Imaging zebrafish embryos by two-photon excitation time-lapse microscopy - Carvalho, Lara; Heisenberg, Carl-Philipp J (Methods in Molecular Biology)
The zebrafish is a favorite model organism to study tissue morphogenesis during development at a subcellular level. This largely results from the fact that zebrafish embryos are transparent and thus accessible to various imaging techniques, such as confocal and two-photon excitation (2PE) microscopy. In particular, 2PE microscopy has been shown to be useful for imaging deep cell layers within the embryo and following tissue morphogenesis over long periods. This chapter describes how to use 2PE microscopy to study morphogenetic movements during early zebrafish embryonic development, providing a general blueprint for its use in zebrafish.

A new model for large-scale population dynamics: quantifying phylogeography - Nicholas Barton; Kelleher, Jerome; Etheridge, Alison M (Evolution; International Journal of Organic Evolution)
Classical models of gene flow fail in three ways: they cannot explain large-scale patterns; they predict much more genetic diversity than is observed; and they assume that loosely linked genetic loci evolve independently. We propose a new model that deals with these problems. Extinction events kill some fraction of individuals in a region. These are replaced by offspring from a small number of parents, drawn from the preexisting population. This model of evolution forwards in time corresponds to a backwards model, in which ancestral lineages jump to a new location if they are hit by an event, and may coalesce with other lineages that are hit by the same event. We derive an expression for the identity in allelic state, and show that, over scales much larger than the largest event, this converges to the classical value derived by Wright and Malécot. However, rare events that cover large areas cause low genetic diversity, large-scale patterns, and correlations in ancestry between unlinked loci.

Investigating temporal changes in hybridisation and introgression between invasive sika (Cervus nippon) and native red deer (Cervus elaphus) on the Kintyre Peninsula, Scotland - Senn, Helen; Goodman, Simon; Swanson, Graeme; Barton, Nicholas H; Pemberton, Josephine (Molecular Ecology)
We investigated temporal changes in hybridization and introgression between native red deer (Cervus elaphus) and invasive Japanese sika (Cervus nippon) on the Kintyre Peninsula, Scotland, over 15 years, through analysis of 1513 samples of deer at 20 microsatellite loci and a mtDNA marker. We found no evidence that either the proportion of recent hybrids, or the levels of introgression had changed over the study period. Nevertheless, in one population where the two species have been in contact since ∼1970, 44% of individuals sampled during the study were hybrids. This suggests that hybridization between these species can proceed fairly rapidly. By analysing the number of alleles that have introgressed from polymorphic red deer into the genetically homogenous sika population, we reconstructed the haplotypes of red deer alleles introduced by backcrossing. Five separate hybridization events could account for all the recently hybridized sika-like individuals found across a large section of the Peninsula. Although we demonstrate that low rates of F1 hybridization can lead to substantial introgression, the progress of hybridization and introgression appears to be unpredictable over the short timescales.

Topic models for semantic video compression - Wanke,Jörn; Ulges, Adrian; Christoph Lampert; Breuel,Thomas M
Most state-of-the-art systems for content-based video understanding tasks require video content to be represented as collections of many low-level descriptors, e.g. as histograms of the color, texture or motion in local image regions. In order to preserve as much of the information contained in the original video as possible, these representations are typically high-dimensional, which conflicts with the aim for compact descriptors that would allow better efficiency and lower storage requirements. In this paper, we address the problem of semantic com- pression of video, i.e. the reduction of low-level descriptors to a small number of dimensions while preserving most of the semantic information. For this, we adapt topic models – which have previously been used as compact representations of still images – to take into account the temporal structure of a video, as well as multi-modal components such as motion information. Experiments on a large-scale collection of YouTube videos show that we can achieve a compression ratio of 20 : 1 compared to ordinary histogram representations and at least 2 : 1 compared to other dimensionality reduction techniques without significant loss of prediction accuracy. Also, improvements are demonstrated for our video-specific extensions modeling temporal structure and multiple modalities.

Optimizing one-shot recognition with micro-set learning - Tang, Kevin D; Tappen, Marshall F; Sukthankar,Rahul; Christoph Lampert
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional class. One-shot recognition aims to apply the knowledge gained from a set of categories with plentiful data to categories for which only a single exemplar is available for each. As with earlier efforts motivated by transfer learning, we seek an internal representation for the domain that generalizes across classes. However, in contrast to existing work, we formulate the problem in a fundamentally new manner by optimizing the internal representation for the one-shot task using the notion of micro-sets. A micro-set is a sample of data that contains only a single instance of each category, sampled from the pool of available data, which serves as a mechanism to force the learned representation to explicitly address the variability and noise inherent in the one-shot recognition task. We optimize our learned domain features so that they minimize an expected loss over micro-sets drawn from the training set and show that these features generalize effectively to previously unseen categories. We detail a discriminative approach for optimizing one-shot recognition using micro-sets and present experiments on the Animals with Attributes and Caltech-101 datasets that demonstrate the benefits of our formulation.

Global interactions in random field models: A potential function ensuring connectedness - Nowozin, Sebastian; Christoph Lampert (SIAM Journal on Imaging Sciences)
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to incorporating only local interactions and cannot model global properties such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that forces the output labeling to be connected and that can naturally be used in the framework of recent maximum a posteriori (MAP)-MRF linear program (LP) relaxations. Using techniques from polyhedral combinatorics, we show that a provably strong approximation to the MAP solution of the resulting MRF can still be found efficiently by solving a sequence of max-flow problems. The efficiency of the inference procedure also allows us to learn the parameters of an MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our algorithm on both synthetic data and on the challenging image segmentation task of the PASCAL Visual Object Classes 2008 data set. We show that in both cases the addition of a connectedness prior significantly reduces the segmentation error.

Unsupervised object discovery: A comparison - Tuytelaars,Tinne; Christoph Lampert; Blaschko,Matthew B; Buntine,Wray (International Journal of Computer Vision)
The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.

Movement templates for learning of hitting and batting - Kober,Jens; Mülling,Katharina; Krömer,Oliver; Christoph Lampert; Schölkopf,Bernhard; Peters, Jan
Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or baseball batting, depend on predictions where the ball can be intercepted and how it can properly be returned to the opponent. These predictions get more accurate over time, hence the behaviors need to be continuously modified. As a result, movement templates with a learned global shape need to be adapted during the execution so that the racket reaches a target position and velocity that will return the ball over to the other side of the net or court. It requires altering learned movements to hit a varying target with the necessary velocity at a specific instant in time. Such a task cannot be incorporated straightforwardly in most movement representations suitable for learning. For example, the standard formulation of the dynamical system based motor primitives (introduced by Ijspeert et al. [1]) does not satisfy this property despite their flexibility which has allowed learning tasks ranging from locomotion to kendama. In order to fulfill this requirement, we reformulate the Ijspeert framework to incorporate the possibility of specifying a desired hitting point and a desired hitting velocity while maintaining all advantages of the original formulation. We show that the proposed movement template formulation works well in two scenarios, i.e., for hitting a ball on a string with a table tennis racket at a specified velocity and for returning balls launched by a ball gun successfully over the net using forehand movements. All experiments were carried out on a Barrett WAM using a four camera vision system.

An efficient divide-and-conquer cascade for nonlinear object detection - Christoph Lampert
We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search (ESS) procedure [11] can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernelized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50% by our method compared to standard cascade evaluation.

P2Y1 receptors inhibit long-term depression in the prefrontal cortex. - Guzmán, José; Schmidt, Hartmut; Franke, Heike; Krügel, Ute; Eilers, Jens; Illes, Peter; Gerevich, Zoltan (Neuropharmacology)
Long-term depression (LTD) is a form of synaptic plasticity that may contribute to information storage in the central nervous system. Here we report that LTD can be elicited in layer 5 pyramidal neurons of the rat prefrontal cortex by pairing low frequency stimulation with a modest postsynaptic depolarization. The induction of LTD required the activation of both metabotropic glutamate receptors of the mGlu1 subtype and voltage-sensitive Ca(2+) channels (VSCCs) of the T/R, P/Q and N types, leading to the stimulation of intracellular inositol trisphosphate (IP3) receptors by IP3 and Ca(2+). The subsequent release of Ca(2+) from intracellular stores activated the protein phosphatase cascade involving calcineurin and protein phosphatase 1. The activation of purinergic P2Y(1) receptors blocked LTD. This effect was prevented by P2Y(1) receptor antagonists and was absent in mice lacking P2Y(1) but not P2Y(2) receptors. We also found that activation of P2Y(1) receptors inhibits Ca(2+) transients via VSCCs in the apical dendrites and spines of pyramidal neurons. In addition, we show that the release of ATP under hypoxia is able to inhibit LTD by acting on postsynaptic P2Y(1) receptors. In conclusion, these data suggest that the reduction of Ca(2+) influx via VSCCs caused by the activation of P2Y(1) receptors by ATP is the possible mechanism for the inhibition of LTD in prefrontal cortex.

Lumpability abstractions of rule-based systems - Feret, Jérôme; Henzinger, Thomas A; Koeppl, Heinz; Petrov, Tatjana
The induction of a signaling pathway is characterized by transient complex formation and mutual posttranslational modification of proteins. To faithfully capture this combinatorial process in a math- ematical model is an important challenge in systems biology. Exploiting the limited context on which most binding and modification events are conditioned, attempts have been made to reduce the com- binatorial complexity by quotienting the reachable set of molecular species, into species aggregates while preserving the deterministic semantics of the thermodynamic limit. Recently we proposed a quotienting that also preserves the stochastic semantics and that is complete in the sense that the semantics of individual species can be recovered from the aggregate semantics. In this paper we prove that this quotienting yields a sufficient condition for weak lumpability and that it gives rise to a backward Markov bisimulation between the original and aggregated transition system. We illustrate the framework on a case study of the EGF/insulin receptor crosstalk.

Local statistics in natural scenes predict the saliency of synthetic textures - Gasper Tkacik; Prentice, Jason S; Victor,Jonathan D; Balasubramanian, Vijay (PNAS)
The visual system is challenged with extracting and representing behaviorally relevant information contained in natural inputs of great complexity and detail. This task begins in the sensory periphery: retinal receptive fields and circuits are matched to the first and second-order statistical structure of natural inputs. This matching enables the retina to remove stimulus components that are predictable (and therefore uninformative), and primarily transmit what is unpredictable (and therefore informative). Here we show that this design principle applies to more complex aspects of natural scenes, and to central visual processing. We do this by classifying high-order statistics of natural scenes according to whether they are uninformative vs. informative. We find that the uninformative ones are perceptually nonsalient, while the informative ones are highly salient, and correspond to previously identified perceptual mechanisms whose neural basis is likely central. Our results suggest that the principle of efficient coding not only accounts for filtering operations in the sensory periphery, but also shapes subsequent stages of sensory processing that are sensitive to high-order image statistics.

Optimal population coding by noisy spiking neurons - Gasper Tkacik; Prentice, Jason S; Balasubramanian, Vijay; Schneidman, Elad (PNAS)
In retina and in cortical slice the collective response of spiking neural populations is well described by "maximum-entropy" models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete–stimulus decorrelation, error correction, and independent encoding. These functions reflected a trade-off between efficient consumption of finite neural bandwidth and the use of redundancy to mitigate noise. Spontaneous activity in the optimal network reflected stimulus-induced activity patterns, and single-neuron response variability overestimated network noise. Our analysis suggests that rather than having a single coding principle hardwired in their architecture, networks in the brain should adapt their function to changing noise and stimulus correlations.

Optimizing information flow in small genetic networks. II. Feed-forward interactions - Walczak, Aleksandra M; Gasper Tkacik; Bialek, William S (Physical Review E Statistical Nonlinear and Soft Matter Physics)
Central to the functioning of a living cell is its ability to control the readout or expression of information encoded in the genome. In many cases, a single transcription factor protein activates or represses the expression of many genes. As the concentration of the transcription factor varies, the target genes thus undergo correlated changes, and this redundancy limits the ability of the cell to transmit information about input signals. We explore how interactions among the target genes can reduce this redundancy and optimize information transmission. Our discussion builds on recent work [Tkacik, Phys. Rev. E 80, 031920 (2009)], and there are connections to much earlier work on the role of lateral inhibition in enhancing the efficiency of information transmission in neural circuits; for simplicity we consider here the case where the interactions have a feed forward structure, with no loops. Even with this limitation, the networks that optimize information transmission have a structure reminiscent of the networks found in real biological systems.

From statistical mechanics to information theory: understanding biophysical information-processing systems - Gasper Tkacik (ArXiv)
These are notes for a set of 7 two-hour lectures given at the 2010 Summer School on Quantitative Evolutionary and Comparative Genomics at OIST, Okinawa, Japan. The emphasis is on understanding how biological systems process information. We take a physicist's approach of looking for simple phenomenological descriptions that can address the questions of biological function without necessarily modeling all (mostly unknown) microscopic details; the example that is developed throughout the notes is transcriptional regulation in genetic regulatory networks. We present tools from information theory and statistical physics that can be used to analyze noisy nonlinear biological networks, and build generative and predictive models of regulatory processes.

Interdependence of behavioural variability and response to small stimuli in bacteria - Park, Heungwon; Pontius, William; Calin Guet; Marko, John F; Emonet,Thierry; Cluzel,Philippe (Nature)
The chemotaxis signalling network in Escherichia coli that controls the locomotion of bacteria is a classic model system for signal transduction1, 2. This pathway modulates the behaviour of flagellar motors to propel bacteria towards sources of chemical attractants. Although this system relaxes to a steady state in response to environmental changes, the signalling events within the chemotaxis network are noisy and cause large temporal variations of the motor behaviour even in the absence of stimulus3. That the same signalling network governs both behavioural variability and cellular response raises the question of whether these two traits are independent. Here, we experimentally establish a fluctuation–response relationship in the chemotaxis system of living bacteria. Using this relationship, we demonstrate the possibility of inferring the cellular response from the behavioural variability measured before stimulus. In monitoring the pre- and post-stimulus switching behaviour of individual bacterial motors, we found that variability scales linearly with the response time for different functioning states of the cell. This study highlights that the fundamental relationship between fluctuation and response is not constrained to physical systems at thermodynamic equilibrium4 but is extensible to living cells5. Such a relationship not only implies that behavioural variability and cellular response can be coupled traits, but it also provides a general framework within which we can examine how the selection of a network design shapes this interdependence

Fine-tuning of chemotactic response in E. coli determined by high-throughput capillary assay - Park, Heungwon; Calin Guet; Emonet,Thierry; Cluzel,Philippe (Current Microbiology)
In E. coli, chemotactic behavior exhibits perfect adaptation that is robust to changes in the intracellular concentration of the chemotactic proteins, such as CheR and CheB. However, the robustness of the perfect adaptation does not explicitly imply a robust chemotactic response. Previous studies on the robustness of the chemotactic response relied on swarming assays, which can be confounded by processes besides chemotaxis, such as cellular growth and depletion of nutrients. Here, using a high-throughput capillary assay that eliminates the effects of growth, we experimentally studied how the chemotactic response depends on the relative concentration of the chemotactic proteins. We simultaneously measured both the chemotactic response of E. coli cells to L: -aspartate and the concentrations of YFP-CheR and CheB-CFP fusion proteins. We found that the chemotactic response is fine-tuned to a specific ratio of [CheR]/[CheB] with a maximum response comparable to the chemotactic response of wild-type behavior. In contrast to adaptation in chemotaxis, that is robust and exact, capillary assays revealed that the chemotactic response in swimming bacteria is fined-tuned to wild-type level of the [CheR]/[CheB] ratio.

Physics-inspired topology changes for thin fluid features - Wojtan, Christopher J; Thürey, Nils; Gross, Markus; Turk, Greg (ACM Transactions on Graphics)
We propose a mesh-based surface tracking method for fluid animation that both preserves fine surface details and robustly adjusts the topology of the surface in the presence of arbitrarily thin features like sheets and strands. We replace traditional re-sampling methods with a convex hull method for connecting surface features during topological changes. This technique permits arbitrarily thin fluid features with minimal re-sampling errors by reusing points from the original surface. We further reduce re-sampling artifacts with a subdivision-based mesh-stitching algorithm, and we use a higher order interpolating subdivision scheme to determine the location of any newly-created vertices. The resulting algorithm efficiently produces detailed fluid surfaces with arbitrarily thin features while maintaining a consistent topology with the underlying fluid simulation.

Fluid simulation with articulated bodies - Kwatra, Nipun; Wojtan, Christopher J; Carlson, Mark; Essa, Irfan; Mucha, Peter; Turk, Greg (IEEE Transactions on Visualization and Computer Graphics)
We present an algorithm for creating realistic animations of characters that are swimming through fluids. Our approach combines dynamic simulation with data-driven kinematic motions (motion capture data) to produce realistic animation in a fluid. The interaction of the articulated body with the fluid is performed by incorporating joint constraints with rigid animation and by extending a solid/fluid coupling method to handle articulated chains. Our solver takes as input the current state of the simulation and calculates the angular and linear accelerations of the connected bodies needed to match a particular motion sequence for the articulated body. These accelerations are used to estimate the forces and torques that are then applied to each joint. Based on this approach, we demonstrate simulated swimming results for a variety of different strokes, including crawl, backstroke, breaststroke, and butterfly. The ability to have articulated bodies interact with fluids also allows us to generate simulations of simple water creatures that are driven by simple controllers.

A multiscale approach to mesh-based surface tension flows - Thürey, Nils; Wojtan, Christopher J; Gross, Markus; Turk, Greg (ACM Transactions on Graphics)
We present an approach to simulate flows driven by surface tension based on triangle meshes. Our method consists of two simulation layers: the first layer is an Eulerian method for simulating surface tension forces that is free from typical strict time step constraints. The second simulation layer is a Lagrangian finite element method that simulates sub-grid scale wave details on the fluid surface. The surface wave simulation employs an unconditionally stable, symplectic time integration method that allows for a high propagation speed due to strong surface tension. Our approach can naturally separate the grid-and sub-grid scales based on a volumepreserving mean curvature flow. As our model for the sub-grid dynamics enforces a local conservation of mass, it leads to realistic pinch off and merging effects. In addition to this method for simulating dynamic surface tension effects, we also present an efficient non-oscillatory approximation for capturing damped surface tension behavior. These approaches allow us to efficiently simulate complex phenomena associated with strong surface tension, such as Rayleigh-Plateau instabilities and crown splashes, in a short amount of time.