Global warming will affect the maximum potential abundance of boreal plant species

Forecasting the impact of future global warming on biodiversity requires understanding how temperature limits the distribution of species. Here we rely on Liebig's Law of Minimum to estimate the effect of temperature on the maximum potential abundance that a species can attain at a certain location. We develop 95%‐quantile regressions to model the influence of effective temperature sum on the maximum potential abundance of 25 common understory plant species of Finland, along 868 nationwide plots sampled in 1985. Fifteen of these species showed a significant response to temperature sum that was consistent in temperature‐only models and in all‐predictors models, which also included cumulative precipitation, soil texture, soil fertility, tree species and stand maturity as predictors. For species with significant and consistent responses to temperature, we forecasted potential shifts in abundance for the period 2041–2070 under the IPCC A1B emission scenario using temperature‐only models. We predict major potential changes in abundance and average northward distribution shifts of 6–8 km yr−1. Our results emphasize inter‐specific differences in the impact of global warming on the understory layer of boreal forests. Species in all functional groups from dwarf shrubs, herbs and grasses to bryophytes and lichens showed significant responses to temperature, while temperature did not limit the abundance of 10 species. We discuss the interest of modelling the ‘maximum potential abundance’ to deal with the uncertainty in the predictions of realized abundances associated to the effect of environmental factors not accounted for and to dispersal limitations of species, among others. We believe this concept has a promising and unexplored potential to forecast the impact of specific drivers of global change under future scenarios.202

Automated stilbene UV fluorescence measurement

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Automatisoitu stilbeenien UV-fluoresenssin mittaus

201

Growing season precipitation in Finland under recent and projected climate

v2010okKA

Dynamics of Spreading of Chainlike Molecules with Asymmetric Surface Interactions

In this work we study the spreading dynamics of tiny liquid droplets on solid surfaces in the case where the ends of the molecules feel different interactions with respect to the surface. We consider a simple model of dimers and short chainlike molecules that cannot form chemical bonds with the surface. We use constant temperature Molecular Dynamics techniques to examine in detail the microscopic structure of the time dependent precursor film. We find that in some cases it can exhibit a high degree of local order that can persist even for flexible chains. Our model also reproduces the experimentally observed early and late-time spreading regimes where the radius of the film grows proportional to the square root of time. The ratios of the associated transport coefficients are in good overall agreement with experiments. Our density profiles are also in good agreement with measurements on the spreading of molecules on hydrophobic surfaces.Comment: 12 pages, LaTeX with APS macros, 21 figures available by contacting [email protected], to appear in Phys. Rev.

Optical measurement of Scots pine heartwood stilbene content

201

Männyn sydänpuun stilbeenipitoisuuden optinen mittaus

201

ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging.\nWe introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods.\nILoReg is available as an R package at https://bioconductor.org/packages/ILoReg.\nSupplementary data are available at Supplementary Information and Supplementary Files 1 and 2.\nMOTIVATION\nRESULTS\nAVAILABILITY\nSUPPLEMENTARY INFORMATIO

scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data

MotivationComputational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods.ResultsWe introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering.Availability and implementationscShaper is available as an R package at https://github.com/elolab/scshaper. The test data are available at https://doi.org/10.5281/zenodo.5734488.</p

Stable Iterative Variable Selection

Motivation: The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space. Results: Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE. Availability and implementation: The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package¼sivs. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.</p