Multilocus Sequence Typing of Genital Chlamydia trachomatis in Norway Reveals Multiple New Sequence Types and a Large Genetic Diversity

Background: The Chlamydia trachomatis incidence rate in Finnmark, the most northern and sparsely populated county in Norway, has been twice the national average. This population based cross-sectional study among Finnmark high school students had the following aims: i) to examine distribution of multilocus sequence types (STs) of C. trachomatis in a previously unmapped area, ii) to compare chlamydia genetic diversity in Finnmark with that of two urban regions, and iii) to compare discriminatory capacity of multilocus sequence typing (MLST) with conventional ompA sequencing in a large number of chlamydia specimens. Methodology: ompA sequencing and a high-resolution MLST system based on PCR amplification and DNA sequencing of five highly variable genetic regions were used. Eighty chlamydia specimens from adolescents aged 15-20 years in Finnmark were collected in five high schools (n = 60) and from routine clinical samples in the laboratory (n = 20). These were compared to routine clinical samples from adolescents in Tromso (n = 80) and Trondheim (n = 88), capitals of North and Central Norway, respectively. Principal Findings: ompA sequencing detected 11 genotypes in 248 specimens from all three areas. MLST displayed 50 STs providing a five-fold higher resolution. Two-thirds of all STs were novel. The common ompA E/Bour genotype comprised 46% and resolved into 24 different STs. MLST identified the Swedish new variant of C. trachomatis not discriminated by ompA sequencing. Simpson's discriminatory index (D) was 0.93 for MLST, while a corrected D-c was 0.97. There were no statistically significant differences in ST genetic diversity between geographic areas. Finnmark had an atypical genovar distribution with G being predominant. This was mainly due to expansion of specific STs of which the novel ST161 was unique for Finnmark. Conclusions/Significance: MLST revealed multiple new STs and a larger genetic diversity in comparison to ompA sequencing and proved to be a useful tool in molecular epidemiology of chlamydia infections.Manuscript title: High-resolution Multilocus Sequence Typing of Chlamydia trachomatis reveals multiple new genotypes in North and Central Norwa

Implementing GitHub Actions Continuous Integration to Reduce Error Rates in Ecological Data Collection

Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real-time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change

Current status and perspectives of interventional clinical trials for glioblastoma - analysis of ClinicalTrials.gov

The records of 208.777 (100%) clinical trials registered at ClinicalTrials.gov were downloaded on the 19th of February 2016. Phase II and III trials including patients with glioblastoma were selected for further classification and analysis. Based on the disease settings, trials were classified into three groups: newly diagnosed glioblastoma, recurrent disease and trials with no differentiation according to disease setting. Furthermore, we categorized trials according to the experimental interventions, the primary sponsor, the source of financial support and trial design elements. Trends were evaluated using the autoregressive integrated moving average model. Two hundred sixteen (0.1%) trials were selected for further analysis. Academic centers (investigator initiated trials) were recorded as primary sponsors in 56.9% of trials, followed by industry 25.9%. Industry was the leading source of monetary support for the selected trials in 44.4%, followed by 25% of trials with primarily academic financial support. The number of newly initiated trials between 2005 and 2015 shows a positive trend, mainly through an increase in phase II trials, whereas phase III trials show a negative trend. The vast majority of trials evaluate forms of different systemic treatments (91.2%). In total, one hundred different molecular entities or biologicals were identified. Of those, 60% were involving drugs specifically designed for central nervous system malignancies. Trials that specifically address radiotherapy, surgery, imaging and other therapeutic or diagnostic methods appear to be rare. Current research in glioblastoma is mainly driven or sponsored by industry, academic medical oncologists and neuro-oncologists, with the majority of trials evaluating forms of systemic therapies. Few trials reach phase III. Imaging, radiation therapy and surgical procedures are underrepresented in current trials portfolios. Optimization in research portfolio for glioblastoma is needed

allodb: An R package for biomass estimation at globally distributed extratropical forest plots

Allometric equations for calculation of tree above-ground biomass (AGB) form the basis for estimates of forest carbon storage and exchange with the atmosphere. While standard models exist to calculate forest biomass across the tropics, we lack a standardized tool for computing AGB across boreal and temperate regions that comprise the global extratropics. Here we present an integrated R package, allodb, containing systematically selected published allometric equations and proposed functions to compute AGB. The data component of the package is based on 701 woody species identified at 24 large Forest Global Earth Observatory (ForestGEO) forest dynamics plots representing a wide diversity of extratropical forests. A total of 570 parsed allometric equations to estimate individual tree biomass were retrieved, checked and combined using a weighting function designed to ensure optimal equation selection over the full tree size range with smooth transitions across equations. The equation dataset can be customized with built-in functions that subset the original dataset and add new equations. Although equations were curated based on a limited set of forest communities and number of species, this resource is appropriate for large portions of the global extratropics and can easily be expanded to cover novel forest types