Foray search: An effective systematic dispersal strategy in fragmented landscapes

In the absence of evidence to the contrary, population models generally assume that the dispersal trajectories of animals are random, but systematic dispersal could be more efficient at detecting new habitat and may therefore constitute a more realistic assumption. Here, we investigate, by means of simulations, the properties of a potentially widespread systematic dispersal strategy termed "foray search." Foray search was more efficient in detecting suitable habitat than was random dispersal in most landscapes and was less subject to energetic constraints. However, it also resulted in considerably shorter net dispersed distances and higher mortality per net dispersed distance than did random dispersal, and it would therefore be likely to lead to lower dispersal rates toward the margins of population networks. Consequently, the use of foray search by dispersers could crucially affect the extinction-colonization balance of metapopulations and the evolution of dispersal rates. We conclude that population models need to take the dispersal trajectories of individuals into account in order to make reliable predictions

Swarm intelligence: when uncertainty meets conflict

When animals share decisions with others, they pool personal information, offset individual errors and, thereby, increase decision accuracy. This is termed ‘swarm intelligence.’ But what if those decisions involve conflicts of interest between individual decision-makers? Should animals share decisions with individuals whose goals are different from, and partially in conflict with, their own? A group decision model developed by Larissa Conradt (MPI Berlin) and colleagues finds that, contrary to intuition, conflicting goals often increase both decision accuracy and the individual gains derived from shared decisions. Thus, conflicts of interest, far from hampering effective decision making, can actually improve decision outcomes for all stakeholders, as long as they also have some goals in common. By contrast, conflict-free decisions shared by animals which all have the same goals are often surprisingly poor

Non-random dispersal in the butterfly Maniola jurtina: implications for metapopulation models

The dispersal patterns of animals are important in metapopulation ecology because they affect the dynamics and survival of populations. Theoretical models assume random dispersal but little is known in practice about the dispersal behaviour of individual animals or the strategy by which dispersers locate distant habitat patches. In the present study, we released individual meadow brown butterflies (Maniola jurtina) in a non-habitat and investigated their ability to return to a suitable habitat. The results provided three reasons for supposing that meadow brown butterflies do not seek habitat by means of random flight. First, when released within the range of their normal dispersal distances, the butterflies orientated towards suitable habitat at a higher rate than expected at random. Second, when released at larger distances from their habitat, they used a non-random, systematic, search strategy in which they flew in loops around the release point and returned periodically to it. Third, butterflies returned to a familiar habitat patch rather than a non-familiar one when given a choice. If dispersers actively orientate towards or search systematically for distant habitat, this may be problematic for existing metapopulation models, including models of the evolution of dispersal rates in metapopulations

Feature Extraction using Spiking Convolutional Neural Networks

Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. Training deep convolutional neural networks is a memory and power intensive job. Spiking networks could potentially help in reducing the power usage. There is a large pool of tools for one to chose to train artificial neural networks of any size, on the other hand all the available tools to simulate spiking neural networks are geared towards computational neuroscience applications and they are not suitable for real life applications. In this work we focus on implementing a spiking CNN using Tensorflow to examine behaviour of the network and study catastrophic forgetting in the spiking CNN and weight initialization problem in R-STDP using MNIST data set. We also report classification accuracies that are achieved using N-MNIST and MNIST data sets

In vivo labeling of endogenous genomic loci in C. elegans using CRISPR/dCas9

Visualization of genomic loci with open chromatin state has been reported in mammalian tissue culture cells using a CRISPR/Cas9-based system that utilizes an EGFP-tagged endonuclease-deficient Cas9 protein (dCas9::EGFP) (Chen et al. 2013). Here, we adapted this approach for use in Caenorhabditis elegans . We generated a C. elegans strain that expresses the dCas9 protein fused to two nuclear-localized EGFP molecules (dCas9::NLS::2xEGFP::NLS) in an inducible manner. Using this strain, we report the visualization in live C. elegans embryos of two endogenous repetitive loci, rrn-4 and rrn-1 , from which 5S and 18S ribosomal RNAs are constitutively generated

Engulfment pathways promote programmed cell death by enhancing the unequal segregation of apoptotic potential

Components of the conserved engulfment pathways promote programmed cell death in Caenorhabditis elegans (C. elegans) through an unknown mechanism. Here we report that the phagocytic receptor CED-1 mEGF10 is required for the formation of a dorsal-ventral gradient of CED-3 caspase activity within the mother of a cell programmed to die and an increase in the level of CED-3 protein within its dying daughter. Furthermore, CED-1 becomes enriched on plasma membrane regions of neighbouring cells that appose the dorsal side of the mother, which later forms the dying daughter. Therefore, we propose that components of the engulfment pathways promote programmed cell death by enhancing the polar localization of apoptotic factors in mothers of cells programmed to die and the unequal segregation of apoptotic potential into dying and surviving daughters. Our findings reveal a novel function of the engulfment pathways and provide a better understanding of how apoptosis is initiated during C. elegans development

PIG-1 MELK-dependent phosphorylation of nonmuscle myosin II promotes apoptosis through CES-1 Snail partitioning

The mechanism(s) through which mammalian kinase MELK promotes tumorigenesis is not understood. We find that theC.elegansorthologue of MELK, PIG-1, promotes apoptosis by partitioning an anti-apoptotic factor. TheC.elegansNSM neuroblast divides to produce a larger cell that differentiates into a neuron and a smaller cell that dies. We find that in this context, PIG-1 is required for partitioning of CES-1 Snail, a transcriptional repressor of the pro-apoptotic geneegl-1BH3-only.pig-1MELK is controlled by both aces-1Snail- andpar-4LKB1-dependent pathway, and may act through phosphorylation and cortical enrichment of nonmuscle myosin II prior to neuroblast division. We propose thatpig-1MELK-induced local contractility of the actomyosin network plays a conserved role in the acquisition of the apoptotic fate. Our work also uncovers an auto-regulatory loop through whichces-1Snail controls its own activity through the formation of a gradient of CES-1 Snail protein. Author summary Apoptosis is critical for the elimination of 'unwanted' cells. What distinguishes wanted from unwanted cells in developing animals is poorly understood. We report that in theC.elegansNSM neuroblast lineage, the level of CES-1, a Snail-family member and transcriptional repressor of the pro-apoptotic geneegl-1, contributes to this process. In addition, we demonstrate thatC.elegansPIG-1, the orthologue of mammalian proto-oncoprotein MELK, plays a critical role in controlling CES-1(Snail)levels. Specifically, during NSM neuroblast division, PIG-1(MELK)controls partitioning of CES-1(Snail)into one but not the other daughter cell thereby promoting the making of one wanted and one unwanted cell. Furthermore, we present evidence that PIG-1(MELK)acts prior to NSM neuroblast division by locally activating the actomyosin network

Automated Plankton Classification With a Dynamic Optimization and Adaptation Cycle

With recent advances in Machine Learning techniques based on Deep Neural Networks (DNNs), automated plankton image classification is becoming increasingly popular within the marine ecological sciences. Yet, while the most advanced methods can achieve human-level performance on the classification of everyday images, plankton image data possess properties that frequently require a final manual validation step. On the one hand, this is due to morphological properties manifesting in high intra-class and low inter-class variability, and, on the other hand is due to spatial-temporal changes in the composition and structure of the plankton community. Composition changes enforce a frequent updating of the classifier model via training with new user-generated training datasets. Here, we present a Dynamic Optimization Cycle (DOC), a processing pipeline that systematizes and streamlines the model adaptation process via an automatic updating of the training dataset based on manual-validation results. We find that frequent adaptation using the DOC pipeline yields strong maintenance of performance with respect to precision, recall and prediction of community composition, compared to more limited adaptation schemes. The DOC is therefore particularly useful when analyzing plankton at novel locations or time periods, where community differences are likely to occur. In order to enable an easy implementation of the DOC pipeline, we provide an end-to-end application with graphical user interface, as well as an initial dataset of training images. The DOC pipeline thus allows for high-throughput plankton classification and quick and systematized model adaptation, thus providing the means for highly-accelerated plankton analysis

A caspase-RhoGEF axis contributes to the cell size threshold for apoptotic death in developing Caenorhabditis elegans

A cell's size affects the likelihood that it will die. But how is cell size controlled in this context and how does cell size impact commitment to the cell death fate? We present evidence that the caspase CED-3 interacts with the RhoGEF ECT-2 in Caenorhabditis elegans neuroblasts that generate "unwanted" cells. We propose that this interaction promotes polar actomyosin contractility, which leads to unequal neuroblast division and the generation of a daughter cell that is below the critical "lethal" size threshold. Furthermore, we find that hyperactivation of ECT-2 RhoGEF reduces the sizes of unwanted cells. Importantly, this suppresses the "cell death abnormal" phenotype caused by the partial loss of ced-3 caspase and therefore increases the likelihood that unwanted cells die. A putative null mutation of ced-3 caspase, however, is not suppressed, which indicates that cell size affects CED-3 caspase activation and/or activity. Therefore, we have uncovered novel sequential and reciprocal interactions between the apoptosis pathway and cell size that impact a cell's commitment to the cell death fate

Prenatal Predictors of Infant Self-Regulation: The Contributions of Placental DNA Methylation of NR3C1 and Neuroendocrine Activity

We examined whether placental DNA methylation of the glucocorticoid receptor gene, NR3C1 was associated with self-regulation and neuroendocrine responses to a social stressor in infancy. Placenta samples were obtained at birth and mothers and their infants (n = 128) participated in the still-face paradigm when infants were 5 months old. Infant self-regulation following the still-face episode was coded and pre-stress cortisol and cortisol reactivity was assessed in response to the still-face paradigm. A factor analysis of NR3C1 CpG sites revealed two factors: one for CpG sites 1-4 and the other for sites 5-13. DNA methylation of the factor comprising NR3C1 CpG sites 5-13 was related to greater cortisol reactivity and infant self-regulation, but cortisol reactivity was not associated with infant self-regulation. The results reveal that prenatal epigenetic processes may explain part of the development of infant self-regulation