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The fundamental phenotypes of growth rate, size and morphology are the result of complex interactions between genotype and environment. We developed a high-throughput software application, WormSizer, which computes size and shape of nematodes from brightfield images. Existing methods for estimating volume either coarsely model the nematode as a cylinder or assume the worm shape or opacity is invariant. Our estimate is more robust to changes in morphology or optical density as it only assumes radial symmetry. This open source software is written as a plugin for the well-known image-processing framework Fiji/ImageJ. It may therefore be extended easily. We evaluated the technical performance of this framework, and we used it to analyze growth and shape of several canonical Caenorhabditis elegans mutants in a developmental time series. We confirm quantitatively that a Dumpy (Dpy) mutant is short and fat and that a Long (Lon) mutant is long and thin. We show that daf-2 insulin-like receptor mutants are larger than wild-type upon hatching but grow slow, and WormSizer can distinguish dauer larvae from normal larvae. We also show that a Small (Sma) mutant is actually smaller than wild-type at all stages of larval development. WormSizer works with Uncoordinated (Unc) and Roller (Rol) mutants as well, indicating that it can be used with mutants despite behavioral phenotypes. We used our complete data set to perform a power analysis, giving users a sense of how many images are needed to detect different effect sizes. Our analysis confirms and extends on existing phenotypic characterization of well-characterized mutants, demonstrating the utility and robustness of WormSizer.

A bachelor is haunted by the ghosts of his past girlfriends at his younger brother's wedding.

Improving the efficiency of root systems should result in crop varieties with better yields, requiring fewer chemical inputs, and that can grow in harsher environments. Little is known about the genetic factors that condition root growth because of roots’ complex shapes, the opacity of soil, and environmental influences. We designed a 3D root imaging and analysis platform and used it to identify regions of the rice genome that control several different aspects of root system growth. The results of this study should inform future efforts to enhance root architecture for agricultural benefit. Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci ( r 2 = 24–37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

\"You are cordially invited... Bruce Wayne and Selina Kyle are about to tie the knot, uniting two of Gotham's greatest vigilantes in the wedding of the century. But the city's deadliest villains are determined to crash the party, and only Batman and Catwoman's closest allies stand in their way! Robin vs. Ra's al Ghul: It's grandson versus grandfather as the son of the Bat faces the head of the Demon in a fatal family feud! Nightwing vs. Hush: Batman's original partner turned best man squares off against Bruce Wayne's oldest friend turned bitterest enemy! Batgirl vs. the Riddler: Two of Gotham City's greatest minds clash as the one-time Oracle and the overlord of Zero Year launch a war of wits! Red Hood vs. Anarky: Agents of chaos collide in a battle between the Dark Knight's rogue Robin and the underworld's most unpredictable mastermind! Harley Quinn vs. The Joker: Once upon a time, they too might have said, \"I do.\" Now the Clown Prince of Crime and the Mistress of Mayhem are at each other's throats, with all nine of Catwoman's lives hanging in the balance!\"-- Provided by publisher.

Background Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks. Results We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user. Conclusions We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa . We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

AIM: Connectivity structures populations, communities and ecosystems in the sea. The extent of connectivity is, therefore, predicted to also influence the outcomes of conservation initiatives, such as marine reserves. Here we review the published evidence about how important seascape connectivity (i.e. landscape connectivity in the sea) is for marine conservation outcomes. LOCATION: Global. METHODS: We analysed the global literature on the effects of seascape connectivity on reserve performance. RESULTS: In the majority of cases, greater seascape connectivity inside reserves translates into better conservation outcomes (i.e. enhanced productivity and diversity). Research on reserve performance is, however, most often conducted separately from research on connectivity, resulting in few studies (< 5% of all studies of seascape connectivity) that have quantified how connectivity modifies reserve effects on populations, assemblages or ecosystem functioning in seascapes. Nevertheless, evidence for positive effects of connectivity on reserve performance is geographically widespread, encompassing studies in the Caribbean Sea, Florida Keys and western Pacific Ocean. MAIN CONCLUSIONS: Given that research rarely connects the effects of connectivity and reserves, our thesis is that stronger linkages between landscape ecology and marine spatial planning are likely to improve conservation outcomes in the sea. The key science challenge is to identify the full range of ecological functions that are modulated by connectivity and the spatial scale over which these functions enhance conservation outcomes.

Nutrient availability has profound influence on development. In the nematode C. elegans, nutrient availability governs post-embryonic development. L1-stage larvae remain in a state of developmental arrest after hatching until they feed. This \"L1 arrest\" (or \"L1 diapause\") is associated with increased stress resistance, supporting starvation survival. Loss of the transcription factor daf-16/FOXO, an effector of insulin/IGF signaling, results in arrest-defective and starvation-sensitive phenotypes. We show that daf-16/FOXO regulates L1 arrest cell-nonautonomously, suggesting that insulin/IGF signaling regulates at least one additional signaling pathway. We used mRNA-seq to identify candidate signaling molecules affected by daf-16/FOXO during L1 arrest. dbl-1/TGF-β, a ligand for the Sma/Mab pathway, daf-12/NHR and daf-36/oxygenase, an upstream component of the daf-12 steroid hormone signaling pathway, were up-regulated during L1 arrest in a daf-16/FOXO mutant. Using genetic epistasis analysis, we show that dbl-1/TGF-β and daf-12/NHR steroid hormone signaling pathways are required for the daf-16/FOXO arrest-defective phenotype, suggesting that daf-16/FOXO represses dbl-1/TGF-β, daf-12/NHR and daf-36/oxygenase. The dbl-1/TGF-β and daf-12/NHR pathways have not previously been shown to affect L1 development, but we found that disruption of these pathways delayed L1 development in fed larvae, consistent with these pathways promoting development in starved daf-16/FOXO mutants. Though the dbl-1/TGF-β and daf-12/NHR pathways are epistatic to daf-16/FOXO for the arrest-defective phenotype, disruption of these pathways does not suppress starvation sensitivity of daf-16/FOXO mutants. This observation uncouples starvation survival from developmental arrest, indicating that DAF-16/FOXO targets distinct effectors for each phenotype and revealing that inappropriate development during starvation does not cause the early demise of daf-16/FOXO mutants. Overall, this study shows that daf-16/FOXO promotes developmental arrest cell-nonautonomously by repressing pathways that promote larval development.

daf-16/FoxO is required to survive starvation in Caenorhabditis elegans, but how daf-16IFoxO promotes starvation resistance is unclear. We show that daf-16/FoxO restructures carbohydrate metabolism by driving carbon flux through the glyoxylate shunt and gluconeogenesis and into synthesis of trehalose, a disaccharide of glucose. Trehalose is a well-known stress protectant, capable of preserving membrane organization and protein structure during abiotic stress. Metabolomic, genetic, and pharmacological analyses confirm increased trehalose synthesis and further show that trehalose not only supports survival as a stress protectant but also serves as a glycolytic input. Furthermore, we provide evidence that metabolic cycling between trehalose and glucose is necessary for this dual function of trehalose. This work demonstrates that daf-16/FoxO promotes starvation resistance by shifting carbon metabolism to drive trehalose synthesis, which in turn supports survival by providing an energy source and acting as a stress protectant. Most animals rarely have access to a constant supply of food, and so have evolved ways to cope with times of plenty and times of shortage. Insulin is a hormone that travels throughout the body to signal when an animal is well fed. Insulin signaling inhibits the activity of a protein called FoxO, which otherwise switches on and off hundreds of genes to control the starvation response. The roundworm, Caenorhabditis elegans, has been well studied in the laboratory, and often has to cope with starvation in the wild. These worms can pause their development if no food is available, or divert to a different developmental path if they anticipate that food will be short in future. As with more complex animals, the worm responds to starvation by reducing insulin-like signaling, which in turn activates a FoxO protein called daf-16. When the worms stop feeding, daf-16 is switched on, which is crucial for survival. It was known how daf-16 stops the roundworm’s development, but it was not known how it helps the worms to survive starvation. Now, Hibshman et al. have compared normal roundworm larvae to larvae that are missing the gene for daf-16 to determine how this protein influences the roundworm’s ability to survive starvation. The worms were examined with and without food, to look for which genes were switched on and off by daf-16 during starvation. This revealed that daf-16 controls metabolism, activating a metabolic shortcut that makes the worms produce glucose and begin turning it into another type of sugar, called trehalose. This sugar usually promotes survival in conditions where water is limiting, like dehydration and high salt, but it can also be broken down to release energy. The levels of trehalose in the worms rose within hours of the onset of starvation. To confirm the importance of trehalose in surviving starvation, roundworms with mutations in genes involved in glucose or trehalose production were examined, as was the effect of giving starving worms glucose or trehalose. Disrupting the production of sugars caused the worms to die sooner of starvation, while supplementing with sugar had the opposite effect meaning the worms survived for longer. Taken together, these findings reveal that daf-16 protects against starvation by shifting metabolism towards the production of trehalose. This helps worms to survive by both protecting them from stress and providing them with a source of energy. These findings not only extend the current understanding of how animals respond to starvation, but could also lead to improved understanding of diseases where this response goes wrong, including diabetes and obesity.

BackgroundMeasurement of the optic nerve sheath diameter (ONSD) via ultrasonography has been proposed as a non-invasive metric of intracranial pressure that may be employed during in-field patient triage. However, first responders are not typically trained to conduct sonographic exams and/or do not have access to an expensive ultrasound device. Therefore, for successful deployment of ONSD measurement in-field, we believe that first responders must have access to low-cost, portable ultrasound and be assisted by artificial intelligence (AI) systems that can automatically interpret the optic nerve sheath ultrasound scan.We examine the suitability of five commercially available, low-cost, portable ultrasound devices that can be combined with future artificial intelligence algorithms to reduce the training required for and cost of in-field optic nerve sheath diameter measurement. This paper is focused on the quality of the images generated by these low-cost probes. We report results of a clinician preference survey and compare with a lab analysis of three quantitative image quality metrics across devices. We also examine the suitability of the devices in a hypothetical far-forward deployment using operators unskilled in ultrasound, with the assumption of a future onboard AI video interpreter.ResultsWe find statistically significant differences in clinician ranking of the devices in the following categories: “Image Quality”, “Ease of Acquisition”, “Software”, and “Overall ONSD”. We show differences in signal-to-noise ratio, generalized contrast-to-noise ratio, point-spread function across the devices. These differences in image quality result in a statistically significant difference in manual ONSD measurement. Finally, we show that sufficiently wide transducers can capture the optic nerve sheath during blind (no visible B-mode) scans performed by operators unskilled in sonography.ConclusionsUltrasound of the optic nerve sheath has the potential to be a convenient, non-invasive, point-of-injury or triage measure for elevated intracranial pressure in cases of traumatic brain injury. When transducer width is sufficient, briefly trained operators may obtain video sequences of the optic nerve sheath without guidance. This data suggest that unskilled operators are able to achieve the images needed for AI interpretation. However, we also show that image quality differences between ultrasound probes may influence manual ONSD measurements.