We introduce a method based on machine vision for automatically measuring aggression and courtship in Drosophila melanogaster. The genetic and neural circuit bases of these innate social behaviors are poorly understood. High-throughput behavioral screening in this genetically tractable model organism is a potentially powerful approach, but it is currently very laborious. Our system monitors interacting pairs of flies and computes their location, orientation and wing posture. These features are used for detecting behaviors exhibited during aggression and courtship. Among these, wing threat, lunging and tussling are specific to aggression; circling, wing extension (courtship ’song’) and copulation are specific to courtship; locomotion and chasing are common to both. Ethograms may be constructed automatically from these measurements, saving considerable time and effort. This technology should enable large-scale screens for genes and neural circuits controlling courtship and aggression.

This work presents three different methods for automatic detection of anatomical landmarks in CT data, namely for the left and right anterior superior iliac spines and the pubic symphysis. The methods exhibit different degrees of generality in terms of portability to other anatomical landmarks and require a different amount of training data. The ſrst method is problem-speciſc and is based on the convex hull of the pelvis. Method two is a more generic approach based on a statistical shape model including the landmarks of interest for every training shape. With our third method we present the most generic approach, where only a small set of training landmarks is required. Those landmarks are transferred to the patient speciſc geometry based on Mean Value Coordinates (MVCs). The methods work on surfaces of the pelvis that need to be extracted beforehand. We perform this geometry reconstruction with our previously introduced fully automatic segmentation framework for the pelvic bones. With a focus on the accuracy of our novel MVC-based approach, we evaluate and compare our methods on 100 clinical CT datasets, for which gold standard landmarks were deſned manually by multiple observers.

In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivo phase-contrast microscopy. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in each frame. In addition to intensity features, region homogeneity measure and class uncertainty are also applied in this detection technique. To track cells with complex motion, a novel matching gain measure is introduced to cope with the challenges, particularly the presence of low-contrast boundary, the variations of appearance, and the frequent overlapping and occlusion. For multicell tracking over time, an optimal matching strategy is introduced to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually determined trajectories, as well as those obtained from existing tracking schemes. The stability of the algorithm with different parameter values is also analyzed and discussed.

We describe an approach for automation of the process of reconstruction of neural tissue from serial section transmission electron micrographs. Such reconstructions require 3D segmentation of individual neuronal processes (axons and dendrites) performed in densely packed neuropil. We first detect neuronal cell profiles in each image in a stack of serial micrographs with multi-scale ridge detector. Short breaks in detected boundaries are interpolated using anisotropic contour completion formulated in fuzzy-logic framework. Detected profiles from adjacent sections are linked together based on cues such as shape similarity and image texture. Thus obtained 3D segmentation is validated by human operators in computer-guided proofreading process. Our approach makes possible reconstructions of neural tissue at final rate of about 5 microm3/manh, as determined primarily by the speed of proofreading. To date we have applied this approach to reconstruct few blocks of neural tissue from different regions of rat brain totaling over 1000microm3, and used these to evaluate reconstruction speed, quality, error rates, and presence of ambiguous locations in neuropil ssTEM imaging data.

Single Plane Illumination Microscopy (SPIM) is an emerging microscopic technique that enables live imaging of large biological specimens in their entirety. By imaging the biological sample from multiple angles, SPIM has the potential to achieve isotropic resolution throughout relatively large biological specimens. For every angle, however, only a shallow section of the specimen is imaged with high resolution, whereas deeper regions appear increasingly blurred. Existing intensity-based registration techniques still struggle to robustly and accurately align images that are characterized by limited overlap and/or heavy blurring. To be able to register such images, we add sub-resolution fluorescent beads to the rigid agarose medium in which the imaged specimen is embedded. For each segmented bead, we store the relative location of its n nearest neighbors in image space as rotation-invariant geometric local descriptors. Corresponding beads between overlapping images are identified by matching these descriptors. The bead correspondences are used to simultaneously estimate the globally optimal transformation for each individual image. The final output image is created by combining all images in an angle-independent output space, using volume injection and local content-based weighting of contributing images. We demonstrate the performance of our approach on data acquired from living embryos of Drosophila and fixed adult C.elegans worms. Bead-based registration outperformed intensity-based registration in terms of computation speed by two orders of magnitude while producing bead registration errors below 1 μm (about 1 pixel). It, therefore, provides an ideal tool for processing of long term time-lapse recordings of embryonic development consisting of hundreds of time points.

Bursts of spikes triggered by sensory stimuli in midbrain dopamine neurons evoke phasic release of dopamine in target brain areas, driving reward-based reinforcement learning and goal-directed behavior. NMDA-type glutamate receptors (NMDARs) play a critical role in the generation of these bursts. Here we report LTP of NMDAR-mediated excitatory transmission onto dopamine neurons in the substantia nigra. Induction of LTP requires burst-evoked Ca2+ signals amplified by preceding metabotropic neurotransmitter inputs in addition to the activation of NMDARs themselves. PKA activity gates LTP induction by regulating the magnitude of Ca2+ signal amplification. This form of plasticity is associative, input specific, reversible, and depends on the relative timing of synaptic input and postsynaptic bursting in a manner analogous to the timing rule for cue-reward learning paradigms in behaving animals. NMDAR plasticity might thus represent a potential neural substrate for conditioned dopamine neuron burst responses to environmental stimuli acquired during reward-based learning.

In a genetic screen for active zone defective mutants in Caenorhabditis elegans, we isolated a loss-of-function allele of unc-7, a gene encoding an innexin/pannexin family gap junction protein. Innexin UNC-7 regulates the size and distribution of active zones at C. elegans neuromuscular junctions. Loss-of-function mutations in another innexin, UNC-9, cause similar active zone defects as unc-7 mutants. In addition to presumptive gap junction localizations, both UNC-7 and UNC-9 are also localized perisynaptically throughout development and required in presynaptic neurons to regulate active zone differentiation. Our mosaic analyses, electron microscopy, as well as expression studies suggest a novel and likely nonjunctional role of specific innexins in active zone differentiation in addition to gap junction formations.

Administration of aminoglycoside antibiotics can precipitate sudden, profound bouts of weakness that have been attributed to block of presynaptic voltage-activated calcium channels (VACCs) and failure of neuromuscular transmission. This serious adverse drug reaction is more likely in neuromuscular diseases such as myasthenia gravis. The relatively low affinity of VACC for aminoglycosides prompted us to explore alternative mechanisms. We hypothesized that the presynaptic Ca(2+)-sensing receptor (CaSR) may contribute to aminoglycoside-induced weakness due to its role in modulating synaptic transmission and its sensitivity to aminoglycosides in heterologous expression systems. We have previously shown that presynaptic CaSR controls a non-selective cation channel (NSCC) that regulates nerve terminal excitability and transmitter release. Using direct, electrophysiological recording, we report that neuronal VACCs are inhibited by neomycin (IC(50) 830 +/- 110 microM) at a much lower affinity than CaSR-modulated NSCC currents recorded from acutely isolated presynaptic terminals (synaptosomes; IC(50) 20 +/- 1 microM). Thus, at clinically relevant concentrations, aminoglycoside-induced weakness is likely precipitated by enhanced CaSR activation and subsequent decrease in terminal excitability rather than through direct inhibition of VACCs themselves.

SUMMARY: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy. AVAILABILITY: http://fly.mpi-cbg.de/catmaid.