Performance of recombinant inbred lines in Brussels sprouts (Brassica oleracea var. gemmifera)

Summary Performance of a random array of recombinant inbred lines derived by single seed descent from five different source populations of Brussels sprouts (Brassica oleracea var. gemmifera) is presented. A total of 2,356 lines were tested in trials during 1985 and 1986. Three of the source populations were derived from double crosses between F₁ hybrids. These hybrids show a considerable heterotic advantage over their inbred parents for the most important agronomic traits. The recombinant inbred lines performed, on average, less well than the parental inbred material, indicating that additive x additive genie interactions may make a significant contribution to the performance of current inbred material. Nevertheless, the very large variation among the recombinant inbred lines permitted many lines to be identified which outperformed the best parental inbred for all traits. Two lines outperformed the reference F₁ hybrid, Gower, for an index that included marketable yield and quality. Consideration was also given to the dangers of misinterpreting phenotypically based proportions. Accordingly, response equations were used to ascertain the real genetic progress that was made. Advance seemed small when compared with the large heterotic effect, which is consistent with the segregation of a large number of loci. The distribution of the recombinant inbred lines was compared to predictions made from early generation trials. There was broad agreement but significant discrepancies existed which, it is suggested, may arise from the effects of genotype-environment interactions.     

Estimation of the Spirometric Residual Volume (RV) by a Regression Built from Gower Distances

We estimate the Residual Volume, a spirometric parameter, by use of four continuous and four categorical variables. The estimation is done using distance-based regression, which allows to construct the predicting regression equation from mixed-type explanatory variables. The additionally introduced categorical variables improve essentially the goodness of fit of the regression equation.     

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences.In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.     

Estimation and Prediction of Generalized Growth Curve with Grouping Variances in AR(q) Dependence Structure

In this paper we consider maximum likelihood analysis of generalized growth curve model with the Box‐Cox transformation when the covariance matrix has AR(q) dependence structure with grouping variances. The covariance matrix under consideration is Σ = D _σ CD _σ where C is the correlation matrix with stationary autoregression process of order q, q < p and D _σ is a diagonal matrix with p elements divided into g(≤ p) groups, i.e., D _σ is a function of {σ₁, …, σ_g} and – 1 < ρ < 1 and σ_l, l = 1, …, g, are unknown. We consider both parameter estimation and prediction of future values. Results are illustrated with real and simulated data.     

Warming effects in the western Antarctic Peninsula ecosystem: the role of population dynamic models for explaining and predicting penguin trends

The western Antarctica Peninsula and Scotia Sea ecosystems appear to be driven by complex links between climatic variables, primary productivity, krill and Avian predators. There are several studies reporting statistical relationships between climate, krill and Penguin population size. The Adélie (Pygoscelis adeliae), Chinstrap (P. antarctica) and Gentoo (P. papua) penguins appear to be influenced by interannual variability in sea-ice extent and krill biomass. In this paper we developed simple conceptual models to decipher the role of climate and krill fluctuations on the population dynamics of these three Pygoscelis penguin species inhabiting the Antarctic Peninsula region. Our results suggest that the relevant processes underlying the population dynamics of these penguin species at King George Island (South Shetland Islands) are intra-specific competition and the combined effects of krill abundance and sea-ice cover. Our results using population theoretical models appear to support that climate change, specifically regional warming on the western Antarctic Peninsula, represents a major driver. At our study site, penguins showed species-specific responses to climate change. While Chinstrap penguins were only influenced by krill abundance, the contrasting population trends of Adélie and Gentoo penguins appear to be better explained by the “sea-ice hypothesis”. We think that proper population dynamic modeling and theory are essential for deciphering and proposing the ecological mechanisms underlying dynamics of these penguin populations.     

Our future as history

These are exciting and challenging times for the International Biometric Society and professional societies in general. These challenges require that we actively plan for our future and consider "Our Future as History"; that which lies ahead becomes the present and the present becomes history. To plan for the future I build on themes identified in my presidential address in Montréal, outlining our current status and initiatives, predicting and highlighting implications that call for expansion of our current activities and creation of new ones. These will ensure that we create our desired future and thereby produce a proud legacy.     

Machine learning helps identifying volume-confounding effects in radiomics

PurposeHighlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features.Methods841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features’ prognostic power and robustness.ResultsOver 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression.ConclusionsThe adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) – based methods for robust radiomics signatures development.