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Chronic neutrophilic leukemia (CNL), atypical chronic myeloid leukemia (aCML), and myelodysplastic/myeloproliferative neoplasms, unclassifiable (MDS/MPN-U) are a group of rare and heterogeneous myeloid disorders. There is strong morphologic resemblance among these distinct diagnostic entities as well as a lack of specific molecular markers and limited understanding of disease pathogenesis, which has made diagnosis challenging in certain cases. The treatment has remained empirical, resulting in dismal outcomes. We, therefore, performed whole-exome and RNA sequencing of these rare hematologic malignancies and present the most complete survey of the genomic landscape of these diseases to date. We observed a diversity of combinatorial mutational patterns that generally do not cluster within any one diagnosis. Gene expression analysis reveals enrichment, but not cosegregation, of clinical and genetic disease features with transcriptional clusters. In conclusion, these groups of diseases represent a continuum of related diseases rather than discrete diagnostic entities.

HPC offers tremendous potential to process large amounts of data often termed as big data. Distributing data efficiently and leveraging specialised hardware (e.g., accelerators) are critical in order to best utilise HPC platforms constituting of heterogeneous and distributed systems. In this paper, we develop a portable, high-level paradigm for such systems to run big data applications, more specifically, graph analytics applications popular in the big data and machine learning communities. Using our paradigm, we accelerate three real-world, compute and data intensive, graph analytics applications: a function call graph similarity application, a triangle enumeration subroutine, and a graph assaying application. Our paradigm utilises the MapReduce framework, Apache Spark, in conjunction with CUDA and simultaneously takes advantage of automatic data distribution and accelerator on each node of the system. We demonstrate scalability and parameter space exploration and offer a portable solution to leverage almost any legacy, current, or next-generation HPC or cloud-based system.

Transcriptional differences between heterogeneous stock mice and high drinking‐in‐the‐dark selected mouse lines have previously been described based on microarray technology coupled with network‐based analysis. The network changes were reproducible in 2 independent selections and largely confined to 2 distinct network modules; in contrast, differential expression appeared more specific to each selected line. This study extends these results by utilizing RNA‐Seq technology, allowing evaluation of the relationship between genetic risk and transcription of noncoding RNA(ncRNA); we additionally evaluate sex‐specific transcriptional effects of selection. Naïve mice (N= 24/group and sex) were utilized for gene expression analysis in the ventral striatum; the transcriptome was sequenced with the Illumina HiSeq platform. Differential gene expression and the weighted gene co‐expression network analysis were implemented largely as described elsewhere, resulting in the identification of genes that change expression level or (co)variance structure. Across both sexes, we detect selection effects on the extracellular matrix and synaptic signaling, although the identity of individual genes varies. A majority of nc RNAs cluster in a single module of relatively low density in both the male and female network. The most strongly differentially expressed transcript in both sexes was Gm22513,a small nuclear RNA with unknown function. Associated with selection, we also found a number of network hubs that change edge strength and connectivity. At the individual gene level, there are many sex‐specific effects; however, at the annotation level, results are more concordant. In addition to demonstrating sex‐specific effects of selection on the transcriptome, the data point to the involvement of extracellular matrix genes as being associated with the binge drinking phenotype.

NO has been implicated in the pathogenesis of septic shock. However, the role of NO synthase 3 (NOS3) during sepsis remains incompletely understood. Here, we examined the impact of NOS3 deficiency on systemic inflammation and myocardial dysfunction during peritonitis-induced polymicrobial sepsis. Severe polymicrobial sepsis was induced by colon ascendens stent peritonitis (CASP) in wild-type (WT) and NOS3-deficient (NOS3KO) mice. NOS3KO mice exhibited shorter survival time than did WT mice after CASP. NOS3 deficiency worsened systemic inflammation assessed by the expression of inflammatory cytokines in the lung, liver, and heart. Colon ascendens stent peritonitis markedly increased the number of leukocyte infiltrating the liver and heart in NOS3KO but not in WT mice. The exaggerated systemic inflammation in septic NOS3KO mice was associated with more marked myocardial dysfunction than in WT mice 22 h after CASP. The detrimental effects of NOS3 deficiency on myocardial function after CASP seem to be caused by impaired Ca2+handling of cardiomyocytes. The impaired Ca2+handling of cardiomyocytes isolated from NOS3KO mice subjected to CASP was associated with depressed mitochondrial ATP production, a determinant of the Ca2+cycling capacity of sarcoplasmic reticulum Ca2+-ATPase. The NOS3 deficiency-induced impairment of the ability of mitochondria to produce ATP after CASP was at least in part attributable to reduction in mitochondrial respiratory chain complex I activity. These observations suggest that NOS3 protects against systemic inflammation and myocardial dysfunction after peritonitis-induced polymicrobial sepsis in mice.