653 research outputs found

    Beyond Data Markets: Opportunities and Challenges for Distributed Ledger Technology in Genomics

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    During the past decade, distributed ledger technology (DLT) has found its way into application areas outside finance, such as supply chain management, the Internet of Things, or health care. To this end, this novel technology phenomenon has recently also caught the attention of researchers and practitioners in genomics. Although various DLT-based data markets for genome data already exist or are in development, the potential of DLT in this context is far from exhausted, whereas the possible risks related to the application of DLT in genomics are not yet sufficiently known. In this work, we investigate the potential opportunities and challenges for the application of DLT in the field of genomics. Thus, we make an important contribution to the safe and socially acceptable use of DLT in this unique and highly relevant use context

    The Potential of AutoML for Demand Forecasting

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    In demand forecasting, which can depend on various internal and external factors, machine learning (ML) methods can capture complex patterns and enable precise forecasts. Accurate forecasts facilitate targeted, demand-oriented planning and control of production and underline the importance of this task. The implementation of ML-algorithms requires knowledge of the specific domain as well as knowledge of data science and involves an elaborate set up process. This often makes the application of ML to potential industrial problems economically unattractive. The major skills shortage in the field of data science further exacerbates this. Automation and better accessibility of ML methods is therefore a key prerequisite for widespread use. This is where the principle of automated ML (AutoML) comes in, automating large parts of a ML pipeline and thus leading to a reduction in human labour input. Therefore, the aim of the publication is to investigate the extent to which AutoML solutions can generate added value for demand planning in the context of production planning and control. For this purpose, publicly available datasets deriving from Walmart as well as an anonymised manufacturing company are used for short-term and long-term forecasting. The AutoML tools from Microsoft, Dataiku and Google conduct these forecasts. Statistical models serve as benchmarks. The results show that the forecasting quality varies depending on the software, the input data and their demand patterns. Overall, the prepared models from Microsoft show the most accurate results in average and the potential of AutoML becomes particularly clear in the short-term forecast. This paper enriches the research field through its broad application, giving valuable insights into the use of AutoML tools for demand planning. The resulting understanding of limitations and benefits of AutoML tools for the case studies presented fosters their suitable application in practice

    Decrease of core 2 O-glycans on synovial lubricin in osteoarthritis reduces galectin-3 mediated crosslinking

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    The synovial fluid glycoprotein lubricin (also known as proteoglycan 4) is a mucin-type O-linked glycosylated biological lubricant implicated to be involved in osteoarthritis (OA) development. Lubricin\u27s ability to reduce friction is related to its glycosylation consisting of sialylated and unsialylated Tn-antigens and core 1 and core 2 structures. The glycans on lubricin have also been suggested to be involved in crosslinking and stabilization of the lubricating superficial layer of cartilage by mediating interaction between lubricin and galectin-3. However, with the spectrum of glycans being found on lubricin, the glycan candidates involved in this interaction were unknown. Here, we confirm that the core 2 O-linked glycans mediate this lubricin-galectin-3 interaction, shown by surface plasmon resonance data indicating that recombinant lubricin (rhPRG4) devoid of core 2 structures did not bind to recombinant galectin-3. Conversely, transfection of Chinese hamster ovary cells with the core 2 GlcNAc transferase acting on a mucin-type O-glycoprotein displayed increased galectin-3 binding. Both the level of galectin-3 and the galectin-3 interactions with synovial lubricin were found to be decreased in late-stage OA patients, coinciding with an increase in unsialylated core 1 O-glycans (T-antigens) and Tn-antigens. These data suggest a defect in crosslinking of surface-active molecules in OA and provide novel insights into OA molecular pathology

    Relations between lipoprotein(a) concentrations, LPA genetic variants, and the risk of mortality in patients with established coronary heart disease: a molecular and genetic association study

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    Background: Lipoprotein(a) concentrations in plasma are associated with cardiovascular risk in the general population. Whether lipoprotein(a) concentrations or LPA genetic variants predict long-term mortality in patients with established coronary heart disease remains less clear. Methods: We obtained data from 3313 patients with established coronary heart disease in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study. We tested associations of tertiles of lipoprotein(a) concentration in plasma and two LPA single-nucleotide polymorphisms ([SNPs] rs10455872 and rs3798220) with all-cause mortality and cardiovascular mortality by Cox regression analysis and with severity of disease by generalised linear modelling, with and without adjustment for age, sex, diabetes diagnosis, systolic blood pressure, BMI, smoking status, estimated glomerular filtration rate, LDL-cholesterol concentration, and use of lipid-lowering therapy. Results for plasma lipoprotein(a) concentrations were validated in five independent studies involving 10 195 patients with established coronary heart disease. Results for genetic associations were replicated through large-scale collaborative analysis in the GENIUS-CHD consortium, comprising 106 353 patients with established coronary heart disease and 19 332 deaths in 22 studies or cohorts. Findings: The median follow-up was 9·9 years. Increased severity of coronary heart disease was associated with lipoprotein(a) concentrations in plasma in the highest tertile (adjusted hazard radio [HR] 1·44, 95% CI 1·14–1·83) and the presence of either LPA SNP (1·88, 1·40–2·53). No associations were found in LURIC with all-cause mortality (highest tertile of lipoprotein(a) concentration in plasma 0·95, 0·81–1·11 and either LPA SNP 1·10, 0·92–1·31) or cardiovascular mortality (0·99, 0·81–1·2 and 1·13, 0·90–1·40, respectively) or in the validation studies. Interpretation: In patients with prevalent coronary heart disease, lipoprotein(a) concentrations and genetic variants showed no associations with mortality. We conclude that these variables are not useful risk factors to measure to predict progression to death after coronary heart disease is established. Funding: Seventh Framework Programme for Research and Technical Development (AtheroRemo and RiskyCAD), INTERREG IV Oberrhein Programme, Deutsche Nierenstiftung, Else-Kroener Fresenius Foundation, Deutsche Stiftung für Herzforschung, Deutsche Forschungsgemeinschaft, Saarland University, German Federal Ministry of Education and Research, Willy Robert Pitzer Foundation, and Waldburg-Zeil Clinics Isny

    Association of Factor V Leiden with Subsequent Atherothrombotic Events:A GENIUS-CHD Study of Individual Participant Data

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    BACKGROUND: Studies examining the role of factor V Leiden among patients at higher risk of atherothrombotic events, such as those with established coronary heart disease (CHD), are lacking. Given that coagulation is involved in the thrombus formation stage on atherosclerotic plaque rupture, we hypothesized that factor V Leiden may be a stronger risk factor for atherothrombotic events in patients with established CHD. METHODS: We performed an individual-level meta-analysis including 25 prospective studies (18 cohorts, 3 case-cohorts, 4 randomized trials) from the GENIUS-CHD (Genetics of Subsequent Coronary Heart Disease) consortium involving patients with established CHD at baseline. Participating studies genotyped factor V Leiden status and shared risk estimates for the outcomes of interest using a centrally developed statistical code with harmonized definitions across studies. Cox proportional hazards regression models were used to obtain age- and sex-adjusted estimates. The obtained estimates were pooled using fixed-effect meta-analysis. The primary outcome was composite of myocardial infarction and CHD death. Secondary outcomes included any stroke, ischemic stroke, coronary revascularization, cardiovascular mortality, and all-cause mortality. RESULTS: The studies included 69 681 individuals of whom 3190 (4.6%) were either heterozygous or homozygous (n=47) carriers of factor V Leiden. Median follow-up per study ranged from 1.0 to 10.6 years. A total of 20 studies with 61 147 participants and 6849 events contributed to analyses of the primary outcome. Factor V Leiden was not associated with the combined outcome of myocardial infarction and CHD death (hazard ratio, 1.03 [95% CI, 0.92-1.16]; I2=28%; P-heterogeneity=0.12). Subgroup analysis according to baseline characteristics or strata of traditional cardiovascular risk factors did not show relevant differences. Similarly, risk estimates for the secondary outcomes including stroke, coronary revascularization, cardiovascular mortality, and all-cause mortality were also close to identity. CONCLUSIONS: Factor V Leiden was not associated with increased risk of subsequent atherothrombotic events and mortality in high-risk participants with established and treated CHD. Routine assessment of factor V Leiden status is unlikely to improve atherothrombotic events risk stratification in this population
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