FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ

The Ecm11-Gmc2 complex promotes synaptonemal complex formation through assembly of transverse filaments in budding yeast

During meiosis, homologous chromosomes pair at close proximity to form the synaptonemal complex (SC). This association is mediated by transverse filament proteins that hold the axes of homologous chromosomes together along their entire length. Transverse filament proteins are highly aggregative and can form an aberrant aggregate called the polycomplex that is unassociated with chromosomes. Here, we show that the Ecm11-Gmc2 complex is a novel SC component, functioning to facilitate assembly of the yeast transverse filament protein, Zip1. Ecm11 and Gmc2 initially localize to the synapsis initiation sites, then throughout the synapsed regions of paired homologous chromosomes. The absence of either Ecm11 or Gmc2 substantially compromises the chromosomal assembly of Zip1 as well as polycomplex formation, indicating that the complex is required for extensive Zip1 polymerization. We also show that Ecm11 is SUMOylated in a Gmc2-dependent manner. Remarkably, in the unSUMOylatable ecm11 mutant, assembly of chromosomal Zip1 remained compromised while polycomplex formation became frequent. We propose that the Ecm11-Gmc2 complex facilitates the assembly of Zip1 and that SUMOylation of Ecm11 is critical for ensuring chromosomal assembly of Zip1, thus suppressing polycomplex formation

The Raine study had no evidence of significant perinatal selection bias after two decades of follow up: A longitudinal pregnancy cohort study

Background: Cohort studies may increase or decrease their selection bias as they progress through time. The Western Australian Pregnancy Cohort (Raine) Study has followed 2868 children for over two decades; from fetal into adult life. This paper analyses the cohort over time, assessing potential bias that may come and go with recruitment, retention and loss of participants. Methods: Linked data from all births in Western Australian over the 3 years the Raine Cohort was recruited were obtained to compare perinatal characteristics and subsequent health outcomes between the Western Australian (WA) contemporaneous birth population and the Raine Cohort at five time points. Perinatal exposure-outcome comparisons were employed to assess bias due to non-participation in Raine Study subsets. Results: There were demographic differences between the Raine Study cohort and its source population at recruitment with further changes across the period of follow up. Despite these differences, the pregnancy and infant data of those with continuing participation were not significantly different to the WA contemporaneous birth population. None of the exposure-outcome associations were significantly different to those in the WA general population at recruitment or at any cohort reviews suggesting no substantial recruitment or attrition bias. Conclusions: The Raine Study is valuable for association studies, even after 20 years of cohort reviews with increasing non-participation of cohort members. Non-participation has resulted in greater attrition of socially disadvantaged participants, however, exposure-outcome association analyses suggest that there is no apparent resulting selection bias

Lung Adenocarcinoma of Never Smokers and Smokers Harbor Differential Regions of Genetic Alteration and Exhibit Different Levels of Genomic Instability

Recent evidence suggests that the observed clinical distinctions between lung tumors in smokers and never smokers (NS) extend beyond specific gene mutations, such as EGFR, EML4-ALK, and KRAS, some of which have been translated into targeted therapies. However, the molecular alterations identified thus far cannot explain all of the clinical and biological disparities observed in lung tumors of NS and smokers. To this end, we performed an unbiased genome-wide, comparative study to identify novel genomic aberrations that differ between smokers and NS

Dietary fructose in relation to blood pressure and serum uric acid in adolescent boys and girls

Evidence that fructose intake may modify blood pressure is generally limited to adult populations. This study examined cross-sectional associations between dietary intake of fructose, serum uric acid and blood pressure in 814 adolescents aged 13–15 years participating in the Western Australian Pregnancy Cohort (Raine) Study. Energy-adjusted fructose intake was derived from 3-day food records, serum uric acid concentration was assessed using fasting blood and resting blood pressure was determined using repeated oscillometric readings. In multivariate linear regression models, we did not see a significant association between fructose and blood pressure in boys or girls. In boys, fructose intake was independently associated with serum uric acid (P<0.01), and serum uric acid was independently associated with systolic blood pressure (P<0.01) and mean arterial pressure (P<0.001). Although there are independent associations, there is no direct relationship between fructose intake and blood pressure. Our data suggest that gender may influence these relationships in adolescence, with significant associations observed more frequently in boys than girls

Tephrochronology

Tephrochronology is the use of primary, characterized tephras or cryptotephras as chronostratigraphic marker beds to connect and synchronize geological, paleoenvironmental, or archaeological sequences or events, or soils/paleosols, and, uniquely, to transfer relative or numerical ages or dates to them using stratigraphic and age information together with mineralogical and geochemical compositional data, especially from individual glass-shard analyses, obtained for the tephra/cryptotephra deposits. To function as an age-equivalent correlation and chronostratigraphic dating tool, tephrochronology may be undertaken in three steps: (i) mapping and describing tephras and determining their stratigraphic relationships, (ii) characterizing tephras or cryptotephras in the laboratory, and (iii) dating them using a wide range of geochronological methods. Tephrochronology is also an important tool in volcanology, informing studies on volcanic petrology, volcano eruption histories and hazards, and volcano-climate forcing. Although limitations and challenges remain, multidisciplinary applications of tephrochronology continue to grow markedly

Dissection of genetic associations with language-related traits in population-based cohorts

Recent advances in the field of language-related disorders have led to the identification of candidate genes for specific language impairment (SLI) and dyslexia. Replication studies have been conducted in independent samples including population-based cohorts, which can be characterised for a large number of relevant cognitive measures. The availability of a wide range of phenotypes allows us to not only identify the most suitable traits for replication of genetic association but also to refine the associated cognitive trait. In addition, it is possible to test for pleiotropic effects across multiple phenotypes which could explain the extensive comorbidity observed across SLI, dyslexia and other neurodevelopmental disorders. The availability of genome-wide genotype data for such cohorts will facilitate this kind of analysis but important issues, such as multiple test corrections, have to be taken into account considering that small effect sizes are expected to underlie such associations

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health