a versatile tool for the analysis and integrative visualization of DNA copy number variants

Background The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others. Results We developed GenomeCAT, a standalone Java application for the analysis and integrative visualization of CNVs. GenomeCAT is composed of three modules dedicated to the inspection of single cases, comparative analysis of multidimensional data and group comparisons aiming at the identification of recurrent aberrations in patients sharing the same phenotype, respectively. Its flexible import options ease the comparative analysis of own results derived from microarray or NGS platforms with data from literature or public depositories. Multidimensional data obtained from different experiment types can be merged into a common data matrix to enable common visualization and analysis. All results are stored in the integrated MySQL database, but can also be exported as tab delimited files for further statistical calculations in external programs. Conclusions GenomeCAT offers a broad spectrum of visualization and analysis tools that assist in the evaluation of CNVs in the context of other experiment data and annotations. The use of GenomeCAT does not require any specialized computer skills. The various R packages implemented for data analysis are fully integrated into GenomeCATs graphical user interface and the installation process is supported by a wizard. The flexibility in terms of data import and export in combination with the ability to create a common data matrix makes the program also well suited as an interface between genomic data from heterogeneous sources and external software tools. Due to the modular architecture the functionality of GenomeCAT can be easily extended by further R packages or customized plug-ins to meet future requirements

Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic

Warming induced shifts in tundra vegetation composition and structure, including circumpolar expansion of shrubs, modifies ecosystem structure and functioning with potentially global consequences due to feedback mechanisms between vegetation and climate. Satellite-derived vegetation indices indicate widespread greening of the surface, often associated with regional evidence of shrub expansion obtained from long-term ecological monitoring and repeated orthophotos. However, explicitly quantifying shrub expansion across large scales using satellite observations requires characterising the fine-scale mosaic of Arctic vegetation types beyond index-based approaches. Although previous studies have illustrated the potential of estimating fractional cover of various Plant Functional Types (PFTs) from satellite imagery, limited availability of reference data across space and time has constrained deriving fraction cover time series capable of detecting shrub expansion. We applied regression-based unmixing using synthetic training data to build multitemporal machine learning models in order to estimate fractional cover of shrubs and other surface components in the Mackenzie Delta Region for six time intervals between 1984 and 2020. We trained Kernel Ridge Regression (KRR) and Random Forest Regression (RFR) models using Landsat-derived spectral-temporal-metrics and synthetic training data generated from pure class spectra obtained directly from the imagery. Independent validation using very-high-resolution imagery suggested that KRR outperforms RFR, estimating shrub cover with a MAE of 10.6 and remaining surface components with MAEs between 3.0 and 11.2. Canopy-forming shrubs were well modelled across all cover densities, coniferous tree cover tended to be overestimated and differentiating between herbaceous and lichen cover was challenging. Shrub cover expanded by on average + 2.2 per decade for the entire study area and + 4.2 per decade within the low Arctic tundra, while relative changes were strongest in the northernmost regions. In conjunction with shrub expansion, we observed herbaceous plant and lichen cover decline. Our results corroborate the perception of the replacement and homogenisation of Arctic vegetation communities facilitated by the competitive advantage of shrub species under a warming climate. The proposed method allows for multidecadal quantitative estimates of fractional cover at 30 m resolution, initiating new opportunities for mapping past and present fractional cover of tundra PFTs and can help advance our understanding of Arctic shrub expansion within the vast and heterogeneous tundra biome

Organic Farming, Climate Change Mitigation and Beyond. Reducing the environmental impacts of eu agriculture

Sustainably feeding the growing world population and preventing dangerous climate change are two of the major challenges facing society today. While there is a growing understanding of the complexity of the links between these challenges and of the global degradation of the environment, the contribution of food and farming to climate change mitigation is all too often looked at from the single perspective of greenhouse gas (GHG) emissions per hectare or kilogram of product. This narrow view fails to account for the vast array of ways that food and farming contribute to climate change, as well as the destructive effects of industrial agriculture on soils, biodiversity and the natural resources on which we depend for food production. The impact of agriculture practices, food wastage, and diets must all be evaluated if we are to understand how food and farming can positively contribute to climate change mitigation and adaptation, while simultaneously providing food security. The issue about what is produced to meet human needs, what is produced for intermediate production purposes (e.g. livestock feed) and what is wasted between the field and the kitchen, needs to be part of the discussion. To provide healthy food in a sustainable way, we need to transform the food & farming system and transition to agriculture and food production that can adapt to unavoidable climate change, preserve our natural heritage such as biodiversity, sustains the quality of our soils, and improve the livelihood of farmers. This report aims to provide a comprehensive discussion of these varied, yet interlinked, issues

Systematic analysis of gene expression in human brains before and after death.

BACKGROUND: Numerous studies have employed microarray techniques to study changes in gene expression in connection with human disease, aging and evolution. The vast majority of human samples available for research are obtained from deceased individuals. This raises questions about how well gene expression patterns in such samples reflect those of living individuals. RESULTS: Here, we compare gene expression patterns in two human brain regions in postmortem samples and in material collected during surgical intervention. We find that death induces significant expression changes in more than 10% of all expressed genes. These changes are non-randomly distributed with respect to their function. Moreover, we observe similar expression changes due to death in two distinct brain regions. Consequently, the pattern of gene expression differences between the two brain regions is largely unaffected by death, although the magnitude of differences is reduced by 50% in postmortem samples. Furthermore, death-induced changes do not contribute significantly to gene expression variation among postmortem human brain samples. CONCLUSION: We conclude that postmortem human brain samples are suitable for investigating gene expression patterns in humans, but that caution is warranted in interpreting results for individual genes.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Treatment of invasive fungal infections in clinical practice: a multi-centre survey on customary dosing, treatment indications, efficacy and safety of voriconazole

Invasive fungal infections are frequent and often deadly complications in patients with malignant hematological diseases. Voriconazole is a third generation triazole antifungal with broad activity against most clinically relevant fungal pathogens. Clinical practice often deviates from insights gained from controlled randomized trials. We conducted a multi-centre survey to evaluate efficacy, safety, treatment indications and dosing of voriconazole outside clinical trials. Patients receiving voriconazole were documented via electronic data capturing. An analysis was conducted after submission of 100 episodes from September 2004 to November 2005. Voriconazole was administered for suspected or proven invasive fungal infection (IFI) (57%), as empirical treatment in patients with fever of unknown origin (21%) and secondary (19%) as well as primary (3%) prophylaxis of IFI. Investigators’ assessment of fungal infection often diverted from EORTC/MSG 2002 criteria. A favorable response was reported in 61.4% for suspected or proven IFI and 52.4% for empirical treatment. Mortality was 15%, 26.7% of which was attributable to IFI. Breakthrough fungal infections occurred in four (21.1%) patients with voriconazole as secondary prophylaxis. Toxicity and adverse events comprised elevated liver enzymes and visual disturbances. Although indications frequently deviated from clinical evidence and legal approval, voriconazole showed efficacy and safety, comparable to major controlled clinical trials. Data from this survey demonstrate the difficulty of putting drugs to their approved use in IFI

Genome-Wide Analysis of Interchromosomal Interaction Probabilities Reveals Chained Translocations and Overrepresentation of Translocation Breakpoints in Genes in a Cutaneous T-Cell Lymphoma Cell Line

In classical models of tumorigenesis, the accumulation of tumor promoting chromosomal aberrations is described as a gradual process. Next-generation sequencing-based methods have recently revealed complex patterns of chromosomal aberrations, which are beyond explanation by these classical models of karyotypic evolution of tumor genomes. Thus, the term chromothripsis has been introduced to describe a phenomenon, where temporarily and spatially confined genomic instability results in dramatic chromosomal rearrangements limited to segments of one or a few chromosomes. Simultaneously arising and misrepaired DNA double-strand breaks are also the cause of another phenomenon called chromoplexy, which is characterized by the presence of chained translocations and interlinking deletion bridges involving several chromosomes. In this study, we demonstrate the genome-wide identification of chromosomal translocations based on the analysis of translocation-associated changes in spatial proximities of chromosome territories on the example of the cutaneous T-cell lymphoma cell line Se-Ax. We have used alterations of intra- and interchromosomal interaction probabilities as detected by genome-wide chromosome conformation capture (Hi-C) to infer the presence of translocations and to fine-map their breakpoints. The outcome of this analysis was subsequently compared to datasets on DNA copy number alterations and gene expression. The presence of chained translocations within the Se-Ax genome, partly connected by intervening deletion bridges, indicates a role of chromoplexy in the etiology of this cutaneous T-cell lymphoma. Notably, translocation breakpoints were significantly overrepresented in genes, which highlight gene-associated biological processes like transcription or other gene characteristics as a possible cause of the observed complex rearrangements. Given the relevance of chromosomal aberrations for basic and translational research, genome-wide high-resolution analysis of structural chromosomal aberrations will gain increasing importance