Brinkthurston5745

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, including 4176 in the NRN for 2006 to 2012, 45 179 in VON for 2006 to 2012, and 25 969 in VON for 2013 to 2(48% female; mean [SD] gestational age, 24.1 [0.8] weeks). Model C statistics were 0.74 in the 2006-2012 validation cohort and 0.73 in the 2013-2016 validation cohort. With the use of decision curve analysis to compare the model with a gestational age-only approach to prognostication, the updated model showed a predictive advantage. The birth hospital contributed equally as much to prediction of survival as gestational age (20%) but less than the other factors combined (60%). Selleckchem PD-1/PD-L1 Inhibitor 3 Conclusions and Relevance An updated model using well-known factors to predict survival for extremely preterm infants performed moderately well when applied to large US cohorts. Because survival rates change over time, the model requires periodic updating. The hospital of birth contributed substantially to outcome prediction.Importance Firearms caused more than 500 pediatric fatalities in 2017-a 50% increase from 2009. Laws regulating firearms are one approach to reducing pediatric firearm fatalities. Objective To evaluate the association between state child access prevention (CAP) firearm laws and pediatric firearm fatalities. Design, Setting, and Participants A state-level, cross-sectional study of CAP firearm laws throughout the United States, 1991-2016, was conducted using negative binomial regression to analyze differences in state fatality rates in children aged 0 to 14 years. Data analysis was performed from November 21, 2018, to October 18, 2019. Exposures Implementation of 2 categories of state CAP firearm laws recklessness laws, which pertain to providing a firearm to a child, and negligence laws, which pertain to accessibility of a firearm within the home. Main Outcomes and Measures Rates of firearm fatalities across all intents and by specific intent (homicide, suicide, and unintentional) per 100 000 children aged 0 tated with relative reductions in firearm fatality rates in children aged 0 to 14 years. The most stringent negligence laws were associated with the largest reductions in unintentional firearm fatalities. Recklessness laws were not associated with reduced firearm fatality rates. The passage of negligence CAP laws may have the potential to reduce firearm fatalities in children.