Hassing Carter (lightnail3)

Empirical experiments have been performed on a standard benchmark set of both DNA sequences and protein sequences. The experimental results demonstrate that our model and algorithm outperform the related leading algorithms, especially for large-scale MLCS problems. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.The progression of cancer is accompanied by the acquisition of stemness features. Many stemness evaluation methods based on transcriptional profiles have been presented to reveal the relationship between stemness and cancer. However, instead of absolute stemness index values-the values with certain range-these methods gave the values without range, which makes them unable to intuitively evaluate the stemness. Besides, these indices were based on the absolute expression values of genes, which were found to be seriously influenced by batch effects and the composition of samples in the dataset. Recently, we have showed that the signatures based on the relative expression orderings (REOs) of gene pairs within a sample were highly robust against these factors, which makes that the REO-based signatures have been stably applied in the evaluations of the continuous scores with certain range. Here, we provided an absolute REO-based stemness index to evaluate the stemness. We found that this stemness index had higher correlation with the culture time of the differentiated stem cells than the previous stemness index. When applied to the cancer and normal tissue samples, the stemness index showed its significant difference between cancers and normal tissues and its ability to reveal the intratumor heterogeneity at stemness level. Importantly, higher stemness index was associated with poorer prognosis and greater oncogenic dedifferentiation reflected by histological grade. All results showed the capability of the REO-based stemness index to assist the assignment of tumor grade and its potential therapeutic and diagnostic implications. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.SUMMARY Methods for quantifying the imbalance in CpG methylation between alleles genome-wide have been described but their algorithmic time complexity is quadratic and their practical use requires painstaking attention to infrastructure choice, implementation, and execution. To solve this problem, we developed CloudASM, a scalable, ultra-efficient, turn-key, portable pipeline on Google Cloud Computing (GCP) that uses a novel pipeline manager and GCP's serverless enterprise data warehouse. AVAILABILITY AND IMPLEMENTATION CloudASM is freely available in the GitHub repository https//github.com/TyckoLab/CloudASM and a sample dataset and its results are also freely available at https//console.cloud.google.com/storage/browser/cloudasm. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Importance The Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network (NRN) extremely preterm birth outcome model is widely used for prognostication by practitioners caring for families expecting extremely preterm birth. The model provides information on mean outcomes from 1998 to 2003 and does not account for substantial variation in outcomes among US hospitals. Objective To update and validate the NRN extremely preterm birth outcome model for most extremely preterm infants in the United States. Design, Setting, and Participants This prognostic study included 3 observational cohorts from January 1, 2006, to December 31, 2016, at 19 US centers in the NRN (derivation cohort) and 637 US centers in Vermont Oxford Network (VON) (validation cohorts). Actively treated infants born at 22 weeks' 0 days' to 25 weeks' 6 days' gestation and weighing 401 to 1000 g, i