Zugversuche an Folienproben aus Typ 316 Edelstahl im Temperaturbereich von 20 bis 900 Grad C

Foil specimens in a thickness range of 20 - 500 $\mu$m are increas ingly used in simulation experiments where neutroninducedchanges of mechanical properties are simulated by light ion bombardment. For a reliable transfer of such data to bulk material, the mechanical behavior of foil specimens in comparison with bulk specimens must be known. In this work results of tensile tests on cold worked and solution annealed type 316 stainless steel foils of 200 $\mu$m thickness are reported for temperatures between 20 and 900° C. The investigated material was part of a heat supplied by JRC Ispra. lt shall serve as reference material for irradiation experimentswithin the Euratom Fusion Programme

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video

Automated gathering of real-world data from online patient forums can complement pharmacovigilance for rare cancers

Current methods of pharmacovigilance result in severe under-reporting of adverse drug events (ADEs). Patient forums have the potential to complement current pharmacovigilance practices by providing real-time uncensored and unsolicited information. We are the first to explore the value of patient forums for rare cancers. To this end, we conduct a case study on a patient forum for Gastrointestinal Stromal Tumor patients. We have developed machine learning algorithms to automatically extract and aggregate side effects from messages on open online discussion forums. We show that patient forum data can provide suggestions for which ADEs impact quality of life the most: For many side effects the relative reporting rate differs decidedly from that of the registration trials, including for example cognitive impairment and alopecia as side effects of avapritinib. We also show that our methods can provide real-world data for long-term ADEs, such as osteoporosis and tremors for imatinib, and novel ADEs not found in registration trials, such as dry eyes and muscle cramping for imatinib. We thus posit that automated pharmacovigilance from patient forums can provide real-world data for ADEs and should be employed as input for medical hypotheses for rare cancers.Algorithms and the Foundations of Software technolog

How do others cope?: Extracting coping strategies for adverse drug events from social media

Patients advise their peers on how to cope with their illness in daily life on online support groups. To date, no efforts have been made to automatically extract recommended coping strategies from online patient discussion groups. We introduce this new task, which poses a number of challenges including complex, long entities, a large long-tailed label space, and cross-document relations. We present an initial ontology for coping strategies as a starting point for future research on coping strategies, and the first end-to-end pipeline for extracting coping strategies for side effects. We also compared two possible computational solutions for this novel and highly challenging task; multi-label classification and named entity recognition (NER) with entity linking (EL). We evaluated our methods on the discussion forum from the Facebook group of the worldwide patient support organization 'GIST support international' (GSI); GIST support international donated the data to us. We found that coping strategy extraction is difficult and both methods attain limited performance (measured with F1 score) on held out test sets; multi-label classification outperforms NER+EL (F1=0.220 vs F1=0.155). An inspection of the multi-label classification output revealed that for some of the incorrect predictions, the reference label is close to the predicted label in the ontology (e.g. the predicted label 'juice' instead of the more specific reference label 'grapefruit juice'). Performance increased to F1=0.498 when we evaluated at a coarser level of the ontology. We conclude that our pipeline can be used in a semi-automatic setting, in interaction with domain experts to discover coping strategies for side effects from a patient forum. For example, we found that patients recommend ginger tea for nausea and magnesium and potassium supplements for cramps.This information can be used as input for patient surveys or clinical studies.Algorithms and the Foundations of Software technolog

HLA-matched platelet transfusions are effective only in refractory patients with positive HLA antibody screening

BACKGROUND Recipients of platelet transfusions with 1-hour corrected count increments (1hCCIs) of 7.5 or less on two subsequent platelet transfusions with random platelets may benefit from human leukocyte antigen (HLA)-matched platelet concentrates. We aimed to quantify the efficacy of HLA-matched platelets concentrates expressed in 1hCCIs. METHODS We performed a cohort study among consecutive refractory patients who received HLA-matched platelet concentrates in the Netherlands between 1994 and 2017. We performed mixed-model linear regression comparing 1hCCIs after HLA split-antigen-matched transfusions with 1hCCIs after HLA-mismatched transfusions, adjusted for within-patient correlations. A donor-to-patient match was categorized as a split-match if all donor HLA-A and -B antigens were present in the patient as well; that is, donor and patient were HLA identical or compatible. Subgroup analyses were performed for patients with positive or negative HLA antibody screens. Finally, the additional effect of ABO mismatches on 1hCCIs was investigated. RESULTS The 1hCCI after an HLA-matched transfusion was 14.09 (95% reference interval, 1.13-29.89). This was 1.94 (95% confidence interval [CI], 0.74-3.15) higher than 1hCCI after HLA-mismatched transfusions. In patients with negative HLA antibody screening tests, HLA matching did not affect 1hCCIs. Conditional on HLA matching, 1hCCIs decreased by 3.70 (95% CI, -5.22 to -2.18) with major ABO mismatches. CONCLUSION Matched platelet concentrates yielded maximal 1hCCIs, whereas mismatched transfusions still resulted in adequate increments. There is no indication for HLA-matched platelets in patients with negative antibody screens

Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity A Microbiome-Wide Analysis From Population Studies

IMPORTANCE Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear.OBJECTIVE To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors.DESIGN, SETTING, AND PARTICIPANTS This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study.EXPOSURES The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The alpha diversity (Shannon, richness, and Inverse Simpson indexes), beta diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition.MAIN OUTCOMES AND MEASURES Associations among alpha diversity, beta diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors. Data analysis was performed from January 1, 2018, to December 31, 2020.RESULTS There were 2166 participants in this study: 1418 from the Rotterdam Study (mean [SD] age, 62.4 [5.9] years; 815 [57.5%] male) and 748 from the LifeLines-DEEP study (mean [SD] age, 44.7 [13.4] years; 431 [57.6%] male); from this total, 193 type 2 diabetes cases were identified. Lower microbiome Shannon index and richness were associated with higher HOMA-IR (eg, Shannon index, -0.06; 95% CI, -0.10 to -0.02), and patients with type 2 diabetes had a lower richness than participants without diabetes (odds ratio [OR], 0.93; 95% CI, 0.88-0.99). The beta diversity (Bray-Curtis dissimilarity matrix) was associated with insulin resistance (R-2 = 0.004, P = .001 in the Rotterdam Study and R-2 = 0.005, P = .002 in the LifeLines-DEEP study). A total of 12 groups of bacteria were associated with HOMA-IR or type 2 diabetes. Specifically, a higher abundance of Christensenellaceae (beta = -0.08; 95% CI, -0.12 to -0.03: P &lt; .001), Christensenellaceae R7 group (beta = -0.07; 95% CI, -0.12 to -0.03; P &lt; .001), Marvinbryantia (beta = -0.07; 95% CI, -0.11 to -0.03; P &lt; .001), Ruminococcaceae UCG005 (beta = -0.09; 95% CI, -0.13 to -0.05; P &lt; .001), Ruminococcaceae UCG008 (beta = -0.07; 95% CI, -0.11 to -0.03; P &lt; .001), Ruminococcaceae UCG010 (beta = -0.08; 95% CI, -0.12 to -0.04; P &lt; .001), or Ruminococcaceae NK4A214 group (beta = -0.09; 95% CI, -0.13 to -0.05; P &lt; .001) was associated with lower HOMA-IR. A higher abundance of Clostridiaceae 1 (OR, 0.51; 95% CI, 0.41-0.65; P &lt; .001), Peptostreptococcaceae (OR, 0.56; 95% CI, 0.45-0.70; P &lt; .001), C sensu stricto 1 (OR, 0.51; 95% CI, 0.40-0.65; P &lt; .001), Intestinibacter (OR, 0.60; 95% CI, 0.48-0.76; P &lt; .001), or Romboutsia (OR, 0.55; 95% CI, 0.44-0.70; P &lt; .001) was associated with less type 2 diabetes. These bacteria are all known to produce butyrate.CONCLUSIONS AND RELEVANCE In this cross-sectional study, higher microbiome alpha diversity, along with more butyrate-producing gut bacteria, was associated with less type 2 diabetes and with lower insulin resistance among individuals without diabetes. These findings could help provide insight into the etiology, pathogenesis, and treatment of type 2 diabetes.</p

Ensuring HLA-matched platelet support requires an ethnic diverse donor population

BACKGROUND: Patients refractory for platelet transfusions benefit from human leukocyte antigen (HLA)-matched platelet transfusions. Differences in ethnic background of patients and donors could hamper the availability of sufficient numbers of HLA-matched donors for all patients. We evaluated our HLA-matched donor program and explored the role of ethnic background of patients related to the number of available donors. METHODS: We performed a cohort study among consecutive patients who received HLA-matched platelet concentrates in the Netherlands between 1994 and 2017. The number of available matched donors was determined per patient. Haplotypes were constructed from genotypes with computer software (PyPop). Based on haplotypes, HaploStats, an algorithm from the National Marrow Donor Program, was used to assess the most likely ethnic background for patients with 5 or fewer and 30 or more donors. RESULTS: HLA typing was available for 19,478 donors in September 2017. A total of 1206 patients received 12,350 HLA-matched transfusions. A median of 83 (interquartile range, 18-266) donors were available per patient. For 95 (10.3%) patients, 5 or fewer donors were available. These patients were more likely to have an African American background, whereas patients with 30 or more donors were more often from Caucasian origin, compared with Caucasian origin for patients with 30 donors. CONCLUSION: Adequate transfusion support could be guaranteed for most but not all refractory patients. More non-Caucasian donors are required to ensure the availability of HLA-matched donors for all patients in the Netherlands

Enabling analytics on sensitive medical data with secure multi-party computation

While there is a clear need to apply data analytics in the healthcare sector, this is often difficult because it requires combining sensitive data from multiple data sources. In this paper, we show how the cryptographic technique of secure multiparty computation can enable such data analytics by performing analytics without the need to share the underlying data. We discuss the issue of compliance to European privacy legislation; report on three pilots bringing these techniques closer to practice; and discuss the main challenges ahead to make fully privacy-preserving data analytics in the medical sector commonplace

International expert consensus on a scientific approach to training novice cardiac resynchronization therapy implanters using performance quality metrics

Aims: Pacing and Cardiac Resynchronization Therapy (CRT) procedural training for novice operators usually takes place in-vivo and methods vary across countries/institutions. No common system exists to objectively assess trainee ability to perform required tasks at predetermined performance levels prior to in-vivo practice. We sought to characterize and validate with experts a reference approach to pacing/CRT implants based on objective and explicit performance quality metrics, for the development of a reproducible, simulation-based, training curriculum aiming to operator proficiency. Methods: Three experienced CRT implanters, a behavioural scientist and two engineers performed a detailed task deconstruction of the pacing/CRT procedure and identified the performance metrics (phases, steps, errors, critical errors) that constitute an optimal CRT implant for training purposes. The metrics were stress tested to determine reliability and score-ability and then subjected to detailed systematic review by an international panel of 15 expert implanters in a modified Delphi process. Results: Thirteen procedure phases were identified, consisting of 196 steps, 122 errors, 50 critical errors. The expert panel deliberation added 16 metrics, deleted 12, and modified 43. Unanimous panel consensus on the resulting CRT procedure metrics was obtained, which verified face and content validity. Conclusion: A reference pacing/CRT procedure and metrics created by a core group of experts accurately characterize the essential components of performance and were endorsed by an international panel of experienced peers. The metrics will underpin quality-assured novice implanter training