Staphylococcus aureus still the commonest culprit

Background: Sepsis is still one of the most leading cause of death in world. 19 million sepsis (formerly severe sepsis) cases and 5 million sepsis-related deaths are estimated to occur annually. Sepsis is also one of the most common cause of patients getting critically ill and getting admission in intensive care unit. The main focus of this study is to identify the culture isolate from the critically ill patients and to check for the antibiotic sensitivity and resistance and identify if the report has changed the course of treatment and outcome of the patients. Critical illness is a life-threatening multisystem process that can result in significant morbidity or mortality. Critically ill patients are those who have dysfunction or failure of one or more organs/system and depend on survival from advanced instruments of monitoring and therapy. The aim of the study was to identify the causative organism causing sepsis in critically ill patients.Methods: It will be multi central retrospective study which included patients of critical illness of Rohilkhand Medical college, Bareilly, Uttar Pradesh and Varun Arjun Medical college, Shahjanpur, A total of 468 patients were taken for the analysis during period of from August 2018 to November 2019 among of which 324 samples came positive. Samples were taken on day one of the patient’s presentation to the hospital and were analysed in BD BACTEC culture medium. Patient’s data were taken from records available at both the hospitals. Sensitivity was performed using disk diffusion method and the results were compared with the records of patients.Results: Among of samples taken that is 324; 194 were male and 130 were female. Maximum patients which were tested positive for Staphylococcus aureus (n=198). Others included Streptococcus pneumococcus (n=25), Escherichia coli (n=50), Klebsiella oxytoca (n=13), Klebsiella pneumoniae (n=15), Pseudomonas aeruginosa (n=20), and Acinetobacter (n=3). In retrospective analysis of the patients of all 324 cases treatment in approximately 148 patients was changed due to change in the sensitivity of antibiotics.Conclusions: Staphylococcus still dominates the sepsis. It is advisable to add an antibiotic with gram negative if patients count does not improve in first 24 hour.

Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi

In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection. The total size of the corpus currently stands at approximately 18 hours (approx. 4-5 hours each language) and it is transcribed and annotated with grammatical information such as part-of-speech tags, morphological features and Universal dependency relationships. We discuss our methodology for data collection in these languages, most of which was done in the middle of the COVID-19 pandemic, with one of the aims being to generate some additional income for low-income groups speaking these languages. In the paper, we also discuss the results of the baseline experiments for automatic speech recognition system in these languages.Comment: Speech for Social Good Workshop, 2022, Interspeech 202

SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages

This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features. In terms of the task, we enrich UniMorph with new data for 32 languages from 13 language families, with most of them being under-resourced: Kunwinjku, Classical Syriac, Arabic (Modern Standard, Egyptian, Gulf), Hebrew, Amharic, Aymara, Magahi, Braj, Kurdish (Central, Northern, Southern), Polish, Karelian, Livvi, Ludic, Veps, Võro, Evenki, Xibe, Tuvan, Sakha, Turkish, Indonesian, Kodi, Seneca, Asháninka, Yanesha, Chukchi, Itelmen, Eibela. We evaluate six systems on the new data and conduct an extensive error analysis of the systems' predictions. Transformer-based models generally demonstrate superior performance on the majority of languages, achieving >90% accuracy on 65% of them. The languages on which systems yielded low accuracy are mainly under-resourced, with a limited amount of data. Most errors made by the systems are due to allomorphy, honorificity, and form variation. In addition, we observe that systems especially struggle to inflect multiword lemmas. The systems also produce misspelled forms or end up in repetitive loops (e.g., RNN-based models). Finally, we report a large drop in systems' performance on previously unseen lemmas.Peer reviewe

UniMorph 4.0:Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Validation of SYBR green I based closed tube loop mediated isothermal amplification (LAMP) assay and simplified direct-blood-lysis (DBL)-LAMP assay for diagnosis of visceral leishmaniasis (VL).

BackgroundThe World Health Organization has targeted elimination of visceral leishmaniasis (VL) in the Indian subcontinent (ISC) by 2020. Despite distinctive decline seen in the number of VL cases in ISC, there is still a quest for development of a diagnostic test which has the utility for detection of active infection and relapse cases and as a test of cure. The present study validated the sensitivity and specificity of SYBR Green I based closed tube LAMP assay reported by us for diagnosis of VL.MethodologyThe validation study was carried out at two endemic sites in India, located at Rajendra Memorial Research Institute of Medical Sciences (RMRIMS), Patna and Institute of Medical Sciences (IMS), Banaras Hindu University (BHU), Varanasi. Standard operating protocols were provided at the two sites for applying LAMP assay on confirmed VL cases. The diagnostic accuracy of LAMP assay was evaluated by Receiver operator curve (ROC) analysis. Furthermore, a simplified LAMP assay based on direct blood lysis, DBL-LAMP, was developed and verified for its diagnostic accuracy.Principal findingsA total of 267 eligible participants were included in the study which comprised of 179 VL cases and 88 controls. Sensitivity and specificity of the LAMP assay were 98.32% (95% C.I- 95.2-99.7%) and 96.59% (95% C.I.-90.4-99.3%), respectively. ROC curve analysis depicted no significant difference between area under curve (AUCROC) for LAMP assay and rK39 RDT, indicative of LAMP as an excellent diagnostic test. DBL-LAMP assay, performed on 67 VL and 100 control samples, yielded a sensitivity of 93.05% (95% C.I- 84.75-97%) and specificity of 100% (95% C.I.- 96.30-100%).Conclusions/significanceThe validated closed tube LAMP for diagnosis of VL will provide impetus to the ongoing VL elimination programme in ISC. The assay based on direct blood lysis promotes its scope for application in field settings by further reducing time and cost

RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations

This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We develop various data generation techniques that expand the originally provided train set and show that all methods increase the performance of models trained on these expanded datasets. Our final system outperforms the baseline from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline's 67.4