PENGARUH PEMBANGUNAN JALAN SOEKARNO TERHADAP PEMBEBANAN LALU LINTAS DI JALAN TOL MANADO - BITUNG

Kota Manado dan Kota Bitung adalah dua kota di Sulawesi utara yang memiliki potensi yang sangat besar dan dikarenakan ketidakmampuan jalan eksisting (Jalan Nasional Manado – Bitung) untuk menampung semua kendaraan yang harus melintasi ruas jalan ini setiap hari akibat adanya aktifitas perindustrian di ruas jalan ini maka pemerintah memutuskan untuk membangun Jalan Tol Manado – Bitung. Namun jalan Tol Manado – Bitung mempunyai kompetitor yaitu Jalan Soekarno yang mengakibatkan volume lalu lintas untuk jalan tol tidak akan tercapai sesuai dengan yang direncanakan. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengetahui jumlah volume lalu lintas yang akan melewati Jalan Tol dan Jalan Soekarno nanti serta membuat perbandingan volume lalu lintas Jalan Tol dengan dan tanpa adanya Jalan Soekarno. Penelitian ini dimulai dengan pengumpulan data volume lalu lintas secara manual selama 15 jam untuk nantinya akan dikalibrasikan dengan data waktu tempuh yang dimbil selama 4 jam dalam sehari yang terbagi menjadi 2 jam pada waktu off peak hour yaitu pukul 13:00-15:00, dan 2 jam pada waktu peak hour pukul 17:00-19:00 untuk mendapatkan fungsi volume tundaan dengan persamaan yang dikembangkan oleh The Bureau of Public Road. Selanjutnya dibuat perhitungan dengan model Greenshield untuk mengetahui kapasitas dan waktu tempuh arus bebas lalu dengan menggunakan prinsip Keseimbangan I Wardrop dan bantuan Solver yang merupakan fasilitas dari Microsoft Excel dilakukan pembebanan lalu lintas untuk mengetahui volume tiap-tiap ruas jalan.Berdasarkan hasil analisis dari data survey didapat bahwa akibat dibangunnya Jalan Soekarno menyebabkan volume kendaraan yang akan melewati Jalan Tol Manado – Bitung mengalami defisit sebanyak 33% yang awalnya adalah 6772.461 kendaraan menjadi 3415.07 kendaraan. Dan dari hasil analisis didapatkan bahwa membutuhkan waktu sebesar 86.4 menit atau 1 jam 26.4 menit untuk melewati Jalan Tol sepanjang 39.9 km.  Kata Kunci : Off Peak Hour, Peak Hour, The Bureau of Public Road, Greenshields, Solver, Defisit

Management of Chlamydia Cases in Australia (MoCCA): protocol for a non-randomised implementation and feasibility trial

INTRODUCTION: The sexually transmitted infection chlamydia can cause significant complications, particularly among people with female reproductive organs. Optimal management includes timely and appropriate treatment, notifying and treating sexual partners, timely retesting for reinfection and detecting complications including pelvic inflammatory disease (PID). In Australia, mainstream primary care (general practice) is where most chlamydia infections are diagnosed, making it a key setting for optimising chlamydia management. High reinfection and low retesting rates suggest partner notification and retesting are not uniformly provided. The Management of Chlamydia Cases in Australia (MoCCA) study seeks to address gaps in chlamydia management in Australian general practice through implementing interventions shown to improve chlamydia management in specialist services. MoCCA will focus on improving retesting, partner management (including patient-delivered partner therapy) and PID diagnosis. METHODS AND ANALYSIS: MoCCA is a non-randomised implementation and feasibility trial aiming to determine how best to implement interventions to support general practice in delivering best practice chlamydia management. Our method is guided by the Consolidated Framework for Implementation Research and the Normalisation Process Theory. MoCCA interventions include a website, flow charts, fact sheets, mailed specimen kits and autofills to streamline chlamydia consultation documentation. We aim to recruit 20 general practices across three Australian states (Victoria, New South Wales, Queensland) through which we will implement the interventions over 12–18 months. Mixed methods involving qualitative and quantitative data collection and analyses (observation, interviews, surveys) from staff and patients will be undertaken to explore our intervention implementation, acceptability and uptake. Deidentified general practice and laboratory data will be used to measure pre-post chlamydia testing, retesting, reinfection and PID rates, and to estimate MoCCA intervention costs. Our findings will guide scale-up plans for Australian general practice. ETHICS AND DISSEMINATION: Ethics approval was obtained from The University of Melbourne Human Research Ethics Committee (Ethics ID: 22665). Findings will be disseminated via conference presentations, peer-reviewed publications and study reports

An Evaluation of Methods for Inferring Boolean Networks from Time-Series Data

Regulatory networks play a central role in cellular behavior and decision making. Learning these regulatory networks is a major task in biology, and devising computational methods and mathematical models for this task is a major endeavor in bioinformatics. Boolean networks have been used extensively for modeling regulatory networks. In this model, the state of each gene can be either ‘on’ or ‘off’ and that next-state of a gene is updated, synchronously or asynchronously, according to a Boolean rule that is applied to the current-state of the entire system. Inferring a Boolean network from a set of experimental data entails two main steps: first, the experimental time-series data are discretized into Boolean trajectories, and then, a Boolean network is learned from these Boolean trajectories. In this paper, we consider three methods for data discretization, including a new one we propose, and three methods for learning Boolean networks, and study the performance of all possible nine combinations on four regulatory systems of varying dynamics complexities. We find that employing the right combination of methods for data discretization and network learning results in Boolean networks that capture the dynamics well and provide predictive power. Our findings are in contrast to a recent survey that placed Boolean networks on the low end of the ‘‘faithfulness to biological reality’’ and ‘‘ability to model dynamics’’ spectra. Further, contrary to the common argument in favor of Boolean networks, we find that a relatively large number of time points in the timeseries data is required to learn good Boolean networks for certain data sets. Last but not least, while methods have been proposed for inferring Boolean networks, as discussed above, missing still are publicly available implementations thereof. Here, we make our implementation of the methods available publicly in open source at http://bioinfo.cs.rice.edu/

Standardized NEON organismal data for biodiversity research

Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high-quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators\u27 workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks

Cooperation of Sumoylated Chromosomal Proteins in rDNA Maintenance

SUMO is a posttranslational modifier that can modulate protein activities, interactions, and localizations. As the GFP-Smt3p fusion protein has a preference for subnucleolar localization, especially when deconjugation is impaired, the nucleolar role of SUMO can be the key to its biological functions. Using conditional triple SUMO E3 mutants, we show that defects in sumoylation impair rDNA maintenance, i.e., the rDNA segregation is defective and the rDNA copy number decreases in these mutants. Upon characterization of sumoylated proteins involved in rDNA maintenance, we established that Top1p and Top2p, which are sumoylated by Siz1p/Siz2p, most likely collaborate with substrates of Mms21p to maintain rDNA integrity. Cohesin and condensin subunits, which both play important roles in rDNA stability and structures, are potential substrates of Mms21, as their sumoylation depends on Mms21p, but not Siz1p and Siz2p. In addition, binding of cohesin and condensin to rDNA is altered in the mms21-CH E3-deficient mutant

Longitudinal Plasma Metabolomics Profile in Pregnancy—A Study in an Ethnically Diverse U.S. Pregnancy Cohort

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons cohort (n = 214 women at 10-14 and 15-26 weeks, 107 at 26-31 weeks, and 103 at 33-39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics

Low Levels of Human HIP14 Are Sufficient to Rescue Neuropathological, Behavioural, and Enzymatic Defects Due to Loss of Murine HIP14 in Hip14−/− Mice

Huntingtin Interacting Protein 14 (HIP14) is a palmitoyl acyl transferase (PAT) that was first identified due to altered interaction with mutant huntingtin, the protein responsible for Huntington Disease (HD). HIP14 palmitoylates a specific set of neuronal substrates critical at the synapse, and downregulation of HIP14 by siRNA in vitro results in increased cell death in neurons. We previously reported that mice lacking murine Hip14 (Hip14−/−) share features of HD. In the current study, we have generated human HIP14 BAC transgenic mice and crossed them to the Hip14−/− model in order to confirm that the defects seen in Hip14−/− mice are in fact due to loss of Hip14. In addition, we sought to determine whether human HIP14 can provide functional compensation for loss of murine Hip14. We demonstrate that despite a relative low level of expression, as assessed via Western blot, BAC-derived human HIP14 compensates for deficits in neuropathology, behavior, and PAT enzyme function seen in the Hip14−/− model. Our findings yield important insights into HIP14 function in vivo

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London