De novo identification of differentially methylated regions in the human genome

Background: The identification and characterisation of differentially methylated regions (DMRs) between phenotypes in the human genome is of prime interest in epigenetics. We present a novel method, DMRcate, that fits replicated methylation measurements from the Illumina HM450K BeadChip (or 450K array) spatially across the genome using a Gaussian kernel. DMRcate identifies and ranks the most differentially methylated regions across the genome based on tunable kernel smoothing of the differential methylation (DM) signal. The method is agnostic to both genomic annotation and local change in the direction of the DM signal, removes the bias incurred from irregularly spaced methylation sites, and assigns significance to each DMR called via comparison to a null model. Results: We show that, for both simulated and real data, the predictive performance of DMRcate is superior to those of Bumphunter and Probe Lasso, and commensurate with that of comb-p. For the real data, we validate all array-derived DMRs from the candidate methods on a suite of DMRs derived from whole-genome bisulfite sequencing called from the same DNA samples, using two separate phenotype comparisons. Conclusions: The agglomeration of genomically localised individual methylation sites into discrete DMRs is currently best served by a combination of DM-signal smoothing and subsequent threshold specification. The findings also suggest the design of the 450K array shows preference for CpG sites that are more likely to be differentially methylated, but its overall coverage does not adequately reflect the depth and complexity of methylation signatures afforded by sequencing. For the convenience of the research community we have created a user-friendly R software package called DMRcate, downloadable from Bioconductor and compatible with existing preprocessing packages, which allows others to apply the same DMR-finding method on 450K array data

Potential of the Julia programming language for high energy physics computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identifiedComment: 32 pages, 5 figures, 4 table

Modelling the dispersion of particle numbers in five European cities

We present an overview of the modelling of particle number concentrations (PNCs) in five major European cities, namely Helsinki, Oslo, London, Rotterdam, and Athens, in 2008. Novel emission inventories of particle numbers have been compiled both on urban and European scales. We used atmospheric dispersion modelling for PNCs in the five target cities and on a European scale, and evaluated the predicted results against available measured concentrations. In all the target cities, the concentrations of particle numbers (PNs) were mostly influenced by the emissions originating from local vehicular traffic. The influence of shipping and harbours was also significant for Helsinki, Oslo, Rotterdam, and Athens, but not for London. The influence of the aviation emissions in Athens was also notable. The regional background concentrations were clearly lower than the contributions originating from urban sources in Helsinki, Oslo, and Athens. The regional background was also lower than urban contributions in traffic environments in London, but higher or approximately equal to urban contributions in Rotterdam. It was numerically evaluated that the influence of coagulation and dry deposition on the predicted PNCs was substantial for the urban background in Oslo. The predicted and measured annual average PNCs in four cities agreed within approximatelyPeer reviewe

Multidisciplinary assessment and prediction tools addressing coastal vulnerability to erosion and sea level rise. Lesson learnt from the RITMARE Project

Natural processes and human activities are strongly connected, and sometimes con icting, in the evolution of coastal and transitional environments. The strong anthropic pressure on coastal regions, together with the e ects of a changing climate, demands nowadays more pressingly for e cient tools to characterise and predict the behaviour of such systems in order to de ne appropriate response strategies. This requires a deep understanding of the connections among di erent drivers and di erent scales, a multidisciplinary challenge in which heterogeneous data, approaches and scales need to be framed within a consistent dynamical description. To this aim, a speci c research line was dedicated to \u201cCoastal Vulnerability to Erosion and Sea Level Rise\u201d within the RITMARE Project, supported by the Italian Ministry of University and Research with the purpose of integrating the Italian Marine community in shared research elds in the period 2012-2017. The activities carried out in this framework have been moving along interconnected branches tackling the themes related with sea level rise, ocean modelling, and geomorphological assessment in present conditions and in di erent climate change scenarios, with an eye on the exploitation of marine sand as a strategic resource. In this contribution we review the main outcomes of this multidisciplinary and coordinated research. Besides discussing the advances and the possibilities from state-of-the art technologies and methodologies, we point out that a coordinated use of the described tools should be promoted in the design of survey and monitoring activities, as well as in the exploitation of already collected data. Expected outcomes of this strategy include the implementation of improved policies and infrastructures for coastal protection, anked by reliable short-term forecasting systems and e cient rapid response protocols, in the framework of an integrated coastal planning at the multi-decadal scale

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Hemorrhage associated with hepatic artery pseudoaneurysms after regional chemotherapy with floxuridine: case report

Pseudoaneurysms of the hepatic artery are a rare complication in patients with primary or secondary liver tumors treated with intra-arterial chemotherapy. We present two patients who developed this complication after placement of a catheter system into the gastroduodenal artery and initiation of regional chemotherapy with floxuridine. Diagnosis was made after symptomatic bleeding occurred, necessitating emergency angiography with coil embolization. Pseudoaneurysms usually occur after mechanical damage of the vessel wall, but the chemical toxicity of floxuridine may add to the development of vascular impairment