Distribution and habitat partitioning of cetaceans (Mammalia: Cetartiodactyla) in the Bohol Sea, Philippines.

Understanding broad-scale species distribution and finer-scale ecological interactions is essential for conservation. We assessed species richness, distribution, habitat use and interspecific associations of cetacean in the Bohol Sea, Philippines. During 72 days of dedicated survey (2010 - 2013), we encountered 12 species of cetacean in 291 sightings, 16.8% of which involved mixed species. We used maximum entropy (MaxEnt) models to assess species’ habitat suitability and found slope and distance from the coast to be influential contributors to cetacean distribution. To explore habitat use, through foraging ecology and niche segregation of sympatric species, we compared behavioral budgets across species and found significant differences (chi-sq = 21.44; p-value = 0.044). We then used GLMs to determine the foraging likelihood in relation to oceanographic features, group size and presence of associated species. Results from model selection complimented those derived from MaxEnt. However, some inter-specific exclusion behavior might also occur. Overall, our study suggests that the Bohol Sea supports a high cetacean biodiversity while more complex inter-specific dynamics might further shape species’ ecological niches. These results highlight the importance of multi-species ecology and can be used to develop management actions

Complex temporal climate signals drive the emergence of human water-borne disease

Predominantly occurring in developing parts of the world, Buruli ulcer is a severely disabling mycobacterium infection which often leads to extensive necrosis of the skin. While the exact route of transmission remains uncertain, like many tropical diseases, associations with climate have been previously observed and could help identify the causative agent's ecological niche. In this paper, links between changes in rainfall and outbreaks of Buruli ulcer in French Guiana, an ultraperipheral European territory in the northeast of South America, were identified using a combination of statistical tests based on singular spectrum analysis, empirical mode decomposition and cross-wavelet coherence analysis. From this, it was possible to postulate for the first time that outbreaks of Buruli ulcer can be triggered by combinations of rainfall patterns occurring on a long (i.e., several years) and short (i.e., seasonal) temporal scale, in addition to stochastic events driven by the El Nino-Southern Oscillation that may disrupt or interact with these patterns. Long-term forecasting of rainfall trends further suggests the possibility of an upcoming outbreak of Buruli ulcer in French Guiana

How Research Data Management Plans Can Help in Harmonizing Open Science and Approaches in the Digital Economy

Within this perspective article, we intend to summarise definitions and terms that are often used in the context of open science and data-driven R&D and we discuss upcoming European regulations concerning data, data sharing and handling. With this background in hand, we take a closer look at the potential connections and permeable interfaces of open science and digital economy, in which data and resulting immaterial goods can become vital pieces as tradeable items. We believe that both science and the digital economy can profit from a seamless transition and foresee that the scientific outcomes of publicly funded research can be better exploited. To close the gap between open science and the digital economy, and to serve for a balancing of the interests of data producers, data consumers, and an economy around services and the public, we introduce the concept of generic research data management plans (RDMs), which have in part been developed through a community effort and which have been evaluated by academic and industry members of the NFDI4Cat consortium. We are of the opinion that in data-driven research, RDMs do need to become a vital element in publicly funded projects

Generating knowledge graphs through text mining of catalysis research related literature

Structured research data management in catalysis is crucial, especially for large amounts of data, and should be guided by FAIR principles for easy access and compatibility of data. Ontologies help to organize knowledge in a structured and FAIR way. The increasing numbers of scientific publications call for automated methods to preselect and access the desired knowledge while minimizing the effort to search for relevant publications. While ontology learning can be used to create structured knowledge graphs, named entity recognition allows detection and categorization of important information in text. This work combines ontology learning and named entity recognition for automated extraction of key data from publications and organization of the implicit knowledge in a machine- and user-readable knowledge graph and data. CatalysisIE is a pre-trained model for such information extraction for catalysis research. This model is used and extended in this work based on a new data set, increasing the precision and recall of the model with regard to the data set. Validation of the presented workflow is presented on two datasets regarding catalysis research. Preformulated SPARQL-queries are provided to show the usability and applicability of the resulting knowledge graph for researchers

From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts

The hydroformylation of alkenes is a cornerstone transformation for the chemical industry, central for both functionalizing and extending the carbon backbone of an alkene. In this study, silica-supported crystalline rhodium sulfide nanoparticles are explored as heterogeneous catalysts in hydroformylation reactions, and it is found that RhxSy systems (x = 17, y = 15 or x = 2, y = 3 with 1 wt% Rh on SiO2) greatly outperform metallic Rh nanoparticles. These systems prove to be exceptionally competitive when benchmarked against other cutting-edge catalysts in terms of activity, with Rh17S15/SiO2 being the superior catalyst candidate. By employing local environment descriptors, unsupervised machine learning and density functional theory, the structure-performance relationships are examined. The results highlight that the presence of S in close proximity to the catalytic site unlocks the tunability of the surface catalytic properties. This allows for the substrate affinity to be modulated, in particular for Rh17S15, with adsorption energies rivalling those of pristine Rh and improved spatial resolution

A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts

Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high-quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level. © 2021 The Authors. ChemCatChem published by Wiley-VCH Gmb

A Unified Research Data Infrastructure for Catalysis Research – Challenges and Concepts

Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high‐quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level.DFG, 441926934, NFDI4Cat – NFDI für Wissenschaften mit Bezug zur Katalys

A randomized placebo-controlled trial of elafibranor in patients with primary biliary cholangitis and incomplete response to UDCA

\ua9 2021 European Association for the Study of the Liver. Background & Aims: Patients with primary biliary cholangitis (PBC) who have an incomplete response to ursodeoxycholic acid remain at risk of disease progression. We investigated the safety and efficacy of elafibranor, a dual PPARα/δ agonist, in patients with PBC. Methods: This 12-week, double-blind phase II trial enrolled 45 adults with PBC who had incomplete response to ursodeoxycholic acid (alkaline phosphatase levels ≥1.67-fold the upper limit of normal (ULN). Patients were randomly assigned to elafibranor 80 mg, elafibranor 120 mg or placebo. The primary endpoint was the relative change of ALP at 12 weeks (NCT03124108). Results: At 12 weeks, ALP was reduced by -48.3\ub114.8% in the elafibranor 80 mg group (p <0.001 vs. placebo) and by -40.6\ub117.4% in the elafibranor 120 mg group (p <0.001) compared to a +3.2\ub114.8% increase in the placebo group. The composite endpoint of ALP ≤1.67-fold the ULN, decrease of ALP >15% and total bilirubin below the ULN was achieved in 67% patients in the elafibranor 80 mg group and 79% patients in the elafibranor 120 mg group, vs. 6.7% patients in the placebo group. Levels of gamma-glutamyltransferase decreased by 37.0\ub125.5% in the elafibranor 80 mg group (p <0.001) and 40.0\ub124.1% in the elafibranor 120 mg group (p <0.01) compared to no change (+0.2\ub126.0%) in the placebo group. Levels of disease markers such as IgM, 5’-nucleotidase or high-sensitivity C-reactive protein were likewise reduced by elafibranor. Pruritus was not induced or exacerbated by elafibranor and patients with pruritus at baseline reported less pruritic symptoms at the end of treatment. All possibly drug-related non-serious adverse events were mild to moderate. Conclusion: In this randomized phase II trial, elafibranor was generally safe and well tolerated and significantly reduced levels of ALP, composite endpoints of bilirubin and ALP, as well as other markers of disease activity in patients with PBC and an incomplete response to ursodeoxycholic acid. Lay summary: Patients with primary biliary cholangitis (a rare chronic liver disease) that do not respond to standard therapy remain at risk of disease progression toward cirrhosis and impaired quality of life. Elafibranor is a nuclear receptor agonist that we tested in a randomized clinical trial over 12 weeks. It successfully decreased levels of disease activity markers, including alkaline phosphatase. Thus, this study is the foundation for a larger prospective study that will determine the efficacy and safety of this drug as a second-line therapy. Clinical trial registration number: Clinical Trials.gov NCT0312410