Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation

Background: Manual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data extraction and entry. Text mining software that can semi- or fully automate information retrieval from the literature would thus provide a significant boost to manual curation efforts. Results: We employ the Textpresso category-based information retrieval and extraction system http://www.textpresso.org webcite, developed by WormBase to explore how Textpresso might improve the efficiency with which we manually curate C. elegans proteins to the Gene Ontology's Cellular Component Ontology. Using a training set of sentences that describe results of localization experiments in the published literature, we generated three new curation task-specific categories (Cellular Components, Assay Terms, and Verbs) containing words and phrases associated with reports of experimentally determined subcellular localization. We compared the results of manual curation to that of Textpresso queries that searched the full text of articles for sentences containing terms from each of the three new categories plus the name of a previously uncurated C. elegans protein, and found that Textpresso searches identified curatable papers with recall and precision rates of 79.1% and 61.8%, respectively (F-score of 69.5%), when compared to manual curation. Within those documents, Textpresso identified relevant sentences with recall and precision rates of 30.3% and 80.1% (F-score of 44.0%). From returned sentences, curators were able to make 66.2% of all possible experimentally supported GO Cellular Component annotations with 97.3% precision (F-score of 78.8%). Measuring the relative efficiencies of Textpresso-based versus manual curation we find that Textpresso has the potential to increase curation efficiency by at least 8-fold, and perhaps as much as 15-fold, given differences in individual curatorial speed. Conclusion: Textpresso is an effective tool for improving the efficiency of manual, experimentally based curation. Incorporating a Textpresso-based Cellular Component curation pipeline at WormBase has allowed us to transition from strictly manual curation of this data type to a more efficient pipeline of computer-assisted validation. Continued development of curation task-specific Textpresso categories will provide an invaluable resource for genomics databases that rely heavily on manual curation

Introduction to the Bethe ansatz II

Building on the fundamentals introduced in part I, we employ the Bethe ansatz to study some ground-state properties (energy, magnetization, susceptibility) of the one-dimensional s=1/2 Heisenberg antiferromagnet in zero and nonzero magnetic field. The 2-spinon triplet and singlet excitations from the zero-field ground state are discussed in detail, and their energies are calculated for finite and infinite chains. Procedures for the numerical calculation of real and complex solutions of the Bethe ansatz equations are discussed and applied. The paper is designed as a tutorial for beginning graduate students. It includes 10 problems for further study.Comment: 9 pages, 5 figure

GROUP 2018 Special Issue Guest Editorial: Another 25 Years of GROUP

For over 25 years, the ACM International Conference on Supporting GroupWork (GROUP) has been and will continue to be the premier venue for research on Computer-Supported Cooperative Work,Human–Computer Interaction, Computer-Supported Collaborative Learning, and Socio-Technical Studies. The three papers in this special issue demonstrate GROUP’s continued commitment to diverse research approaches, emerging technologies, and collaborative work. We hope you enjoy these papers and, like us, look forward to another 25 years of GROUP.https://deepblue.lib.umich.edu/bitstream/2027.42/146739/1/Robert et al. 2018.pdfDescription of Robert et al. 2018.pdf : Articl

CT differentiation of enlarged mediastinal lymph node due to anthracosis from metastatic lymphadenopathy: a comparative study proven by endobronchial US-guided transbronchial needle aspiration

PURPOSEAnthracosis often results in mediastinal nodal enlargement. The aim of this comparative study was to evaluate if it is possible to differentiate endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) proven anthracotic lymph nodes from malignant lymph node enlargement by means of multislice computed tomography (MSCT).METHODSWe compared the MSCT findings of 89 enlarged lymph nodes due to anthracosis with 54 malignant lymph nodes (non-small cell lung cancer 75.9%, small cell lung cancer 18.5%, and non-Hodgkin lymphoma 5.6%). The lymph nodes were assessed for density (calcification, fat, and necrosis), shape (oval, round), contrast enhancement, and contour (sharp, ill-defined).RESULTSMalignant lymph nodes showed significantly greater axis diameters (P < 0.001). Both anthracotic and malignant nodes were most often oval (86.5% of all malignant nodes vs. 81.5% of all anthracotic nodes, P = 0.420) and showed confluence in a remarkable percentage (28.1% vs. 42.6%, P = 0.075). Anthracotic nodes showed calcifications more often (18% vs. 0%, P < 0.001). Malignant lymph nodes showed a significantly greater short and long axis diameter (P < 0.001), and they had a higher frequency of ill-defined contours (27.8% vs. 2.2%, P < 0.001) and contrast enhancement (27.8% vs. 5.6%, P < 0.001). Nodal necrosis, which appeared in one third of the malignant nodes, was not observed in anthracosis (35.2% vs. 0%, P < 0.001). Confluence of enlarged lymph nodes was seen in malignant lymph nodes (42.6%), as well as in lymph node enlargement due to anthracosis (28.1%, P = 0.075).CONCLUSIONOur results show that there are significant differences in MSCT findings of malignant enlarged lymph nodes and benign lymph node enlargement due to anthracosis

Synovitis and bone inflammation in early rheumatoid arthritis: high-resolution multi-pinhole SPECT versus MRI

PURPOSEWe aimed to assess the relationship between bone inflammation in multi-pinhole single-photon emission computed tomography (MPH-SPECT) and synovitis detected by magnetic resonance imaging (MRI) in early rheumatoid arthritis patients. MATERIALS AND METHODSMPH-SPECT with technetium dicarboxypropanedisphosphonate (Tc-99mDPD) and 3 Tesla MRI were performed in 10 early rheumatoid arthritis patients. Eighty finger joint sites were assessed for increased osteoblastic activity using visual and region-of-interest (ROI) analysis. Presence of joint inflammation in MRI was investigated using the subscores of the rheumatoid arthritis MRI score. RESULTSTc-99mDPD uptake was increased in 38 (47.5%) and 22 (27.5%) joint sites as determined by visual and ROI analysis, respectively. A total of 32 (84.2%) sites with increased bone metabolism showed a normal MRI bone signal. The MPHSPECT uptake ratio was elevated only in the subgroup with severe synovitis (P < 0.001). CONCLUSIONIn early rheumatoid arthritis, molecular imaging with MPHSPECT detects higher rates of inflammatory bone involvement compared to MRI. Our preliminary data suggest that osteitis is related to severe synovitis

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery