Characterization of Low-mass, Wide-separation Substellar Companions to Stars in Upper Scorpius: Near-infrared Photometry and Spectroscopy

We present new 0.9-2.45 $\mu$m spectroscopy ($R \sim 1000$), and $Y$, $J$, $H$, $K_s$, $L^\prime$ photometry, obtained at Gemini North, of three low-mass brown dwarf companions on wide orbits around young stars of the Upper Scorpius OB association: HIP 78530 B, [PGZ2001] J161031.9-191305 B, and GSC 06214-00210 B. We use these data to assess the companions' spectral type, temperature, surface gravity and mass, as well as the ability of the BT-Settl and Drift-Phoenix atmosphere models to reproduce the spectral features of young substellar objects. For completeness, we also analyze the archival spectroscopy and photometry of the Upper Scorpius planetary mass companion 1RXS J160929.1-210524 b. Based on a comparison with model spectra we find that the companions, in the above order, have effective temperatures of 2700, 2500, 2300 and 1700 K. These temperatures are consistent with our inferred spectral types, respectively M7 $\beta$, M9 $\gamma$, M9 $\gamma$, and L4 $\gamma$. From bolometric luminosities estimated from atmosphere model spectra adjusted to our photometry, and using evolution models at 5-10 Myr, we estimate masses of 21-25, 28-70, 14-17 and 7-12 $M_{\rm Jup}$, respectively. J1610-1913 B appears significantly over-luminous for its inferred temperature, which explains its higher mass estimate. Synthetic spectra based on the BT-Settl and Drift-Phoenix atmosphere models generally offer a good fit to our observed spectra, although our analysis has highlighted a few problems. For example, the best fits in the individual near-infrared bands occur at different model temperatures. Also, temperature estimates based on a comparison of the broadband magnitudes and colors of the companions to synthetic magnitudes from the models are systematically lower than the temperature estimates based on a comparison with synthetic spectra.Comment: 16 pages, 8 figures, published in the Astrophysical Journa

OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents

Motivation: Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. Results: We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. Availability: The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. Contact: [email protected]

Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten

Das noch recht junge Forschungsgebiet "Text Mining" umfaßt eine Verbindung von Verfahren der Sprachverarbeitung mit Datenbank- und Informationssystemtechnologien. Es entstand aus der Beobachtung, dass ca. 85% aller Datenbankinhalte nur in unstrukturierter Form vorliegen, so dass sich die Techniken des klassischen Data Mining zur Wissensgewinnung nicht anwenden lassen. Beispiele für solche Daten sind Volltextdatenbanken mit Büchern, Unternehmenswebseiten, Archive mit Zeitungsartikeln oder wissenschaftlichen Publikationen, aber auch Ströme kontinuierlich auflaufender Emails oder Meldungen von Nachrichtenagenturen (Newswires). Im Gegensatz zum Information Retrieval geht es beim Text Mining nicht darum, lediglich Dokumente anhand von Anfragen aufzufinden, sondern aus einem einzelnen oder einem Satz von Dokumenten neues Wissen zu gewinnen, etwa durch automatische Textzusammenfassungen, die Erkennung und Verfolgung benannter Objekte oder die Aufdeckung neuer Trends in Forschung und Industrie. Durch die ständig wachsende Zahl elektronisch verfügbarer Texte werden automatisch arbeitende Verfahren zur Bewältigung der Informationsflut immer dringender, was Text Mining zu einem sehr aktiven und auch kommerziell interessanten Forschungsgebiet macht. Der vorliegende Bericht enthält eine Auswahl von Themen, die von Studierenden der Universität Karlsruhe im Rahmen eines Hauptseminars am IPD im Wintersemester 2004/2005 erarbeitet wurden. Sie reichen von den Grundlagen der Computerlinguistik über einzelne Algorithmen zur Sprachverarbeitung bis hin zu konkreten Anwendungen im Text Mining. Zahlreiche Literaturreferenzen zu jedem Kapitel sollen dem Leser eine weitergehende Studie der einzelnen Themen ermöglichen

Automated extraction and semantic analysis of mutation impacts from the biomedical literature

BACKGROUND: Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities. RESULTS: We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner. CONCLUSION: We present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions

Lower fractional anisotropy without evidence for neuro-inflammation in patients with early-phase schizophrenia spectrum disorders

Various lines of research suggest immune dysregulation as a potential therapeutic target for negative and cognitive symptoms in schizophrenia spectrum disorders (SSD). Immune dysregulation would lead to higher extracellular free-water (EFW) in cerebral white matter (WM), which may partially underlie the frequently reported lower fractional anisotropy (FA) in SSD. We aim to investigate differences in EFW concentrations – a presumed proxy for neuro-inflammation – between early-phase SSD patients (n = 55) and healthy controls (HC; n = 37), and to explore immunological and cognitive correlates. To increase specificity for EFW, we study several complementary magnetic resonance imaging contrasts that are sensitive to EFW. FA, mean diffusivity (MD), magnetization transfer ratio (MTR), myelin water fraction (MWF) and quantitative T1 and T2 were calculated from diffusion-weighted imaging (DWI), magnetization transfer imaging (MTI) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT). For each measure, WM skeletons were constructed with tract-based spatial statistics. Multivariate SSD-HC comparisons with WM skeletons and their average values (i.e. global WM) were not statistically significant. In voxel-wise analyses, FA was significantly lower in SSD in the genu of the corpus callosum and in the left superior longitudinal fasciculus (p < 0.04). Global WM measures did not correlate with immunological markers (i.e. IL1-RA, IL-6, IL-8, IL-10 and CRP) or cognition in HC and SSD after corrections for multiple comparisons. We confirmed lower FA in early-phase SSD patients. However, non–FA measures did not provide additional evidence for immune dysregulation or for higher EFW as the primary mechanism underlying the reported lower FA values in SSD

Cytokine alterations in first-episode schizophrenia patients before and after antipsychotic treatment

Schizophrenia has been associated with central nervous system and peripheral immune system imbalances. However, most studies have not yielded conclusive results due to limitations such as small sample size, dissimilarities in the clinical status of patients and the high variability of cytokine levels within the normal human population. Here, we have attempted to account for these limitations by carrying out standardised multiplex immunoassay analyses of 9 cytokines in serum from 180 antipsychotic-naïve first-episode schizophrenia patients and 350 matched controls across 5 clinical cohorts. All subjects were matched for potential confounding factors including age, gender, smoking and body mass index. We found that the levels of interleukin (IL)-1RA, IL-10 and IL-15 were increased significantly in patients across the cohorts. We also found that the levels of IL-1RA and IL-10 were decreased in 32 patients who had been followed up and treated for 6. weeks with atypical antipsychotics. Interestingly, we found that the changes in IL-10 levels were significantly correlated with the improvements in negative, general and total symptom scores. These results indicate that mixed pro- and anti-inflammatory responses may be altered in first onset patients, suggesting a role in the aetiology of schizophrenia. The finding that only the anti-inflammatory cytokine IL-10 responded to treatment in parallel with symptom improvement suggests that this could be used as a potential treatment response biomarker in future studies of schizophrenia

Semantic text mining support for lignocellulose research

Biofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties. Semantic technologies, including natural language processing, ontologies, semantic Web services and Web-based collaboration tools, promise to support users in handling complex data, thereby facilitating knowledge-intensive tasks. An ongoing challenge is to select the appropriate technologies and combine them in a coherent system that brings measurable improvements to the users. We present our ongoing development of a semantic infrastructure in support of genomics-based lignocellulose research. Part of this effort is the automated curation of knowledge from information on fungal enzymes that is available in the literature and genome resources. Working closely with fungal biology researchers who manually curate the existing literature, we developed ontological natural language processing pipelines integrated in a Web-based interface to assist them in two main tasks: mining the literature for relevant knowledge, and at the same time providing rich and semantically linked information

Improvement of Cardiac Function After Roux-en-Y Gastric Bypass in Morbidly Obese Patients Without Cardiac History Measured by Cardiac MRI

Purpose: Metabolic syndrome in patients with morbid obesity causes a higher cardiovascular morbidity, eventually leading to left ventricular hypertrophy and decreased left ventricular ejection fraction (LVEF). Roux-en-Y gastric bypass (RYGB) is considered the gold standard modality for treatment of morbid obesity and might even lead to improved cardiac function. Our objective is to investigate whether cardiac function in patients with morbid obesity improves after RYGB. Materials and Methods: In this single center pilot study, 15 patients with an uneventful cardiac history who underwent RYGB were included from May 2015 to March 2016. Cardiac function was measured by cardiac magnetic resonance imaging (CMRI), performed preoperatively and 3, 6, and 12 months postoperative. LVEF and myocardial mass and cardiac output were measured. Results: A total of 13 patients without decreased LVEF preoperative completed follow-up (mean age 37, 48.0 ± 8.8). There was a significant decrease of cardiac output 12 months postoperative (8.3 ± 1.8 preoperative vs. 6.8 ± 1.8 after 12 months, P = 0.001). Average myocardial mass declined by 15.2% (P < 0.001). After correction for body surface area (BSA), this appeared to be non-significant (P = 0.36). There was a significant improvement of LVEF/BSA at 6 and 12 months postoperative (26.2 ± 4.1 preoperative vs. 28.4 ± 3.4 and 29.2 ± 3.6 respectively, both P = 0.002). Additionally, there was a significant improvement of stroke volume/BSA 12 months after surgery (45.8 ± 8.0 vs. 51.9 ± 10.7, P = 0.033). Conclusion: RYGB in patients with morbid obesity with uneventful history of cardiac disease leads to improvement of cardiac function

Energy harvesting for active implants: powering a ruminal pH-monitoring system

Energy harvesting is a feasible method to prolong service life of implanted devices. We present a thermal energy harvesting approach for a ruminal pH-monitoring probe in cattle. Thermoelectric generators utilize the temperature gradient between the probe and the ruminal fluid during water intake. The in vivo experiment yielded a maximum electric power of 32 μW