A Greedy Hypercube-Labeling Algorithm

Due to its attractive topological properties, the hypercube multiprocessor has emerged as one of the architectures of choice when it comes to implementing a large number of computational problems. In many such applications, Gray-code labelings of the hypercube are a crucial prerequisite for obtaining efficient algorithms. We propose a greedy algorithm that, given an n-dimensional hypercube H with N=22 nodes, returns a Gray-code labeling of H, that is, a labeling of the nodes with binary strings of length n such that two nodes are neighbors in the hypercube if, and only if, their labels differ in exactly one bit. Our algorithm is conceptually very simple and runs in O(N log N) time being, therefore, optimal. As it turns out, with a few modifications our labeling algorithm can be used to recognize hypercubes as well

Time-Optimal Tree Computations on Sparse Meshes

The main goal of this work is to fathom the suitability of the mesh with multiple broadcasting architecture (MMB) for some tree-related computations. We view our contribution at two levels: on the one hand, we exhibit time lower bounds for a number of tree-related problems on the MMB. On the other hand, we show that these lower bounds are tight by exhibiting time-optimal tree algorithms on the MMB. Specifically, we show that the task of encoding and/or decoding n-node binary and ordered trees cannot be solved faster than Ω(log n) time even if the MMB has an infinite number of processors. We then go on to show that this lower bound is tight. We also show that the task of reconstructing n-node binary trees and ordered trees from their traversais can be performed in O(1) time on the same architecture. Our algorithms rely on novel time-optimal algorithms on sequences of parentheses that we also develop

Cronobacter sakazakii infection alters serotonin transporter and improved fear memory retention in the rat

It is well established that Cronobacter sakazakii infection cause septicemia, necrotizingenterocolitis (NEC) and meningitis. In the present study, we tested whether the C. sakazakii infection alter the learning and memory through serotonin transporter (SERT). To investigate the possible effect on SERT, on postnatal day (PND)-15, wistar rat pups were administered with single dose of C. sakazakii culture (Infected group: IF; 107 CFU) or 100μL of Luria-Bertani broth (LB; Medium Control: MC) or without any treatment (Naïve control: NC). All the individuals were subjected to passive avoidance test on PND-30 to test their fear memory. We show that single dose of C. sakazakii infection improved fear memory retention. Subsequently, we show that C. sakazakii infection induced the activation of Toll-like receptor-3 (TLR-3) and heat-shock proteins-90 (Hsp-90). On the other hand, level of serotonin (5-HT) and SERT protein was down-regulated. Furthermore, we show that C. sakazakii infection up-regulate microRNA (miR)-16 expression. The observed results highlight that C. sakazakii infections was responsible for improved fear memory retention and may have reduced the level of SERT protein, which is possibly associated with the interaction of up-regulated Hsp-90 with SERT protein or miR-16 with SERT mRNA. Taken together, observed results suggest that C. sakazakkii infection alter the fear memory possibly through SERT. Hence, this model may be effective to test the C. sakazakii infection induced changes in synaptic plasticity through SERT and effect of other pharmacological agents against pathogen induced memory disorder

Time- and Cost-Optimal Parallel Algorithms for the Dominance and Visibility Graphs

The compaction step of integrated circuit design motivates associating several kinds of graphs with a collection of non-overlapping rectangles in the plane. These graphs are intended to capture various visibility relations amongst the rectangles in the collection. The contribution of this paper is to propose time- and cost-optimal algorithms to construct two such graphs, namely, the dominance graph (DG, for short) and the visibility graph (VG, for short). Specifically, we show that with a collection of n non-overlapping rectangles as input, both these structures can be constructed in θ (log n) time using n processors in the CREW model

Genome-wide miRNAprofiling of mantle cell lymphoma reveals a distinct subgroup with poor prognosis

miRNA deregulation has been implicated in the pathogenesis of mantle cell lymphoma (MCL). Using a high-throughput quantitative real-time PCR platform, we performed miRNA profiling on cyclin D1–positive MCL (n = 30) and cyclin D1–negative MCL (n =7) and compared them with small lymphocytic leukemia/ lymphoma (n =12), aggressive B-cell lymphomas (n =138), normal B-cell subsets, and stromal cells.We identified a 19-miRNA classifier that included 6 up-regulated miRNAs and 13 down regulated miRNA that was able to distinguish MCL from other aggressive lymphomas. Some of the up-regulated miRNAs are highly expressed in naive B cells. This miRNAclassifier showed consistent results in formalinfixed paraffin-embedded tissues and was able to distinguish cyclin D1–negative MCL from other lymphomas. A 26-miRNA classifier could distinguish MCL from small lymphocytic leukemia/lymphoma, dominated by 23 up-regulated miRNAs in MCL. Unsupervised hierarchical clustering of MCL patients demonstrated a cluster characterized by high expression of miRNAs from the polycistronic miR17-92 cluster and its paralogs, miR-106a-363 and miR-106b-25, and associated with high proliferation gene signature. The other clusters showed enrichment of stroma-associated miRNAs, and also had higher expression of stroma-associated genes. Our clinical outcome analysis in the present study suggested that miRNAs can serve as prognosticators

Brain Derived Neurotrophic Factor (BDNF) Expression Is Regulated by MicroRNAs miR-26a and miR-26b Allele-Specific Binding

Brain-derived neurotrophic factor (BDNF) is a neurotrophin that plays an essential role in neuronal development and plasticity. MicroRNA (miRNAs) are small non-coding RNAs of about 22-nucleotides in length regulating gene expression at post-transcriptional level. In this study we explore the role of miRNAs as post-transcriptional inhibitors of BDNF and the effect of 3′UTR sequence variations on miRNAs binding capacity. Using an in silico approach we identified a group of miRNAs putatively regulating BDNF expression and binding to BDNF 3′UTR polymorphic sequences. Luciferase assays demonstrated that these miRNAs (miR-26a1/2 and miR-26b) downregulates BDNF expression and that the presence of the variant alleles of two single nucleotide polymorphisms (rs11030100 and rs11030099) mapping in BDNF 3′UTR specifically abrogates miRNAs targeting. Furthermore we found a high linkage disequilibrium rate between rs11030100, rs11030099 and the non-synonymous coding variant rs6265 (Val66Met), which modulates BDNF mRNA localization and protein intracellular trafficking. Such observation led to hypothesize that miR-26s mediated regulation could extend to rs6265 leading to an allelic imbalance with potentially functional effects, such as peptide's localization and activity-dependent secretion. Since rs6265 has been previously implicated in various neuropsychiatric disorders, we evaluated the distribution of rs11030100, rs11030099 and rs6265 both in a control and schizophrenic group, but no significant difference in allele frequencies emerged. In conclusion, in the present study we identified two novel miRNAs regulating BDNF expression and the first BDNF 3′UTR functional variants altering miRNAs-BDNF binding