The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a "SQLite" relational database or "ASCII" flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R- project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.Peer reviewe

Glycans in Sera of Amyotrophic Lateral Sclerosis Patients and Their Role in Killing Neuronal Cells

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by degeneration of upper and lower motor neurons. To date, glycosylation patterns of glycoproteins in fluids of ALS patients have not been described. Moreover, the aberrant glycosylation related to the pathogenesis of other neurodegenerative diseases encouraged us to explore the glycome of ALS patient sera. We found high levels of sialylated glycans and low levels of core fucosylated glycans in serum-derived N-glycans of patients with ALS, compared to healthy volunteer sera. Based on these results, we analyzed the IgG Fc N297-glycans, as IgG are major serum glycoproteins affected by sialylation or core fucosylation and are found in the motor cortex of ALS patients. The analyses revealed a distinct glycan, A2BG2, in IgG derived from ALS patient sera (ALS-IgG). This glycan increases the affinity of IgG to CD16 on effector cells, consequently enhancing Antibody-Dependent Cellular Cytotoxicity (ADCC). Therefore, we explore whether the Fc-N297-glycans of IgG may be involved in ALS disease. Immunostaining of brain and spinal cord tissues revealed over-expression of CD16 and co-localization of intact ALS-IgG with CD16 and in brain with activated microglia of G93A-SOD1 mice. Intact ALS-IgG enhanced effector cell activation and ADCC reaction in comparison to sugar-depleted or control IgG. ALS-IgG were localized in the synapse between brain microglia and neurons of G93A-SOD1 mice, manifesting a promising in vivo ADCC reaction. Therefore, glycans of ALS-IgG may serve as a biomarker for the disease and may be involved in neuronal damage

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Domain imaging in FINEMET ribbons

The magnetization behaviour of a ferromagnetic material depends on its domain structure, which in turn is largely determined by magnetic anisotropies. In this work, domain patterns were observed by a quite forgotten but still the simplest and the cheapest technique: the Bitter method. A systematic study of the evolution of the domain structure in FINEMET ribbons after thermal annealing is presented, correlating the results with the crystalline structure, magnetostriction and coercivity measurements.Fil: Silveyra, Josefina María. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Fisica. Laboratorio de Sólidos Amorfos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina; ArgentinaFil: Vlasak, G.. Slovak Academy of Sciences; EslovaquiaFil: Švec, P.. Slovak Academy of Sciences; EslovaquiaFil: Janičkovič, D.. Slovak Academy of Sciences; EslovaquiaFil: Cremaschi, Victoria Juliana. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Fisica. Laboratorio de Sólidos Amorfos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina; Argentin