An Empirical Analysis of Development Processes for Anticipatory Standards

There is an evolution in the process used by standards-development organizations (SDOs) and this is changing the prevailing standards development activity (SDA) for information and communications technology (ICT). The process is progressing from traditional SDA modes, typically involving the selection from many candidate, existing alternative components, into the crafting of standards that include a substantial design component (SSDC), or 'anticipatory' standards. SSDC require increasingly important roles from organizational players as well as SDOs. Few theoretical frameworks exist to understand these emerging processes. This project conducted archival analysis of SDO documents for a selected subset of web-services (WS) standards taken from publicly available sources including minutes of meetings, proposals, drafts and recommendations. This working paper provides a deeper understanding of SDAs, the roles played by different organizational participants and the compliance with SDO due process requirements emerging from public policy constraints, recent legislation and standards accreditation requirements. This research is influenced by a recent theoretical framework that suggests viewing the new standards-setting processes as a complex interplay among three forces: sense-making, design, and negotiation (DSN). The DSN model provides the framework for measuring SDO progress and therefore understanding future generations of standards development processes. The empirically grounded results are useful foundation for other SDO modeling efforts

Deep learning for skin melanoma classification using dermoscopic images in different color spaces

Skin cancer begins in the skin cells. The damage to the skin cells can cause genetic mutations that lead to uncontrolled growth and the formation of tumors. It is estimated that millions of people are diagnosed with skin cancer of different kinds each year. The earlier a skin cancer is diagnosed, the better the patient's prognosis and the lower their chance of complications. In this work, an efficient deep learning classification (EDLCS) to classify dermoscopic images is developed. The importance of color in the diagnosis of skin melanoma has caused color analysis to attract considerable attention from researchers of image-based skin melanoma analysis. Three different color spaces such as red-green-blue (RGB), hue-saturation-lightness (HIS) and LAB are investigated in this study. The obtained dermoscopic images are in RGB color space. The RGB dermoscopic images are first converted into HSV and LAB spaces to investigate the HSV and LAB color spaces for melanoma classification. Then, the color space converted image is fed to the proposed EDLCS to evaluate their performances. Results show that the proposed EDLCS provides 99.58% accuracy while using the LAB color model to classify preprocessed images while other models provide 99.17%

Global text mining and development of pharmacogenomic knowledge resource for precision medicine

Understanding patients' genomic variations and their effect in protecting or predisposing them to drug response phenotypes is important for providing personalized healthcare. Several studies have manually curated such genotype-phenotype relationships into organized databases from clinical trial data or published literature. However, there are no text mining tools available to extract high-accuracy information from such existing knowledge. In this work, we used a semiautomated text mining approach to retrieve a complete pharmacogenomic (PGx) resource integrating disease-drug-gene-polymorphism relationships to derive a global perspective for ease in therapeutic approaches. We used an R package, pubmed.mineR, to automatically retrieve PGx-related literature. We identified 1,753 disease types, and 666 drugs, associated with 4,132 genes and 33,942 polymorphisms collated from 180,088 publications. With further manual curation, we obtained a total of 2,304 PGx relationships. We evaluated our approach by performance (precision = 0.806) with benchmark datasets like Pharmacogenomic Knowledgebase (PharmGKB) (0.904), Online Mendelian Inheritance in Man (OMIM) (0.600), and The Comparative Toxicogenomics Database (CTD) (0.729). We validated our study by comparing our results with 362 commercially used the US- Food and drug administration (FDA)-approved drug labeling biomarkers. Of the 2,304 PGx relationships identified, 127 belonged to the FDA list of 362 approved pharmacogenomic markers, indicating that our semiautomated text mining approach may reveal significant PGx information with markers for drug response prediction. In addition, it is a scalable and state-of-art approach in curation for PGx clinical utility

COSGAP: COntainerized Statistical Genetics Analysis Pipelines

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Age and frailty are independently associated with increased COVID-19 mortality and increased care needs in survivors: results of an international multi-centre study

INTRODUCTION: Increased mortality has been demonstrated in older adults with COVID-19, but the effect of frailty has been unclear.METHODS: This multi-centre cohort study involved patients aged 18years and older hospitalised with COVID-19, using routinely collected data. We used Cox regression analysis to assess the impact of age, frailty, and delirium on the risk of inpatient mortality, adjusting for sex, illness severity, inflammation, and co-morbidities. We used ordinal logistic regression analysis to assess the impact of age, Clinical Frailty Scale (CFS), and delirium on risk of increased care requirements on discharge, adjusting for the same variables.RESULTS: Data from 5,711 patients from 55 hospitals in 12 countries were included (median age 74, IQR 54-83; 55.2% male). The risk of death increased independently with increasing age (>80 vs 18-49: HR 3.57, CI 2.54-5.02), frailty (CFS 8 vs 1-3: HR 3.03, CI 2.29-4.00) inflammation, renal disease, cardiovascular disease, and cancer, but not delirium. Age, frailty (CFS 7 vs 1-3: OR 7.00, CI 5.27-9.32), delirium, dementia, and mental health diagnoses were all associated with increased risk of higher care needs on discharge. The likelihood of adverse outcomes increased across all grades of CFS from 4 to 9.CONCLUSIONS: Age and frailty are independently associated with adverse outcomes in COVID-19. Risk of increased care needs was also increased in survivors of COVID-19 with frailty or older age

A Theoretical Investigation of the Emerging Standards for Web Services

Currently, standards for web services are being developed via three different initiatives (W3C, Semantic web services and ebXML). To the best of our knowledge, no theoretical perspectives underlie these standardization efforts. Without the benefit of a strong theoretical basis, the results, within and across these initiatives, have remained piecemeal. We suggest ‘Language-Action Theories’ as a plausible perspective that can effectively define, assess and refine web services standards. In this paper, we first investigate the existing initiatives to identify commonalities that point to theories of ‘Language-Action’ as an appropriate theoretical basis for web services standards. Next, we adapt work from these theories to develop a comprehensive reference framework for understanding web services standards. Finally, we use this reference framework to assess the three initiatives, and analyze the findings to provide insights for future development and refinement of web services standards