Computational morphodynamics of plants: integrating development over space and time

The emerging field of computational morphodynamics aims to understand the changes that occur in space and time during development by combining three technical strategies: live imaging to observe development as it happens; image processing and analysis to extract quantitative information; and computational modelling to express and test time-dependent hypotheses. The strength of the field comes from the iterative and combined use of these techniques, which has provided important insights into plant development

Computational Morphodynamics: A Modeling Framework to Understand Plant Growth

Computational morphodynamics utilizes computer modeling to understand the development of living organisms over space and time. Results from biological experiments are used to construct accurate and predictive models of growth. These models are then used to make novel predictions that provide further insight into the processes involved, which can be tested experimentally to either confirm or rule out the validity of the computational models. This review highlights two fundamental challenges: (a) to understand the feedback between mechanics of growth and chemical or molecular signaling, and (b) to design models that span and integrate single cell behavior with tissue development. We review different approaches to model plant growth and discuss a variety of model types that can be implemented to demonstrate how the interplay between computational modeling and experimentation can be used to explore the morphodynamics of plant development

Joint action aesthetics

Synchronized movement is a ubiquitous feature of dance and music performance. Much research into the evolutionary origins of these cultural practices has focused on why humans perform rather than watch or listen to dance and music. In this study, we show that movement synchrony among a group of performers predicts the aesthetic appreciation of live dance performances. We developed a choreography that continuously manipulated group synchronization using a defined movement vocabulary based on arm swinging, walking and running. The choreography was performed live to four audiences, as we continuously tracked the performers’ movements, and the spectators’ affective responses. We computed dynamic synchrony among performers using cross recurrence analysis of data from wrist accelerometers, and implicit measures of arousal from spectators’ heart rates. Additionally, a subset of spectators provided continuous ratings of enjoyment and perceived synchrony using tablet computers. Granger causality analyses demonstrate predictive relationships between synchrony, enjoyment ratings and spectator arousal, if audiences form a collectively consistent positive or negative aesthetic evaluation. Controlling for the influence of overall movement acceleration and visual change, we show that dance communicates group coordination via coupled movement dynamics among a group of performers. Our findings are in line with an evolutionary function of dance–and perhaps all performing arts–in transmitting social signals between groups of people. Human movement is the common denominator of dance, music and theatre. Acknowledging the time-sensitive and immediate nature of the performer-spectator relationship, our study makes a significant step towards an aesthetics of joint actions in the performing arts

Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant

Quantitative studies in plant developmental biology require monitoring and measuring the changes in cells and tissues as growth gives rise to intricate patterns. The success of these studies has been amplified by the combined strengths of two complementary techniques, namely live imaging and computational image analysis. Live imaging records time-lapse images showing the spatial-temporal progress of tissue growth with cells dividing and changing shape under controlled laboratory experiments. Image processing and analysis make sense of these data by providing computational ways to extract and interpret quantitative developmental information present in the acquired images. Manual labeling and qualitative interpretation of images are limited as they don't scale well to large data sets and cannot provide field measurements to feed into mathematical and computational models of growth and patterning. Computational analysis, when it can be made sufficiently accurate, is more efficient, complete, repeatable, and less biased. In this chapter, we present some guidelines for the acquisition and processing of images of sepals and the shoot apical meristem of Arabidopsis thaliana to serve as a basis for modeling. We discuss fluorescent markers and imaging using confocal laser scanning microscopy as well as present protocols for doing time-lapse live imaging and static imaging of living tissue. Image segmentation and tracking are discussed. Algorithms are presented and demonstrated together with low-level image processing methods that have proven to be essential in the detection of cell contours. We illustrate the application of these procedures in investigations aiming to unravel the mechanical and biochemical signaling mechanisms responsible for the coordinated growth and patterning in plants

Mutually reinforcing patterning mechanisms: authors' reply

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A dietary non-human sialic acid may facilitate hemolytic-uremic syndrome

Hemolytic-uremic syndrome (HUS) is a systemic disease characterized by microvascular endothelial damage, mainly in the gastrointestinal tract and the kidneys. A major cause of HUS is Shiga toxigenic Escherichia coli (STEC) infection. In addition to Shiga toxin, additional STEC virulence factors may contribute to HUS. One is the newly discovered subtilase cytotoxin (SubAB), which is highly toxic to eukaryotic cells, and when injected intraperitoneally into mice causes pathology resembling that associated with human HUS. Recent data show that SubAB exhibits a strong preference for glycans terminating in 2-3-linked N-glycolylneuraminic acid (Neu5Gc), a sialic acid that humans are unable to synthesize, because we genetically lack the necessary enzyme. However, Neu5Gc can still be found on human cells due to metabolic incorporation from the diet. Dietary incorporation happens to be highest in human endothelium and to a lesser extent in the intestinal epithelium, the two affected cell types in STEC-induced HUS. Mammalian-derived foods such as red meat and dairy products appear to be the primary source of dietary Neu5Gc. Ironically, these are also common sources of STEC contamination. Taken together, these findings suggest a 'two-hit' process in the pathogenesis of human SubAB-induced disease. First, humans eat Neu5Gc-rich food, leading to incorporation of Neu5Gc on the surfaces of endothelial and intestinal cells. Second, when exposed to a SubAB-producing STEC strain, the toxin produced would be able to bind to the intestinal epithelial cells, perhaps causing acute gastrointestinal symptoms, and eventually damaging endothelial cells in other organs like the kidney, thereby causing HUS.Jonas C Löfling, Adrienne W Paton, Nissi M Varki, James C Paton and Ajit Vark

Differential effects of short-chain fatty acids and iron on expression of iha in Shiga-toxigenic Escherichia coli

Shiga-toxigenic Escherichia coli (STEC) colonizing the bowel are exposed to a variety of short-chain fatty acids (SCFAs), including acetate, propionate and butyrate, produced by gut microflora. However, the total concentrations and relative amounts of SCFAs in the lumen vary with intestinal niche. Here we report that conditions simulating SCFA concentrations present in the human gut trigger expression of the iha gene, which encodes an adherence-conferring outer-membrane protein of pathogenic E. coli. We show that growth under conditions simulating colonic, but not ileal, SCFA concentrations increases iha expression in three tested STEC strains, with the strongest expression detected in LEE-negative STEC O113:H21 strain 98NK2. Expression of iha is known to be subject to Fur-mediated iron repression in O157:H7 STEC, and the same occurs in 98NK2. However, exogenous iron did not repress iha expression in the presence of colonic SCFAs in either 98NK2 or the O157:H7 strain EDL933. Moreover, exposure to the iron chelator 2,2'-dipyridyl caused no further enhancement of iha expression over that induced by colonic SCFAs. These findings indicate that SCFAs regulate iha expression in STEC independently of iron. Increased expression of iha under colonic but not ileal SCFA conditions possibly may contribute to preferential colonization of the human colon by STEC.Sylvia Herold, James C. Paton, Potjanee Srimanote and Adrienne W. Pato