In evolutionary biology, randomness has been perceived as a force that, in and of itself, is capable of inventing: mutation creates new genetic information at random across the genome which leads to phenotypic change, which is then subject to selection. However, in science in general and in computer science in particular, the widespread use of randomness takes a different form. Here, randomization allows for the breaking of pattern, as seen for example in its removal of biases (patterns) by random sampling or random assignment to conditions. Combined with various forms of evaluation, this breaking of pattern becomes an extraordinarily powerful tool, as also seen in many randomized algorithms in computer science. Here we show that this power of randomness is harnessed in nature by sex and recombination. In a finite population, and under the assumption of interactions between genetic variants, sex and recombination allow selection to test how well an allele will perform in a sample of combinations of interacting genetic partners drawn at random from all possible such combinations; consequently, even a small number of tests of genotypes such as takes place in a finite population favors alleles that will most likely perform well in a vast number of yet unrealized genetic combinations. This power of randomization is not manifest in asexual populations.
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http://dx.doi.org/10.1016/j.tpb.2018.11.005 | DOI Listing |
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFESC Heart Fail
January 2025
Department of Cardiology, Stavanger University Hospital, Stavanger, Norway.
Background: Cardiac myosin binding protein C (cMyC) is an emerging new biomarker of myocardial injury rising earlier and cleared faster than cardiac troponins. It has discriminatory power similar to high-sensitive troponins in diagnosing myocardial infarction in patients presenting with chest pain. It is also associated with outcome in patients with acute heart failure.
View Article and Find Full Text PDFPharm Stat
January 2025
Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Clinical trials (CTs) often suffer from small sample sizes due to limited budgets and patient enrollment challenges. Using historical data for the CT data analysis may boost statistical power and reduce the required sample size. Existing methods on borrowing information from historical data with right-censored outcomes did not consider matching between historical data and CT data to reduce the heterogeneity.
View Article and Find Full Text PDFPharm Stat
January 2025
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, United States.
Background: High dietary quality can protect against diet-related chronic diseases. In the United States, racial and ethnic minorities and those with lower incomes consistently exhibit lower dietary quality. Independently-owned restaurants are a common prepared food source in minority low-income communities, but there are significant knowledge gaps on how to work with these restaurants to offer healthy food, due to underlying and dynamic complexities associated with providing healthy food options.
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