Viewing tropical forest succession as a three-dimensional dynamical system

As tropical forests are complex systems, they tend to be modelled either roughly via scaling relationships or in a detailed manner as high-dimensional systems with many variables. We propose an approach which lies between the two whereby succession in a tropical forest is viewed as a trajectory in the configuration space of a dynamical system with just three dependent variables, namely, the mean leaf-area index (LAI) and its standard deviation (SD) or coefficient of variation along a transect, and the mean diameter at breast height (DBH) of trees above the 90th percentile of the distribution of tree DBHs near the transect. Four stages in this forest succession are identified: (I) naturally afforesting grassland: the initial stage with scattered trees in grassland; (II) very young forest: mostly covered by trees with a few remaining gaps; (III) young smooth forest: almost complete cover by trees of mostly similar age resulting in a low SD; and (IV) old growth or mature forest: the attracting region in configuration space characterized by fluctuating SD from tree deaths and regrowth. High-resolution LAI measurements and other field data from Khao Yai National Park, Thailand show how the system passes through these stages in configuration space, as do simple considerations and a crude cellular automaton model.     

Pseudomonas fluorescens MFE01 delivers a putative type VI secretion amidase that confers biocontrol against the soft-rot pathogen Pectobacterium atrosepticum

The type VI secretion system (T6SS) is a contractile nanomachine widespread in Gram-negative bacteria. The T6SS injects effectors into target cells including eukaryotic hosts and competitor microbial cells and thus participates in pathogenesis and intermicrobial competition. Pseudomonas fluorescens MFE01 possesses a single T6SS gene cluster that confers biocontrol properties by protecting potato tubers against the phytopathogen Pectobacterium atrosepticum (Pca). Here, we demonstrate that a functional T6SS is essential to protect potato tuber by reducing the pectobacteria population. Fluorescence microscopy experiments showed that MFE01 displays an aggressive behaviour with an offensive T6SS characterized by continuous and intense T6SS firing activity. Interestingly, we observed that T6SS firing is correlated with rounding of Pectobacterium cells, suggesting delivery of a potent cell wall targeting effector. Mutagenesis coupled with functional assays then revealed that a putative T6SS secreted amidase, Tae3^Pf, is mainly responsible for MFE01 toxicity towards Pca. Further studies finally demonstrated that Tae3^Pf is toxic when produced in the periplasm, and that its toxicity is counteracted by the Tai3^Pf inner membrane immunity protein.     

A deep learning algorithm for 3D cell detection in whole mouse brain image datasets

Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.