The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Use of Global Drag Rise Boundaries to Investigate Ill-Posed Transonic Airfoil Optimization

This paper presents a series of transonic airfoils, designed using differing optimization approaches, which are evaluated over a wide range of operating conditions using global aerodynamic performance maps. Global drag rise boundaries, which are identified, modelled and directly optimized during design, include drag divergence and onset of wave drag. The AIAA ADODG Case 2 airfoil optimization case is used to compare the results of the new global performance design approach with conventional multi-point optimization. The impact of alternative design formulations is presented in terms of both global performance maps and selected drag rise characteristics around the Case 2 design condition. In particular, the trade-off between drag divergence and the preceding onset of wave drag is discussed. The new approach addresses the issue of early excessive drag creep, which is typically encountered for optimization focused on a narrow range of operating conditions. The study provides some further insights into how a well posed optimization formulation for transonic airfoil design can potentially be established

Bag‐of‐features for image memorability evaluation

Image memorability represents the degree to which images are remembered or forgotten after a period of time. Studying image memorability in computer vision is the task of finding special characteristics in memorable images, in order to develop a representative model of this type of images. Several approaches have been realised to examine features that can affect image memorability. In this study, the authors use bag‐of‐features as another kind of visual feature descriptor to assess image memorability. The authors’ method based on bag‐of‐visual‐words (BoVWs) technique involves four main steps. First, the authors extract local image features from regions/points of interest which are automatically detected. Then, they encode these local features by mapping them to a created visual vocabulary. Later, the authors apply features pooling and normalisation techniques to obtain image BoVW representation. Finally, the authors use this representation to examine image memorability as a problem of classification. They present different implementation choices for each step and compare reached results. The authors’ method performs best significant results in comparison with other approaches found in literature

Change in Hippocampus Volume as a Function of Radiation Dose: Early Results From a Prospective Trial with Standardized Imaging and Morphometric Evaluation

Pediatric brain tumor patients are at high risk of developing neurocognitive deficits following treatment. The perihippocampal subventricular zone contains a niche of radiosensitive neural progenitor cells linked to memory development, and radiotherapy (RT) to this brain substructure has been associated with neurocognitive impairment in randomized trials. In this prospective study, 3D volumetric MRIs were obtained in pediatric brain tumor patients at baseline and during follow-up to measure volumetric changes in multiple brain substructures along with neurocognitive, endocrine, quality-of-life, and exploratory biomarker assessments. In this planned interim analysis, we model early outcomes for change in hippocampal volume at 6 months following RT. As of 2/26/2021, 47 patients had enrolled on this prospective study and 36 had completed their 6-month follow-up assessments after fractionated intensity-modulated proton therapy (IMPT) for primary brain and skull base tumors. Left and right hippocampus volumes were independently measured on T1 sagittal precontrast MRI using automated software at baseline and 6-months after RT and were compared to manual physician contours. The relationship between mean hippocampus dose and change in volume was assessed by Pearson's correlation coefficient. The effect of mean hippocampus dose on change in volume was assessed for mean doses < 10 Gy and ≥10 Gy by t-test. A linear mixed-effects (LME) model was applied to evaluate other predictors associated with change in hippocampus volume, assuming random effects of subjects. Potential factors considered were age, gender, tumor location, focal vs. whole brain RT, prior craniotomy, and chemotherapy. Mean hippocampus dose was strongly correlated with change in hippocampus volume at 6 months following RT (r = −0.727, 95% CI [-0.820 -0.596], P < 0.001). Hippocampus volumes and observed changes over time were similar between the software and physician-delineated contours. A significant reduction in hippocampus volume was observed for mean doses ≥10 Gy (mean Δ -10.8% ± 5.5%, P < 0.001), while no significant change in volume was observed for mean doses < 10 Gy (mean Δ +0.7% ± 3.9%). The LME model demonstrated that only mean hippocampus dose was significantly associated with change in hippocampus volume (P < 0.001). The final model predicted a -3.4% change in hippocampus volume for every 10 Gy increase in mean dose. Regression diagnostics showed no evidence of lack-of-fit and no patterns in residuals. Change in hippocampus volume was correlated with hippocampus mean dose at 6 months following RT. A significant reduction in hippocampus volume was observed for mean doses ≥10 Gy compared to no significant change at mean doses < 10 Gy. Future analyses from this study will assess volume change in this and other brain substructures over time as a function of radiation dose and will correlate these findings with measured neurocognitive and other late effects