Long-term methotrexate (MTX) treatment is known to cause MTX-associated lymphoproliferative disorder (MTX-LPD). A 58-year-old woman with psoriasis vulgaris and pityriasis rubra pilaris was treated with a combination of MTX and ustekinumab for 4 months when she developed generalized lymphadenopathy. The initial histopathological analysis indicated Hodgkin's lymphoma; however, assessing the patient's clinical history revealed the diagnosis of MTX-LPD. To our knowledge, this is the first case of a MTX-LPD after only 4 months of treatment.Stencil kernel is an important type of kernel used extensively in many application domains. Over the years, researchers have been studying the optimizations on parallelization, communication reuse, and computation reuse for various target platforms. However, challenges still exist, especially on the computation reuse problem for accelerators, due to the lack of complete design-space exploration and effective design-space pruning. In this paper, we present solutions to the above challenges for a wide range of stencil kernels (i.e., stencil with reduction operations), where the computation reuse patterns are extremely flexible due to the commutative and associative properties. We formally define the complete design space, based on which we present a provably optimal dynamic programming algorithm and a heuristic beam search algorithm that provides near-optimal solutions under an architecture-aware model. Experimental results show that for synthesizing stencil kernels to FPGAs, compared with state-of-the-art stencil compiler without computation reuse capability, our proposed algorithm can reduce the look-up table (LUT) and digital signal processor (DSP) usage by 58.1% and 54.6% on average respectively, which leads to an average speedup of 2.3× for compute-intensive kernels, outperforming the latest CPU/GPU results.With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.Background Transcriptional regulation of gene expression is crucial for the adaptation and survival of bacteria. Regulatory interactions are commonly modeled as Gene Regulatory Networks (GRNs) derived from experiments such as RNA-seq, microarray and ChIP-seq. While the reconstruction of GRNs is fundamental to decipher cellular function, even GRNs of economically important bacteria such as Corynebacterium glutamicum are incomplete. Materials and Methods Here, we analyzed the predictive power of GRNs if used as in silico models for gene expression and investigated the consistency of the C.  https://www.selleckchem.com/products/rxdx-106-cep-40783.html  glutamicum GRN with gene expression data from the GEO database. Results We assessed the consistency of the C. glutamicum GRN using real, as well as simulated, expression data and showed that GRNs alone cannot explain the expression profiles well. Conclusion Our results suggest that more sophisticated mechanisms such as a combination of transcriptional, post-transcriptional regulation and signaling should be taken into consideration when analyzing and constructing GRNs.Although homeless persons experience traumatic brain injury (TBI) frequently, little is known about the structural and functional brain changes in this group. We aimed to describe brain volume changes and related cognitive/motor deficits in homeless persons with or without TBI versus controls. Participants underwent T1-weighted magnetic resonance imaging (MRI), neuropsychological (NP) tests (the Grooved Pegboard Test [GPT]/Finger Tapping Test [FTT]), alcohol/drug use screens (the Alcohol Use Disorders Identification Test [AUDIT]/Drug Abuse Screening Test [DAST]), and questionnaires (the Brain Injury Screening Questionnaire [BISQ]/General Information Questionnaire [GIQ]) to determine TBI. Normalized volumes of brain substructures from MRI were derived from FreeSurfer. Comparisons were tested by Mann-Whitney U and Kruskal-Wallis rank sum tests. Leave-one-out cross-validation using random forest classifier was applied to determine the ability of predicting TBI. Diagnostic ability of this classifier was assessed er modeled on MRI, NP tests, and screening data combined. The MRI-data-based classifier was the best predictor of TBI within the homeless group (AUC 0.76, 95% CI 0.53-0.99). Normalized volumes of specific brain substructures were important indicators of TBI in homeless participants and they are important indicators of TBI in the state of homelessness itself. They may improve predictive ability of NP and screening tests in determining these outcomes.In this issue, Kaufmann and colleagues1 describe a population of immune cells that home to brain in multiple sclerosis (MS). Using an approved therapeutic, targeting α4β1integrin, they demonstrated how to trap these cells in blood, opening the possibility for their elimination before they cross into brain.Recent evidence suggest that the endothelial barrier function is enhanced by the mild activation of the unfolded protein response (UPR), which aims to suppress abnormal increases of endoplasmic reticulum stress. Heat shock protein 90 inhibitors and growth hormone releasing hormone antagonists exert the capacity to activate this multifaceted cellular mechanism (UPR). Thus, investigations on the signalling network involved in those events, may deliver exciting opportunities in diseases related to endothelial barrier dysfunction. The diverse spectrum of those pathologies include sepsis and Acute Respiratory Distress Syndrome (ARDS).