Dampak Pembangunan Infrastruktur Perdesaan Pada Program PNPM Mandiri Perdesaan Di Kabupaten Toli Toli

The purpose of this study was to determine the Development Impact of Rural Infrastructure in PNPM RuralProgram in Toli-Toli. Research conducted on the implementation of PNPM Rural Program in Toli-Toli forfiscal year 2007 and 2008.Primary data obtained from interviews with relevant parties and direct observation in the field, then the datais processed with Descriptive Analysis.The results showed the impact of rural infrastructure development in poor communities in Toli Toli, namely:increasing revenue, impoving public education, improving health and improving the public midset. Impact onvillage institutions, namely: the function and role of local government to be effective, institutions ofparticipatory development and improvement of the quality of facilities.and social infrastructure andeconomic base of societ

Identification of Pathogen Signatures in Prostate Cancer Using RNA-seq - Fig 1

<p>(A) Pathogen detection pipeline (B) Fusion Reads with one part (P1) mapped to human sequences and the other part (P2) mapped to pathogen sequences. Reads marked in blue are mapped to human sequences, reads marked in green are mapped to pathogen sequences, reads marked in black are unmapped.</p

Identification of Pathogen Signatures in Prostate Cancer Using RNA-seq

<div><p>Infections of the prostate by bacteria, human papillomaviruses, polyomaviruses, xenotropic murine leukemia virus (MLV)-related gammaretroviruses, human cytomegaloviruses and other members of the herpesvirus family have been widely researched. However, many studies have yielded conflicting and controversial results. In this study, we systematically investigated the transcriptomes of human prostate samples for the unique genomic signatures of these pathogens using RNA-seq data from both western and Chinese patients. Human and nonhuman RNA-seq reads were mapped onto human and pathogen reference genomes respectively using alignment tools Bowtie and BLAT. Pathogen infections and integrations were analyzed in adherence with the standards from published studies. Among the nine pathogens (Propionibacterium acnes, HPV, HCMV, XMRV, BKV, JCV, SV40, EBV, and HBV) we analyzed, Propionibacterium acnes genes were detected in all prostate tumor samples and all adjacent samples, but not in prostate samples from healthy individuals. SV40, HCMV, EBV and low-risk HPVs transcripts were detected in one tumor sample and two adjacent samples from Chinese prostate cancer patients, but not in any samples of western prostate cancer patients; XMRV, BKV and JCV sequences were not identified in our work; HBV, as a negative control, was absent from any samples. Moreover, no pathogen integration was identified in our study. While further validation is required, our analysis provides evidence of Propionibacterium acnes infections in human prostate tumors. Noted differences in viral infections across ethnicity remain to be confirmed with other large prostate cancer data sets. The effects of bacterial and viral infections and their contributions to prostate cancer pathogenesis will require continuous research on associated pathogens.</p></div

Cancer-specific <i>P</i>. <i>acnes</i> genes in Data set 2: Western prostate cancer samples compared with matched adjacent samples.

<p>Only expressed transcripts with an FPKM > = 1.0 in at least 3 out 10 cancer samples are shown.</p><p>Cancer-specific <i>P</i>. <i>acnes</i> genes in Data set 2: Western prostate cancer samples compared with matched adjacent samples.</p

Viral transcripts detected in Chinese prostate samples.

<p>Expressed gene # column shows the number of expressed genes in each virus detected sample from RNA-seq data.</p><p>7N. read counts column shows the number of paired-end reads mapped to each viral reference sequence for sample 7N.</p><p>7T. read counts column shows the number of paired-end reads mapped to each viral reference sequence for sample 7T.</p><p>8N. read counts column shows the number of paired-end reads mapped to each viral reference sequence for sample 8N.</p><p>Viral transcripts detected in Chinese prostate samples.</p

<i>P</i>. <i>acnes</i> genes expressed in Data set 1.

<p><i>P</i>. <i>acnes</i> genes expressed in Data set 1.</p

Living Coordinative Chain-Transfer Polymerization and Copolymerization of Ethene, α-Olefins, and α,ω-Nonconjugated Dienes using Dialkylzinc as “Surrogate” Chain-Growth Sites

Highly efficient, rapid, and reversible chain transfer between active transition-metal-based propagating centers derived from {Cp*Hf(Me)[N(Et)C(Me)N(Et)]}[B(C6F5)4] (Cp* = η5-C5Me5) (1a) or {Cp*Hf(Me)[N(Et)C(Me)N(Et)]}[B(C6F5)3Me] (1b) and multiple equivalents of dialkylzinc (ZnR2) acting as “surrogate” chain-growth sites has been achieved for establishing the living coordinative chain-transfer polymerization (CCTP) of ethene, α-olefins, and α,ω-nonconjugated dienes and living CCTP copolymerization of ethene with α-olefins and α,ω-nonconjugated dienes. These living CCTP processes not only provide a work-around solution to the “one chain per metal” cap on product yield currently limiting traditional living coordination polymerization of ethene and α-olefins but, in addition, provide access to practical volumes of a variety of unique new classes of precision polyolefins of tunable molecular weights and very narrow polydispersity (Mw/Mn ≤ 1.1)

Sub-network of Adamon.

<p>Each node represents a drug. Drugs approved for the treatment of Parkinson 's disease are marked in orange. Drugs approved for pain treatment are marked in blue.</p

Sub-network of Tasmar.

<p>Each node represents a drug. Drugs approved for the treatment of Parkinson's disease are marked in orange. Drugs approved for rheumatoid arthritis therapy are marked in blue.</p

Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning

<div><p>Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of <b>N</b>ormalized <b>D</b>iscounted <b>C</b>umulative <b>G</b>ain in the top <b>5</b> positions (NDCG@5)≥0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5≥0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with <b>E</b>xpression <b>A</b>nalysis <b>S</b>ystematic <b>E</b>xplorer (EASE) score≤10⁻⁵ (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5≥0.7. In all of the 106 drug-indication pairs with an EASE score≤0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.</p></div