Lizenzmodelle auf dem Weg zu Open Access - Gestaltungspotentiale und Umsetzung in der Praxis

Die Ergänzung traditioneller Lizenzmodelle um Open-Access-Komponenten sowie die Umstellung auf sofortige freie Verfügbarkeit wissenschaftlicher Inhalte findet derzeit auf verschiedenen Ebenen statt. Neben nationalen (DEAL-) Verhandlungen, dem sog. Journal Flipping oder auch der Gründung institutioneller Repositorien versuchen Bibliotheken zunehmend auch bei der bilateralen oder konsortialen Verlängerung von Lizenzen den Open-Access-Gedanken umzusetzen. Anhand von aktuellen Beispielen - SCOAP³, IOP und EDP - sollen die Gestaltungspotenziale beim Übergang zu Open-Access-Lizenzmodellen dargestellt werden. Das Ziel besteht dabei darin, die Umsetzbarkeit wissenschaftspolitisch geforderter Ziele im Hinblick auf Open Access zu analysieren und mögliche Hinderungsgründe zu diskutieren

Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes.

There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods

On the relation between promoter divergence and gene expression evolution

Recent studies have characterized significant differences in the cis-regulatory sequences of related organisms, but the impact of these differences on gene expression remains largely unexplored. Here, we show that most previously identified differences in transcription factor (TF)-binding sequences of yeasts and mammals have no detectable effect on gene expression, suggesting that compensatory mechanisms allow promoters to rapidly evolve while maintaining a stabilized expression pattern. To examine the impact of changes in cis-regulatory elements in a more controlled setting, we compared the genes induced during mating of three yeast species. This response is governed by a single TF (STE12), and variations in its predicted binding sites can indeed account for about half of the observed expression differences. The remaining unexplained differences are correlated with the increased divergence of the sequences that flank the binding sites and an apparent modulation of chromatin structure. Our analysis emphasizes the flexibility of promoter structure, and highlights the interplay between specific binding sites and general chromatin structure in the control of gene expression

Concerns about global phosphorus demand for lithium-iron-phosphate batteries in the light electric vehicle sector

A 'Matters Arising' article, arising from: Xu, C. et al. (2020). Future material demand for automotive lithium-based batteries. Communications Materials 1: 99

Eliminating viral hepatitis C in Belgium: the micro-elimination approach

Background: Hepatitis C virus is one of the leading causes of chronic liver disease and liver-related deaths worldwide. The estimated prevalence of chronic hepatitis C viral infection among the general Belgian population was 0.57% (n = 64,000) in 2015. Although Belgium has had a ‘Hepatitis C Plan’ since 2014, elimination efforts are unclear. This study employs the best available data and modelling estimates to define the burden of hepatitis C viral infection among key subgroups in Belgium, identify information gaps and propose potential approaches to screening, linkage to care and treatment, and cure. Methods: We examined the peer-reviewed and grey literature since 2012 for data on the prevalence of hepatitis C viral infection in Belgium in key subgroups identified by national experts and in the literature. Ultimately, this research is primarily based on data provided by the key stakeholders themselves due to a lack of reliable data in the literature. Based on this, we modelled the treatment rates required to reach elimination of hepatitis C in several subgroups. Results: Eleven potential subgroups were identified. There were no data available for two subgroups: generational cohorts and men who have sex with men. In six subgroups, fewer than 3000 people were reported or estimated to have hepatitis C infection. Migrants and people who inject drugs were the most affected subgroups, and children were the least affected subgroup. Only two subgroups are on target to achieve elimination by 2030: patients living with haemophilia and transplant recipients. Conclusions: Removing Belgian treatment reimbursement restrictions in January 2019 was a big step towards eliminating HCV. In addition, increasing surveillance, including with a national registry, treatment prescription by other health-care providers and availability of treatment in local pharmacies are central to improving the current situation and getting on track to reach the 2030 WHO hepatitis C elimination targets in Belgium

SWITCH : A randomised, sequential, open-label study to evaluate the efficacy and safety of Sorafenib-sunitinib versus Sunitinib-sorafenib in the treatment of metastatic renal cell cancer

Background Understanding how to sequence targeted therapies for metastatic renal cell carcinoma (mRCC) is important for maximisation of clinical benefit. Objectives To prospectively evaluate sequential use of the multikinase inhibitors sorafenib followed by sunitinib (So-Su) versus sunitinib followed by sorafenib (Su-So) in patients with mRCC. Design, setting, and participants The multicentre, randomised, open-label, phase 3 SWITCH study assessed So-Su versus Su-So in patients with mRCC without prior systemic therapy, and stratified by Memorial Sloan Kettering Cancer Center risk score (favourable or intermediate). Intervention Patients were randomised to sorafenib 400 mg twice daily followed, on progression or intolerable toxicity, by sunitinib 50 mg once daily (4 wk on, 2 wk off) (So-Su), or vice versa (Su-So). Outcome measurements and statistical analysis The primary endpoint was improvement in progression-free survival (PFS) with So-Su versus Su-So, assessed from randomisation to progression or death during second-line therapy. Secondary endpoints included overall survival (OS) and safety. Results and limitations In total, 365 patients were randomised (So-Su, n = 182; Su-So, n = 183). There was no significant difference in total PFS between So-Su and Su-So (median 12.5 vs 14.9 mo; hazard ratio [HR] 1.01; 90% confidence interval [CI] 0.81–1.27; p = 0.5 for superiority). OS was similar for So-Su and Su-So (median 31.5 and 30.2 mo; HR 1.00, 90% CI 0.77–1.30; p = 0.5 for superiority). More So-Su patients than Su-So patients reached protocol-defined second-line therapy (57% vs 42%). Overall, adverse event rates were generally similar between the treatment arms. The most frequent any-grade treatment-emergent first-line adverse events were diarrhoea (54%) and hand-foot skin reaction (39%) for sorafenib; and diarrhoea (40%) and fatigue (40%) for sunitinib. Conclusions Total PFS was not superior with So-Su versus Su-So. These results demonstrate that sorafenib followed by sunitinib and vice versa provide similar clinical benefit in mRCC

ManyDogs Project: A Big Team Science Approach to Investigating Canine Behavior and Cognition

Dogs have a special place in human history as the first domesticated species and play important roles in many cultures around the world. However, their role in scientific studies has been relatively recent. With a few notable exceptions (e.g., Darwin, Pavlov, Scott, and Fuller), domestic dogs were not commonly the subject of rigorous scientific investigation of behavior until the late 1990s. Although the number of canine science studies has increased dramatically over the last 20 years, most research groups are limited in the inferences they can draw because of the relatively small sample sizes used, along with the exceptional diversity observed in dogs (e.g., breed, geographic location, experience). To this end, we introduce the ManyDogs Project, an international consortium of researchers interested in taking a big team science approach to understanding canine behavioral science. We begin by discussing why studying dogs provides valuable insights into behavior and cognition, evolutionary processes, human health, and applications for animal welfare. We then highlight other big team science projects that have previously been conducted in canine science and emphasize the benefits of our approach. Finally, we introduce the ManyDogs Project and our mission: (a) replicating important findings, (b) investigating moderators that need a large sample size such as breed differences, (c) reaching methodological consensus, (d) investigating cross-cultural differences, and (e) setting a standard for replication studies in general. In doing so, we hope to address previous limitations in individual lab studies and previous big team science frameworks to deepen our understanding of canine behavior and cognition

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Enhanced transfer of organic matter to higher trophic levels caused by ocean acidification and its implications for export production : A mass balance approach

Ongoing acidification of the ocean through uptake of anthropogenic CO2 is known to affect marine biota and ecosystems with largely unknown consequences for marine food webs. Changes in food web structure have the potential to alter trophic transfer, partitioning, and biogeochemical cycling of elements in the ocean. Here we investigated the impact of realistic end-of-the-century CO2 concentrations on the development and partitioning of the carbon, nitrogen, phosphorus, and silica pools in a coastal pelagic ecosystem (Gullmar Fjord, Sweden). We covered the entire winter-to-summer plankton succession (100 days) in two sets of five pelagic mesocosms, with one set being CO2 enriched (similar to 760 mu atm pCO(2)) and the other one left at ambient CO2 concentrations. Elemental mass balances were calculated and we highlight important challenges and uncertainties we have faced in the closed mesocosm system. Our key observations under high CO2 were: (1) A significantly amplified transfer of carbon, nitrogen, and phosphorus from primary producers to higher trophic levels, during times of regenerated primary production. (2) A prolonged retention of all three elements in the pelagic food web that significantly reduced nitrogen and phosphorus sedimentation by about 11 and 9%, respectively. (3) A positive trend in carbon fixation (relative to nitrogen) that appeared in the particulate matter pool as well as the downward particle flux. This excess carbon counteracted a potential reduction in carbon sedimentation that could have been expected from patterns of nitrogen and phosphorus fluxes. Our findings highlight the potential for ocean acidification to alter partitioning and cycling of carbon and nutrients in the surface ocean but also show that impacts are temporarily variable and likely depending upon the structure of the plankton food web.Peer reviewe