108 research outputs found
Conservational Analysis of Influenza A Virus RNA-dependent RNA Polymerase
No full textCompeting Interests: The authors have declared that no competing interests exist. Copyright: 2015 Darapaneni V et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Correspondence to: Vivek Darapaneni, Department of virology and computational biochemistry, Sake
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning
No full textThis repository contains resources developed for the paper: Gupta, V., Zhang, S., Vempala, A., He, Y., Choji, T., Srikumar V., Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning. In: Proceeding of the The Association of Computational Linguistic 2022 (ACL ’22), May 2022".
It includes the relevant rows marking for the train set of the InfoTabS dataset (https://infotabs.github.io/) Gupta et. al. 2020 [1].
We followed the protocol of Gupta et al. (2022) [2] which annotated the development and test sets (alpha1, alpha2, alpha3) sets: one table and three distinct hypotheses formed a HIT. We divide the tasks equally into 110 batches, each batch having 51 HITs each having three examples. In total, we collected 81,282 annotations from 90 distinct annotators.
Overall, twenty five annotators completed over 1000 tasks, corresponding to 87.75 % of the examples, indicating a tail distribution with the annotations. Overall, 16,248 training set table-hypothesis pairs were successfully labeled with the evidence rows. On average, we obtain 89.49% F1-score with equal precision and recall for annotation agreement when compared with majority vote. It also includes an annotation template used on the mTurk platform for crowdsourcing. The cited datasets were used in this work. The cited datasets were used in this work.
Files to access the annotation follow the below structure:
annotation_batches
batches_test: contain final results “.csv” files for all the development and test set batches (taken from Gupta et. al. 2022)
batches_train: contain our annotated results “.csv” files for all the train set batches
README.md: contain the readme for the annotation batches details
main_template_row_relevant.html: content the annotation template used for each HIT i.e. marking the relevant row for each instance
annotation_stats.md: Have details of the annotation statistics
release_mturk: contain the release batches details i.e. csv for corresponding batches released
Files to recreate the annotation statistics and pre-processed data:
results_test: contain the pre-processed batch csv for dev and test set each batch. In the dev and test set. The integrated one computes the agreement stats for all the batches.(taken from Gupta et. al. 2022)
results_train: similar to resutls_train expect contain the pre-processed batch csv for train set.
scripts: contain the scripts needed to create the csv in the results_test and results_train sets. The script title denotes the function (the statistic it computes) for the scripts.
src: the scripts use these python files to create the relevant statistics.
References:
[1] InfoTabS: Inference on Tables as Semi-structured Data, Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar, ACL 2020
[2] Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning, Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar, TACL 2022, presented at ACL 202
Mixed integer polynomial programming
No full textThe mixed integer polynomial programming problem is reformulated as a multi-parametric programming problem by relaxing integer variables as continuous variables and then treating them as parameters. The optimality conditions for the resulting parametric programming problem are given by a set of simultaneous parametric polynomial equations which are solved analytically to give the parametric optimal solution as a function of the relaxed integer variables. Evaluation of the parametric optimal solution for integer variables fixed at their integer values followed by screening of the evaluated solutions gives the optimal solutions
TempTabQA: Temporal Question Answering for Semi-Structured Tables
No full text<p>This repository contains resources, namely <strong>TempTabQA</strong>, developed for the paper: Gupta, V., Kandoi, P., Vora, M., Zhang, S., He, Y., Reinanda R., Srikumar V., <i>TempTabQA: Temporal Question Answering for Semi-Structured Tables</i>. In: Proceeding of the The 2023 Conference on Empirical Methods in Natural Language Processing, Dec 2023.</p><p><strong>TempTabQA</strong> is a dataset which comprises 11,454 question-answer pairs extracted from Wikipedia Infobox tables. These question-answer pairs are annotated by human annotators. We provide two test sets instead of one: the <strong>Head</strong> set with popular frequent domains, and the <strong>Tail</strong> set with rarer domains. </p><p>Files to access the annotation follow the below structure:</p><p>Maindata</p><ul><li>qapairs: split into train, dev, head, and tail sets, in both csv and json formats</li><li>Tables: Wikipedia category and tables metadata in csv, json and html formats</li></ul><p>Carefully read the ```LICENCE``` for non-academic usage.</p><p><i>Note : Wherever required consider the year of 2022 as the build date for the dataset.</i></p><p> </p><p> </p><p> </p>
Author response
No full textPreviously we showed that membrane fusion is required for TANGO1-dependent export of procollagen VII from the endoplasmic reticulum (ER) (Nogueira, et al., 2014). Along with the t-SNARE Syntaxin 18, we now reveal the complete complement of SNAREs required in this process, t-SNAREs BNIP1 and USE1, and v-SNARE YKT6. TANGO1 recruits YKT6-containing ER Golgi Intermediate Compartment (ERGIC) membranes to procollagen VII-enriched patches on the ER. Moreover residues 1214-1396, that include the first coiled coil of TANGO1, specifically recruit ERGIC membranes even when targeted to mitochondria. TANGO1 is thus pivotal in concentrating procollagen VII in the lumen and recruiting ERGIC membranes on the cytoplasmic surface of the ER. Our data reveal that growth of a mega transport carrier for collagen export from the ER is not by acquisition of a larger patch of ER membrane, but instead by addition of ERGIC membranes to procollagen-enriched domains of the ER by a TANGO1-mediated process.European Commission (European Union Seventh Framework Programme (FP7/2007-2013) 625149). Ministerio de Economía y Competitividad (SEV-2012-0208). Ministerio de Economía y Competitividad (Plan Nacional BFU2013-44188-P). European Research Council (Consolider CSD2009-00016)
Development and Psychometric Properties of A Screening Tool for Assessing Developmental Coordination Disorder in Adults
No full textBackground: Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder affecting motor coordination. Evidence suggests this disorder persists into adulthood and may be associated with biomechanical dysfunction and pain. We report on the development and initial validation of a questionnaire to assess for DCD in adults. Methods: An initial item pool (13 items) was derived from the American Psychiatric Association criteria and World Health Organisation definition for DCD. An expert panel assessed face and content validity which led to a 9-item Functional Difficulties Questionnaire (FDQ-9) with possible scores ranging from 9-36 (higher scores indicating greater functional difficulties). The FDQ-9 was piloted on individuals recruited from convenience samples. The underlying factor structure and aspects of reliability, validity and accuracy were tested. The Receiver Operating Characteristic Curve was employed to evaluate the diagnostic accuracy of the test using self-reported dyspraxia as the reference standard. Results: Principal Axis Factoring yielded a two factor solution relating to gross and fine motor skills; for conceptual parsimony these were combined. Internal reliability was high (0.81), the mean inter-item correlation was 0.51 and preliminary findings suggested satisfactory construct validity. The Area under the Curve was 0.918 [95% CI 0.84-1.00] indicating a diagnostic test with high accuracy. A cut-off score was established with a sensitivity and specificity of 86% [95% CI 78%-89%] and 81% [95 % CI 73%-89%] respectively. Test-retest reliability was good (ICC 0.96 [95% CI 0.92 to 0.98]. Conclusion: The psychometric properties of the FDQ-9 appear promising. Work is required to conduct further psychometric evaluations on new samples and apply the scale to clinical practice
Author response
No full textTANGO1 binds and exports Procollagen VII from the endoplasmic reticulum (ER). In this study, we report a connection between the cytoplasmic domain of TANGO1 and SLY1, a protein that is required for membrane fusion. Knockdown of SLY1 by siRNA arrested Procollagen VII in the ER without affecting the recruitment of COPII components, general protein secretion, and retrograde transport of the KDEL-containing protein BIP, and ERGIC53. SLY1 is known to interact with the ER-specific SNARE proteins Syntaxin 17 and 18, however only Syntaxin 18 was required for Procollagen VII export. Neither SLY1 nor Syntaxin 18 was required for the export of the equally bulky Procollagen I from the ER. Altogether, these findings reveal the sorting of bulky collagen family members by TANGO1 at the ER and highlight the existence of different export pathways for secretory cargoes one of which is mediated by the specific SNARE complex containing SLY1 and Syntaxin 18.We thank the entire Malhotra Lab for help with figures and corrections. We would like to acknowledge the CRG's mass spectrometry facility for sample analysis and the CRG advanced light microscopy facility for help with conventional and STED fluorescent microscopy. P Erlmann was partially funded by a DFG fellowship (ER 681/1-1). V Malhotra is an Institució Catalana de Recerca i Estudis Avançats (ICREA) professor at the Center for Genomic Regulation and the work in his laboratory is funded by grants from Plan Nacional (BFU2008-00414), Consolider (CSD2009-00016), Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR) Grups de Recerca Emergents (SGR2009-1488; AGAUR-Catalan Government), and European Research Council (268692)
Author Correction: COVID-19 vaccine guidance for patients with cancer participating in oncology clinical trials
No full textIn the original version of this Perspective, the name of the author Giuseppe Curigliano was incorrectly written as Guiseppe Curigiliano. The affiliations have been corrected in the HTML and PDF versions of the manuscrip
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