.130, P=0.008) and HD was excluded as a predictor. CONCLUSION This study suggests that higher XSPI in HD patients is related to the presence of AVF.
HBV‐Pol is crucial for HBV‐mediated inhibition of inflammasome activation and IL‐1β production

Hepatitis B virus (HBV) infection is the most critical factor underlying liver cirrhosis and hepatocellular carcinoma worldwide. IL‐1β and IL‐18, generated by activation of the inflammasome/caspase‐1 signaling pathway, play important roles in the control and clearance of HBV. However, the specific relationship between the inflammasome response and IFN‐α resistance or viral persistence is yet to be established.
Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Summary Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2018 in the field of Health Information Management (HIM) and Health Informatics. Methods: A systematic review of the literature was performed, with the help of a medical librarian, by the two editors of the HIM section of the International Medical Informatics Association (IMIA) Yearbook. In order to include papers that would address the special theme of the 2019 Yearbook on artificial intelligence (AI) as well as HIM, we searched bibliographic databases for HIM-related papers with an AI focus using both Medical Subject Headings (MeSH) descriptors and keywords in titles and abstracts. A shortlist of 15 candidate best papers was first selected by section editors before being peer-reviewed by independent external reviewers. Results: While there were a significant number of manuscripts that addressed issues relevant to HIM, there were virtually none with MeSH headings indicating an HIM focus. Manuscripts that were considered related to the HIM field in terms of the practice of health information management as well as the profession included those that examined using machine learning and other AI approaches to identify protected health information in clinical text to aid with de-identification, automated coding approaches to translate free-text into standardized codes, and natural language processing approaches to extract clinical data to assist with populating cancer and other registries. Conclusions: The papers discussed in the HIM section reflect the special theme of the use of AI in healthcare on issues particularly relevant to the field of HIM. This synopsis discusses these papers and recommends that HIM practitioners be more involved in research and that researchers in AI and related areas recognize the applicability and relevance of their work to the field of HIM.
Functional niche occupation and species richness in herbaceous plant communities along experimental gradients of stress and disturbance.

BACKGROUND AND AIMS The patterns of niche occupation in functional trait space have been widely studied to understand the processes of community assembly, but are rarely linked to environmental conditions (here, stress and disturbance). In this paper, we investigate (i) how the pattern of functional niche occupation, incorporating intraspecific trait variation and covariation, varies along experimental gradients of stress and disturbance, (ii) whether habitat filtering and/or limiting similarity modify the pattern, and (iii) whether their strength varies as a function of species richness or levels of stress and disturbance. METHODS We constructed an experimental system consisting of 24 herbaceous plant mesocosms under different levels of stress and disturbance, and measured ten traits on five individuals for each species in each mesocosm. We quantified the total functional niche volume occupied by an entire mesocosm, the functional niche overlap among species within a mesocosm and the average functional niche volume occupied per species, and investigated how these metrics varied from species-poor to species-rich mesocosms along gradients of stress and disturbance. KEY RESULTS Species richness and functional niche overlap positively correlated to disturbance at low and medium levels of stress, but peaked at the intermediate level of disturbance when stress was high. The total functional niche volume and average functional niche volume did not change significantly along these gradients. Compared to null models, each mesocosm occupied a smaller total functional niche volume (habitat filtering) and the species within each mesocosm are less overlapped and more functionally specialized (limiting similarity). Moreover, the standardized metrics (to the null expectations) did not change significantly under different levels of stress and disturbance. CONCLUSIONS This experimental evidence shows that both habitat filtering and limiting similarity determine the patterns of functional niche occupation and species richness, but their strength does not change along environmental gradients of stress and disturbance.
O7D.2 Health-related educational differences in duration of working life and loss of paid employment: working life expectancy in the netherlands

Objectives This study aims to provide insight into health-related educational differences in duration of working life by working life expectancy (WLE) and working years lost (WYL) through disability benefits and other non-employment states in the Netherlands. Methods Monthly information on employment status of the Dutch population (n=4,999,947) between 16 and 66 year from 2001 to 2015 was used to estimate working life courses. Across educational groups monthly transitions between paid employment and non-employment states were calculated with a Markov model with transitional probabilities. Using this multistate model (R-package mstate) the WLE and WYL due to disability benefits and other non-employment states were estimated, stratified by educational groups. Results Despite starting in paid employment much earlier, low educated men and low educated women had a 4.17 (men) and 9.50 (women) years lower WLE at age 16 than high educated men and women. Among low educated men 3.59 WYL were due to disability benefit compared to 0.78 WYL among high educated men, resulting in a WYL gap 2.81 years. Low educated women had 3.47 WYL due to disability benefit compared to 1.38 WYL among high educated women introducing a WYL gap of 2.09. Educational inequalities in premature death added to this WYL gap another 0.7 years among men and 0.3 years among women. Conclusions The working life course showed large educational differences. A considerable amount of the lost working time is health-related due to disability benefits and premature death. In comparison to high educated workers, those with a low educational level lose a substantial part of their working life due to disability benefit, unemployment, and no income. The metrics of WLE and WYL provide useful insights into the life-course perspective of working careers.
Contributions from the 2018 Literature on Bioinformatics and Translational Informatics

Summary Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.
Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Summary Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.
Sexually dimorphic neuronal inputs to the neuroendocrine dopaminergic system governing prolactin release

Prolactin (PRL) is a pleiotropic hormone that was identified in the context of maternal care and its release from the anterior pituitary is primarily controlled by neuroendocrine dopaminergic (NEDA) neurones of the arcuate nucleus of the hypothalamus. The sexually dimorphic nature of PRL physiology and associated behaviours is evident in mammals, even though the number and density of NEDA neurones is reported as not being sexually dimorphic in rats. However, the underlying circuits controlling NEDA neuronal activity and subsequent PRL release are largely uncharacterised. Thus, we mapped whole‐brain monosynaptic NEDA inputs in male and female mice. Accordingly, we employed a rabies virus based monosynaptic tracing system capable of retrogradely mapping inputs into genetically defined neuronal populations. To gain genetic access to NEDA neurones, we used the dopamine transporter promoter. Here, we unravel 59 brain regions that synapse onto NEDA neurones and reveal that male and female mice, despite monomorphic distribution of NEDA neurones in the arcuate nucleus of the hypothalamus, receive sexually dimorphic amount of inputs from the anterior hypothalamic nucleus, anteroventral periventricular nucleus, medial preoptic nucleus, paraventricular hypothalamic nucleus, posterior periventricular nucleus, supraoptic nucleus, suprachiasmatic nucleus, lateral supramammillary nucleus, tuberal nucleus and periaqueductal grey. Beyond highlighting the importance of considering sex as a biological variable when evaluating connectivity in the brain, these results illustrate a case where a neuronal population with similar anatomical distribution has a subjacent sexually dimorphic connectivity pattern, potentially capable of contributing to the sexually dimorphic nature of PRL release and function.
Cortical auditory evoked potentials in mild cognitive impairment: Evidence from a temporal-spatial principal component analysis.

Mild cognitive impairment (MCI) is considered an intermediate transitional stage for the development of dementia, especially Alzheimer's disease. The identification of neurophysiological biomarkers for MCI will allow improvement in detecting and tracking the progression of cognitive impairment. The primary objective of this study was to compare cortical auditory evoked potentials between older adults with and without probable MCI to identify potential neurophysiological indicators of cognitive impairment. We applied a temporal-spatial principal component analysis to the evoked potentials achieved during the processing of pure tones and speech sounds, to facilitate the separation of the components of the P1-N1-P2 complex. The probable MCI group showed a significant amplitude increase in a factor modeling N1b for speech sounds (Cohen's d = .84) and a decrease in a factor around the P2 time interval, especially for pure tones (Cohen's d = 1.17). Moreover, both factors showed a fair discrimination value between groups (area under the curve [AUC] = .698 for N1b in speech condition; AUC = .746 for P2 in tone condition), with high sensitivity to detect MCI cases (86% and 91%, respectively). The results for N1b suggest that MCI participants may suffer from a deficit to inhibit irrelevant speech information, and the decrease of P2 amplitude could be a signal of cholinergic hypoactivation. Therefore, both components could be proposed as early biomarkers of cognitive impairment.
Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

Summary Objectives: To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the “Ten threats to global health in 2019“ report published by the World Health Organization. Methods: We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme. Results: In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures. Conclusions: As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.
AI in Health: State of the Art, Challenges, and Future Directions

Summary Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.