Noise-Constrained Performance Optimization by Simultaneous Gate and Wire Sizing Based on Lagrangian Relaxation

Noise, as well as area, delay, and power, is one of the most important concerns in the design of deep sub-micron ICs. Currently existing algorithms can not handle simultaneous switching conditions of signals for noise minimization. In this paper, we model not only physical coupling capacitance, but also simultaneous switching behavior for noise optimization. Based on Lagrangian relaxation, we present an algorithm that can optimally solve the simultaneous noise, area, delay, and power optimization problem by sizing circuit components. Our algorithm, with linear memory requirement overall and linear runtime per iteration, is very effective and efficient. For example, for a circuit of 6144 wires and 3512 gates, our algorithm solves the simultaneous optimization problem using only 2.1 MB memory and 47 minute runtime to achieve the precision of within 1% error on a SUN UltraSPARC-I workstation

Packing internally disjoint Steiner paths of data center networks

Let $S\subseteq V(G)$ and $\pi_{G}(S)$ denote the maximum number $t$ of edge-disjoint paths $P_{1},P_{2},\ldots,P_{t}$ in a graph $G$ such that $V(P_{i})\cap V(P_{j})=S$ for any $i,j\in\{1,2,\ldots,t\}$ and $i\neq j$. If $S=V(G)$, then $\pi_{G}(S)$ is the maximum number of edge-disjoint spanning paths in $G$. It is proved [Graphs Combin., 37 (2021) 2521-2533] that deciding whether $\pi_G(S)\geq r$ is NP-complete for a given $S\subseteq V(G)$. For an integer $r$ with $2\leq r\leq n$, the $r$-path connectivity of a graph $G$ is defined as $\pi_{r}(G)=$min$\{\pi_{G}(S)|S\subseteq V(G)$ and $|S|=r\}$, which is a generalization of tree connectivity. In this paper, we study the $3$-path connectivity of the $k$-dimensional data center network with $n$-port switches $D_{k,n}$ which has significate role in the cloud computing, and prove that $\pi_{3}(D_{k,n})=\lfloor\frac{2n+3k}{4}\rfloor$ with $k\geq 1$ and $n\geq 6$

Anti-Cancer Effects of Protein Extracts from Calvatia lilacina, Pleurotus ostreatus and Volvariella volvacea

Calvatia lilacina (CL), Pleurotus ostreatus (PO) and Volvariella volvacea (VV) are widely distributed worldwide and commonly eaten as mushrooms. In this study, cell viabilities were evaluated for a human colorectal adenocarcinoma cell line (SW480 cells) and a human monocytic leukemia cell line (THP-1 cells). Apoptotic mechanisms induced by the protein extracts of PO and VV were evaluated for SW480 cells. The viabilities of THP-1 and SW480 cells decreased in a concentration-dependent manner after 24 h of treatment with the protein extracts of CL, PO or VV. Apoptosis analysis revealed that the percentage of SW480 cells in the SubG1 phase (a marker of apoptosis) was increased upon PO and VV protein-extract treatments, indicating that oligonucleosomal DNA fragmentation existed concomitantly with cellular death. The PO and VV protein extracts induced reactive oxygen species (ROS) production, glutathione (GSH) depletion and mitochondrial transmembrane potential (ΔΨm) loss in SW480 cells. Pretreatment with N-acetylcysteine, GSH or cyclosporine A partially prevented the apoptosis induced by PO protein extracts, but not that induced by VV extracts, in SW480 cells. The protein extracts of CL, PO and VV exhibited therapeutic efficacy against human colorectal adenocarcinoma cells and human monocytic leukemia cells. The PO protein extracts induced apoptosis in SW480 cells partially through ROS production, GSH depletion and mitochondrial dysfunction. Therefore, the protein extracts of these mushrooms could be considered an important source of new anti-cancer drugs

Inductance modeling for onchip interconnects

Abstract. As the operation frequency reaches gigahertz in deep-submicron designs, the effects of inductance on noise and delay can no longer be neglected. Most of the previous works on inductance extraction are field-solvers, which are intrinsically more accurate but computationally expensive. Others focus on modeling the inductances of special routing topologies such as the bus structure. Therefore, it is not suitable to incorporate them on-line into a layout (placement and routing) tool for inductance (delay and noise) optimization. In this paper, we consider the overlapping of unequal wire lengths and dimensions to efficiently extract the loop inductance from the coplanar interconnect structure. The difference between our simulation results and the estimation values obtained by FastHenry [12] is within 10% for practical cases. In particular, our modeling is extremely efficient, and thus can be incorporated into a layout tool for inductance optimization

TPMD: a database and resources of microsatellite marker genotyped in Taiwanese populations

Taiwan Polymorphic Marker Database (TPMD) (http://tpmd.nhri.org.tw/) is a marker database designed to provide experimental details and useful marker information allelotyped in Taiwanese populations accompanied by resources and technical supports. The current version deposited more than 372 000 allelotyping data from 1425 frequently used and fluorescent-labeled microsatellite markers with variation types of dinucleotide, trinucleotide and tetranucleotide. TPMD contains text and map displays with searchable and retrievable options for marker names, chromosomal location in various human genome maps and marker heterozygosity in populations of Taiwanese, Japanese and Caucasian. The integration of marker information in map display is useful for the selection of high heterozygosity and commonly used microsatellite markers to refine mapping of diseases locus followed by identification of disease gene by positional candidate cloning. In addition, our results indicated that the number of markers with heterozygosity over 0.7 in Asian populations is lower than that in Caucasian. To increase accuracy and facilitate genetic studies using microsatellite markers, we also list markers with genotyping difficulty due to ambiguity of allele calling and recommend an optimal set of microsatellite markers for genotyping in Taiwanese, and possible extension of genotyping in other Mongoloid populations

UMARS: Un-MAppable Reads Solution

[[abstract]]Background: Un-MAppable Reads Solution (UMARS) is a user-friendly web service focusing on retrieving valuable information from sequence reads that cannot be mapped back to reference genomes. Recently, next-generation sequencing (NGS) technology has emerged as a powerful tool for generating high-throughput sequencing data and has been applied to many kinds of biological research. In a typical analysis, adaptor-trimmed NGS reads were first mapped back to reference sequences, including genomes or transcripts. However, a fraction of NGS reads failed to be mapped back to the reference sequences. Such un-mappable reads are usually imputed to sequencing errors and discarded without further consideration.Methods: We are investigating possible biological relevance and possible sources of un-mappable reads. Therefore, we developed UMARS to scan for virus genomic fragments or exon-exon junctions of novel alternative splicing isoforms from un-mappable reads. For mapping un-mappable reads, we first collected viral genomes and sequences of exon-exon junctions. Then, we constructed UMARS pipeline as an automatic alignment interface.Results: By demonstrating the results of two UMARS alignment cases, we show the applicability of UMARS. We first showed that the expected EBV genomic fragments can be detected by UMARS. Second, we also detected exon-exon junctions from un-mappable reads. Further experimental validation also ensured the authenticity of the UMARS pipeline. The UMARS service is freely available to the academic community and can be accessed via http://musk.ibms.sinica.edu.tw/UMARS/.Conclusions: In this study, we have shown that some un-mappable reads are not caused by sequencing errors. They can originate from viral infection or transcript splicing. Our UMARS pipeline provides another way to examine and recycle the un-mappable reads that are commonly discarded as garbage

Antidiabetic effect and mode of action of cytopiloyne

Cytopiloyne was identified as a novel polyacetylenic compound. However, its antidiabetic properties are poorly understood. The aim of the present study was to investigate the anti-diabetic effect and mode of action of cytopiloyne on type 2 diabetes (T2D). We first evaluated the therapeutic effect of cytopiloyne on T2D in db/db mice. We found that one dose of cytopiloyne reduced postprandial glucose levels while increasing blood insulin levels. Accordingly, long-term treatment with cytopiloyne reduced postprandial blood glucose levels, increased blood insulin, improved glucose tolerance, suppressed the level of glycosylated hemoglobin A1c (HbA1c), and protected pancreatic islets in db/db mice. Next, we studied the anti-diabetic mechanism of action of cytopiloyne. We showed that cytopiloyne failed to decrease blood glucose in streptozocin- (STZ-)treated mice whose β cells were already destroyed. Additionally, cytopiloyne dose dependently increased insulin secretion and expression in β cells. The increase of insulin secretion/expression of cytopiloyne was regulated by protein kinase C α (PKC α ) and its activators, calcium, and diacylglycerol (DAG). Overall, our data suggest that cytopiloyne treats T2D via regulation of insulin production involving the calcium/DAG/PKC α cascade in β cells. These data thus identify the molecular mechanism of action of cytopiloyne and prove its therapeutic potential in T2D

A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription Factor MYB

Background. MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis. Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included. Results. ARNT2 is predicted to be the essential gene partner of MYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature of MYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members of MYB are elevated to promote poor prognosis when both levels of MYB and ARNT2 decline. Both MYBL1 and MYBL2 may partially decrease the tumor suppressive activities that are predicted to be up-regulated by MYB and ARNT2. Conclusions. The major prognostic feature of MYB is predicted to be determined by the MYB subnetwork (41 probes) that is relevant across subtypes