Academic literature on the topic 'Knowledge graph profiling'

Insect short neuropeptide F (sNPF), an ortholog of prolactin-releasing peptide of invertebrates, regulates diverse biological processes, including feeding, olfaction, locomotion, and sleep homeostasis in insects. However, its function is still unclear in an important model insect and agricultural pest, the pea aphid (Acyrthosiphon pisum). Here, we investigated short neuropeptide F (ApsNPF) and its receptor (ApsNPFR) in A. pisum. The sNPF gene contains three exons and two long introns. In addition, the genome contains a single sNPF receptor with seven transmembrane domains. Stage- and tissue-specific transcript profiling by qRT-PCR revealed that ApsNPF and ApsNPFR were mainly expressed in the central nervous system. The receptor was also detected in antennae, midgut, and integument. The highest expression levels were found in first instar nymphs compared to other developmental stages. Besides, the starvation-induced pattern indicated that the sNPF network depends on the nutritional state of the insect. An electrical penetration graph showed that probing time and phloem duration of A. pisum on broad bean plants decreased in response to dssNPF and dssNPFR in RNAi assays. sNPF silencing reduced the number of nymphs per female but not aphid survival. We believe that our results advance in-depth knowledge of the sNPF/sNPFR signaling cascade and its place in regulating feeding behavior in insects. In turn, it may contribute to the potential design of new strategies to control aphids, with a focus on the sNPF system.

Acute Myeloid Leukemia (AML) is a clonal disease of the hematopoietic system that initiated and sustained by self-renewing hematopoietic stem and progenitor cells (HSPC). Mutations in the de novo DNA methyltransferase 3A (DNMT3A) gene occur in approximately 25% of adult acute myeloid leukemias (AML). Although the mechanisms through which such mutations promote leukemogenesis remain unclear, we have previously shown that loss of the DNMT3A can inhibit normal hematopoietic differentiation (Challen, Nature Genetics, 2011), accounting for the emergence of DNMT3A-HSC clones as a predisposition to hematological malignancies (Yang, Cancer Cell, 2015). Therapies that selectively eliminate the initiating pre-leukemic population would greatly improve outcomes for affected patients. However, the identification as well as selective elimination of such a distinct population has been problematic because of the considerable overlap in gene expression profiles with bulk normal hematopoietic stem cells. Molecular targets during leukemia development have not been well elucidated due to lack of the real definitive markers, which is a significant knowledge gap and barrier for understanding clonal leukemogenesis and therapeutic applications. Single-cell RNA sequencing has emerged as a powerful tool to analyze new cell types, cellular heterogeneity and cell differentiation routes. This technique made important contributions to our understanding of hematopoietic stem and cancer cell heterogeneity and selective resistance of cancer cell subpopulations to molecularly targeted cancer therapies. To identify early events involved in pre-leukemic transformation, we have performed single-cell RNA-sequencing (scRNA-seq) in WT and Dnmt3a KO mice. Flow cytometry sorted wild-type and pre-leukemic Dnmt3a KO HSPC cells were captured using 10X genomics chromium platform. After genome mapping, dimensional reduction, and clustering using Cell ranger pipeline, we generated transcriptome data and integrated the data sets using Seurat. Approximately 8,000 cells from each group were sequenced, and each cell expressed 1800-4500 genes. Graph-based clustering analysis revealed 16 unique cell clusters in both WT and DNMT3A KO mice. Interestingly, when compared with WT mice, we observed a 10-fold expansion of a single cell cluster in Dnmt3a KO cells before the advent of overt leukemia. This cluster co-expresses several stem cell genes including well-known leukemic stem cell surface markers such as CD47, as well as several novel genes. Some of these novel genes, encode cell surface proteins such as Ly6c2 and Ly86. We further validated protein expressions in AML cell lines and primary AML blast. In conclusion, the discovery of novel cluster in DNMT3A KO mice, and the relative abundance of this cluster in pre-leukemic stage of DNMT3A KO mice indicates that they promote leukemogenesis and offers an opportunity to specifically target DNMT3A mutant pre-leukemic cells using T cell immunotherapy. Disclosures No relevant conflicts of interest to declare.

Application programs that exhibit strong locality of reference lead to minimized cache misses and better performance in different architectures. However, to maximize the performance of multithreaded applications running on emerging manycore systems, data movement in on-chip network should also be minimized. Unfortunately, the way many multithreaded programs are written does not lend itself well to minimal data movement. Motivated by this observation, in this paper, we target task-based programs (which cover a large set of available multithreaded programs), and propose a novel compiler-based approach that consists of four complementary steps. First, we partition the original tasks in the target application into sub-tasks and build a data reuse graph at a sub-task granularity. Second, based on the intensity of temporal and spatial data reuses among sub-tasks, we generate new tasks where each such (new) task includes a set of sub-tasks that exhibit high data reuse among them. Third, we assign the newly-generated tasks to cores in an architecture-aware fashion with the knowledge of data location. Finally, we re-schedule the execution order of sub-tasks within new tasks such that sub-tasks that belong to different tasks but share data among them are executed in close proximity in time. The detailed experiments show that, when targeting a state of the art manycore system, our proposed compiler-based approach improves the performance of 10 multithreaded programs by 23.4% on average, and it also outperforms two state-of-the-art data access optimizations for all the benchmarks tested. Our results also show that the proposed approach i) improves the performance of multiprogrammed workloads, and ii) generates results that are close to maximum savings that could be achieved with perfect profiling information. Overall, our experimental results emphasize the importance of dividing an original set of tasks of an application into sub-tasks and constructing new tasks from the resulting sub-tasks in a data movement- and locality-aware fashion.

Abstract Nowadays, with increasing open knowledge graphs (KGs) being published on the Web, users depend on open data portals and search engines to find KGs. However, existing systems provide search services and present results with only metadata while ignoring the contents of KGs, i.e., triples. It brings difficulty for users' comprehension and relevance judgement. To overcome the limitation of metadata, in this paper we propose a content-based search engine for open KGs named CKGSE. Our system provides keyword search, KG snippet generation, KG profiling and browsing, all based on KGs' detailed, informative contents rather than their brief, limited metadata. To evaluate its usability, we implement a prototype with Chinese KGs crawled from OpenKG.CN and report some preliminary results and findings.