Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame-to-frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB-based image processing package well-suited to quantitative analysis of high-throughput live-cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine-learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame-to-frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell-cycle dynamics in bacteria as well as cell-contact mediated phenomena. This package has a range of built-in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution.

Science is facing a “replication crisis” in which many experimental findings cannot be replicated and are likely to be false. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. We model the community’s confidence in a claim as a Markov process with successive published results shifting the degree of belief. Publication bias in favor of positive findings influences the distribution of published results. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.DOI: http://dx.doi.org/10.7554/eLife.21451.001

Cold pools (CPs) contribute to convective organization. However, it is unclear by which mechanisms organization occurs. By using a particle method to track CP gust fronts in large eddy simulations, we characterize the basic collision modes between CPs. Our results show that CP interactions, where three expanding gust fronts force an updraft, are key at triggering new convection. Using this, we conceptualize CP dynamics into a parameter‐free mathematical model: circles expand from initially random points in space. Where two expanding circles collide, a stationary front is formed. However, where three expanding circles enclose a single point, a new expanding circle is seeded. This simple model supports three fundamental features of CP dynamics: precipitation cells constitute a spatially interacting system, CPs come in generations, and scales steadily increase throughout the diurnal cycle. Finally, this model provides a framework for how CPs act to cause convective self‐organization, clustering, and extremes.

Early mammalian development is both highly regulative and self-organizing. It involves the interplay of cell position, predetermined gene regulatory networks, and environmental interactions to generate the physical arrangement of the blastocyst with precise timing. However, this process occurs in the absence of maternal information and in the presence of transcriptional stochasticity. How does the preimplantation embryo ensure robust, reproducible development in this context? It utilizes a versatile toolbox that includes complex intracellular networks coupled to cell—cell communication, segregation by differential adhesion, and apoptosis. Here, we ask whether a minimal set of developmental rules based on this toolbox is sufficient for successful blastocyst development, and to what extent these rules can explain mutant and experimental phenotypes. We implemented experimentally reported mechanisms for polarity, cell—cell signaling, adhesion, and apoptosis as a set of developmental rules in an agent-based in silico model of physically interacting cells. We find that this model quantitatively reproduces specific mutant phenotypes and provides an explanation for the emergence of heterogeneity without requiring any initial transcriptional variation. It also suggests that a fixed time point for the cells’ competence of fibroblast growth factor (FGF)/extracellular signal—regulated kinase (ERK) sets an embryonic clock that enables certain scaling phenomena, a concept that we evaluate quantitatively by manipulating embryos in vitro. Based on these observations, we conclude that the minimal set of rules enables the embryo to experiment with stochastic gene expression and could provide the robustness necessary for the evolutionary diversification of the preimplantation gene regulatory network.

Convective self-aggregation is a modelling paradigm for convective rain cell organisation over a constant-temperature tropical sea surface. This set-up can give rise to cloud clusters developing over timescales of weeks. In reality, sea-surface temperatures do oscillate diurnally, affecting the atmospheric state and influencing rain rates significantly. Over land, surface temperatures vary more strongly. Here, we carry out a suite of cloud-resolving numerical experiments, and find that qualitatively different dynamics emerge from modest surface temperature oscillations: while the spatial distribution of rainfall is homogeneous during the first day, already on the second day, the rain field is firmly structured. In later days, this clustering becomes stronger and alternates from day to day. We show that these features are robust to changes in resolution, domain size and mean surface temperature, but can be removed by a reduction of the amplitude of diurnal surface temperature oscillation, suggesting a transition from a random to a clustered state. Maximal clustering occurs at a scale of $${l}_{\max }\approx 180\ {\rm{km}}$$ l max ≈ 180 km , which we relate to the emergence of mesoscale convective systems. At $${l}_{\max }$$ l max , rainfall is strongly enhanced and far exceeds the rainfall expected at random. Simple conceptual modelling helps interpret the transition to clustering, which is driven by the formation of mesoscale convective systems, and brings about day-to-day moisture oscillations. Our results may help clarify how continental extremes build up, and how cloud clustering over the tropical ocean could emerge as an instance of spontaneous symmetry breaking at timescales much faster than in conventional radiative–convective equilibrium self-aggregation.