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

Three perceptions of the evapotranspiration landscape: comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances

A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km2) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to Nash–Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling

Preparation of 9-fluoro-9-deoxy-N-[2-14C]acetylneuraminic acid Activation and transfer onto asialo-α1-acid glycoprotein

Abstract9-Fluoro-9-deoxy-N-[2-14C]acetylneuraminic acid has been prepared from 6-fluoro-6-deoxy-N-acetylmannosamine and [2-14C]pyruvic acid for the first time, using Clostridium perfringens N-acetylneuraminate pyruvate-lyase (EC 4.1.3.3). The fluoro sugar was activated by CMP-N-acetylneuraminic acid synthase and CTP to yield CMP-9-fluoro-9-deoxy-N-[2-14C]acetylneuraminic acid. Both products were obtained in good yield (60 and 30%, respectively). The radioactive sugar in its activated form is glycosidically attached to asialo-α1-acid glycoprotein by sialyltransferase and can be removed by the action of Vibrio cholerae sialidase. The reaction rates of the enzymes studied are lower with the 9-fluoro derivatives than with the N-acetylneuraminic acid substrates

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

Tunable light and drug induced depletion of target proteins

Biological processes in development and disease are controlled by the abundance, localization and modification of cellular proteins. We have developed versatile tools based on recombinant E3 ubiquitin ligases that are controlled by light or drug induced heterodimerization for nanobody or DARPin targeted depletion of endogenous proteins in cells and organisms. We use this rapid, tunable and reversible protein depletion for functional studies of essential proteins like PCNA in DNA repair and to investigate the role of CED-3 in apoptosis during Caenorhabditis elegans development. These independent tools can be combined for spatial and temporal depletion of different sets of proteins, can help to distinguish immediate cellular responses from long-term adaptation effects and can facilitate the exploration of complex networks

A Mathematical Model for the Dynamics and Synchronization of Cows

We formulate a mathematical model for daily activities of a cow (eating, lying down, and standing) in terms of a piecewise affine dynamical system. We analyze the properties of this bovine dynamical system representing the single animal and develop an exact integrative form as a discrete-time mapping. We then couple multiple cow "oscillators" together to study synchrony and cooperation in cattle herds. We comment on the relevant biology and discuss extensions of our model. With this abstract approach, we not only investigate equations with interesting dynamics but also develop interesting biological predictions. In particular, our model illustrates that it is possible for cows to synchronize \emph{less} when the coupling is increased.Comment: to appear in Physica

Copycat dynamics in leaderless animal group navigation

Background: Many animals are known to have improved navigational efficiency when moving together as a social group. One potential mechanism for social group navigation is known as the 'many wrongs principle', where information from many inaccurate compasses is pooled across the group. In order to understand how animal groups may use the many wrongs principle to navigate, it is important to consider how directional information is transferred and shared within the group. Methods: Here we use an individual-based model to explore the information-sharing and copying dynamics of a leaderless animal group navigating towards a target in a virtual environment. We assume that communication and information-sharing is indirect and arises through individuals partially copying the movement direction of their neighbours and weighting this information relative to their individual navigational knowledge. Results: We find that the best group navigation performance occurs when individuals directly copy the direction of movement of a subset of their neighbours while only giving a small (6%) weighting to their individual navigational knowledge. Surprisingly, such a strategy is shown to be highly efficient regardless of the level of individual navigational error. We find there is little relative improvement in navigational efficiency when individuals copy from more than 7 influential neighbours. Conclusions: Our findings suggest that we would expect navigating group-living animals to predominantly copy the movement of others rather than relying on their own navigational knowledge. We discuss our results in the context of individual and group navigation behaviour in animals

NLRC5 promotes transcription of BTN3A1-3 genes and Vγ9Vδ2 T cell-mediated killing

BTN3A molecules-BTN3A1 in particular-emerged as important mediators of Vγ9Vδ2 T cell activation by phosphoantigens. These metabolites can originate from infections, e.g. with Mycobacterium tuberculosis, or by alterations in cellular metabolism. Despite the growing interest in the BTN3A genes and their high expression in immune cells and various cancers, little is known about their transcriptional regulation. Here we show that these genes are induced by NLRC5, a regulator of MHC class I gene transcription, through an atypical regulatory motif found in their promoters. Accordingly, a robust correlation between NLRC5 and BTN3A gene expression was found in healthy, in M. tuberculosis-infected donors' blood cells, and in primary tumors. Moreover, forcing NLRC5 expression promoted Vγ9Vδ2 T-cell-mediated killing of tumor cells in a BTN3A-dependent manner. Altogether, these findings indicate that NLRC5 regulates the expression of BTN3A genes and hence open opportunities to modulate antimicrobial and anticancer immunity