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

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

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

The Presence of the Past: Culture, Opinion and Identity in Germany

Also CSST Working Paper #125.http://deepblue.lib.umich.edu/bitstream/2027.42/51316/1/552.pd

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

Analysis of 16S rRNA gene sequences and circulating cell-free DNA from plasma of chronic fatigue syndrome and non-fatigued subjects

BACKGROUND: The association of an infectious agent with chronic fatigue syndrome (CFS) has been difficult and is further complicated by the lack of a known lesion or diseased tissue. Cell-free plasma DNA could serve as a sentinel of infection and disease occurring throughout the body. This type of systemic sample coupled with broad-range amplification of bacterial sequences was used to determine whether a bacterial pathogen was associated with CFS. Plasma DNA from 34 CFS and 55 non-fatigued subjects was assessed to determine plasma DNA concentration and the presence of bacterial 16S ribosomal DNA (rDNA) sequences. RESULTS: DNA was isolated from 81 (91%) of 89 plasma samples. The 55 non-fatigued subjects had higher plasma DNA concentrations than those with CFS (average 151 versus 91 ng) and more CFS subjects (6/34, 18%) had no detectable plasma DNA than non-fatigued subjects (2/55, 4%), but these differences were not significant. Bacterial sequences were detected in 23 (26%) of 89. Only 4 (14%) CFS subjects had 16S rDNA sequences amplified from plasma compared with 17 (32%) of the non-fatigued (P = 0.03). All but 1 of the 23 16S rDNA amplicon-positive subjects had five or more unique sequences present. CONCLUSIONS: CFS subjects had slightly lower concentrations or no detectable plasma DNA than non-fatigued subjects. There was a diverse array of 16S rDNA sequences in plasma DNA from both CFS and non-fatigued subjects. There were no unique, previously uncharacterized or predominant 16S rDNA sequences in either CFS or non-fatigued subjects

MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology

While the analysis of mitochondrial morphology has emerged as a key tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a challenging task and bottleneck for statistically robust conclusions. Here, we present Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet in a toolbox for Windows and Linux operating systems that combines segmentation with morphological analysis