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Disturbances alter biodiversity via their specific characteristics, including severity and extent in the landscape, which act at different temporal and spatial scales. Biodiversity response to disturbance also depends on the community characteristics and habitat requirements of species. Untangling the mechanistic interplay of these factors has guided disturbance ecology for decades, generating mixed scientific evidence of biodiversity responses to disturbance. Understanding the impact of natural disturbances on biodiversity is increasingly important due to human‐induced changes in natural disturbance regimes. In many areas, major natural forest disturbances, such as wildfires, windstorms, and insect outbreaks, are becoming more frequent, intense, severe, and widespread due to climate change and land‐use change. Conversely, the suppression of natural disturbances threatens disturbance‐dependent biota. Using a meta‐analytic approach, we analysed a global data set (with most sampling concentrated in temperate and boreal secondary forests) of species assemblages of 26 taxonomic groups, including plants, animals, and fungi collected from forests affected by wildfires, windstorms, and insect outbreaks. The overall effect of natural disturbances on α‐diversity did not differ significantly from zero, but some taxonomic groups responded positively to disturbance, while others tended to respond negatively. Disturbance was beneficial for taxonomic groups preferring conditions associated with open canopies (e.g. hymenopterans and hoverflies), whereas ground‐dwelling groups and/or groups typically associated with shady conditions (e.g. epigeic lichens and mycorrhizal fungi) were more likely to be negatively impacted by disturbance. Across all taxonomic groups, the highest α‐diversity in disturbed forest patches occurred under moderate disturbance severity, i.e. with approximately 55% of trees killed by disturbance. We further extended our meta‐analysis by applying a unified diversity concept based on Hill numbers to estimate α‐diversity changes in different taxonomic groups across a gradient of disturbance severity measured at the stand scale and incorporating other disturbance features. We found that disturbance severity negatively affected diversity for Hill number q = 0 but not for q = 1 and q = 2, indicating that diversity–disturbance relationships are shaped by species relative abundances. Our synthesis of α‐diversity was extended by a synthesis of disturbance‐induced change in species assemblages, and revealed that disturbance changes the β‐diversity of multiple taxonomic groups, including some groups that were not affected at the α‐diversity level (birds and woody plants). Finally, we used mixed rarefaction/extrapolation to estimate biodiversity change as a function of the proportion of forests that were disturbed, i.e. the disturbance extent measured at the landscape scale. The comparison of intact and naturally disturbed forests revealed that both types of forests provide habitat for unique species assemblages, whereas species diversity in the mixture of disturbed and undisturbed forests peaked at intermediate values of disturbance extent in the simulated landscape. Hence, the relationship between α‐diversity and disturbance severity in disturbed forest stands was strikingly similar to the relationship between species richness and disturbance extent in a landscape consisting of both disturbed and undisturbed forest habitats. This result suggests that both moderate disturbance severity and moderate disturbance extent support the highest levels of biodiversity in contemporary forest landscapes.
Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7–8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.
The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.
Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304\(\pm\)0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of \(\pm\)0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
(2016)
Background
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.
Results
We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.
Conclusions
The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
Background
The aim of this study was to quantify the variability in spinal radiosurgery (SRS) planning practices between five international institutions, all member of the Elekta Spine Radiosurgery Research Consortium.
Methods
Four institutions provided one representative patient case each consisting of the medical history, CT and MR imaging. A step-wise planning approach was used where, after each planning step a consensus was generated that formed the basis for the next planning step. This allowed independent analysis of all planning steps of CT-MR image registration, GTV definition, CTV definition, PTV definition and SRS treatment planning. In addition, each institution generated one additional SRS plan for each case based on intra-institutional image registration and contouring, independent of consensus results.
Results
Averaged over the four cases, image registration variability ranged between translational 1.1 mm and 2.4 mm and rotational 1.1° and 2.0° in all three directions. GTV delineation variability was 1.5 mm in axial and 1.6 mm in longitudinal direction averaged for the four cases. CTV delineation variability was 0.8 mm in axial and 1.2 mm in longitudinal direction. CTV-to-PTV margins ranged between 0 mm and 2 mm according to institutional protocol. Delineation variability was 1 mm in axial directions for the spinal cord. Average PTV coverage for a single fraction18 Gy prescription was 87 ± 5 %; Dmin to the PTV was 7.5 ± 1.8 Gy averaged over all cases and institutions. Average Dmax to the PRV_SC (spinal cord + 1 mm) was 10.5 ± 1.6 Gy and the average Paddick conformity index was 0.69 ± 0.06.
Conclusions
Results of this study reflect the variability in current practice of spine radiosurgery in large and highly experienced academic centers. Despite close methodical agreement in the daily workflow, clinically significant variability in all steps of the treatment planning process was demonstrated. This may translate into differences in patient clinical outcome and highlights the need for consensus and established delineation and planning criteria.
While our knowledge about the roles of microbes and viruses in the ocean has increased tremendously due to recent advances in genomics and metagenomics, research on marine microbial eukaryotes and zooplankton has benefited much less from these new technologies because of their larger genomes, their enormous diversity, and largely unexplored physiologies. Here, we use a metatranscriptomics approach to capture expressed genes in open ocean Tara Oceans stations across four organismal size fractions. The individual sequence reads cluster into 116 million unigenes representing the largest reference collection of eukaryotic transcripts from any single biome. The catalog is used to unveil functions expressed by eukaryotic marine plankton, and to assess their functional biogeography. Almost half of the sequences have no similarity with known proteins, and a great number belong to new gene families with a restricted distribution in the ocean. Overall, the resource provides the foundations for exploring the roles of marine eukaryotes in ocean ecology and biogeochemistry.