Ignacio Arganda‐Carreras

SUMMARY: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers. AVAILABILITY AND IMPLEMENTATION: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation . CONTACT: ignacio.arganda@ehu.eus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Flexural and angular limb deformities (LD) are an important cause of early-life morbidity and mortality/euthanasia in Thoroughbred foals. The majority are congenital in origin but, to date, their precise aetiology is poorly understood. We hypothesized that maternal-and pregnancy-level factors, particularly those with potential to influence in-utero growth and development, could play an important role. The aim of this study was therefore to investigate associations between such factors and early-life LD in Thoroughbred foals. A birth cohort was established on seven farms across the United Kingdom and Ireland and details of veterinary interventions for LD in foals in the first six months of life prospectively recorded. Details of dams' signalment, breeding history and reproductive and veterinary history in the breeding season(s) of interest were retrieved retrospectively from stud farm and veterinary records. Associations between mare-and pregnancy-level factors and LD in offspring were assessed using multivariable logistic regression. Records were available for 275 pregnancies in 235 mares, over two breeding seasons. Pregnancies resulted in the birth of 272 live foals, 21% of which (n = 57/272, 95% CI, 16-26) required veterinary intervention for LD in the first six months of life. Odds of LD decreased by 4% per day increase in gestation length between 314 and 381 days (OR 0.96, 95% CI, 0.93-0.99, P = .01). Longer gestation length appears to reduce the odds of early-life LD, including within the normal range of gestation length for Thoroughbred foals. Further work is required to elucidate biological mechanisms behind this association.

MOTIVATION: Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images. RESULTS: The MorphoLibJ library proposes a large collection of generic tools based on MM to process binary and grey-level 2D and 3D images, integrated into user-friendly plugins. We illustrate how MorphoLibJ can facilitate the exploitation of 3D images of plant tissues. AVAILABILITY AND IMPLEMENTATION: MorphoLibJ is freely available at http://imagej.net/MorphoLibJ CONTACT: david.legland@nantes.inra.frSupplementary information: Supplementary data are available at Bioinformatics online.

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.