Neurons and glia are functionally organized into circuits and higher-order structures via synaptic connectivity, well-orchestrated molecular signaling, and activity-dependent refinement. Such organization allows the precise information processing required for complex behaviors. Disruption of nervous systems by genetic deficiency or events such as trauma or environmental exposure may produce a diseased state in which certain aspects of inter-neuron signaling are impaired. Optical imaging techniques allow the direct visualization of individual neurons in a circuit environment. Imaging probes specific for given biomolecules may help elucidate their contribution to proper circuit function. Genetically encoded sensors can visualize trafficking of particular molecules in defined neuronal populations, non-invasively in intact brain or reduced preparations. Sensor analysis in healthy and diseased brains may reveal important differences and shed light on the development and progression of nervous system disorders. We review the field of genetically encoded sensors for molecules and cellular events, and their potential applicability to the study of nervous system disease.

Many perceptual processes and neural computations, such as speech recognition, motor control and learning, depend on the ability to measure and mark the passage of time. However, the processes that make such temporal judgements possible are unknown. A number of different hypothetical mechanisms have been advanced, all of which depend on the known, temporally predictable evolution of a neural or psychological state, possibly through oscillations or the gradual decay of a memory trace. Alternatively, judgements of elapsed time might be based on observations of temporally structured, but stochastic processes. Such processes need not be specific to the sense of time; typical neural and sensory processes contain at least some statistical structure across a range of time scales. Here, we investigate the statistical properties of an estimator of elapsed time which is based on a simple family of stochastic process.

We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to the subsequent linear model. We describe algorithms that estimate both the input nonlinearity and the linear weights simultaneously; and present techniques to regularise and quantify uncertainty in the estimates. In a second approach, the model is generalized to allow a different nonlinear transform of the stimulus value at each lag-time. Although more general, this model is algorithmically more straightforward to fit. However, it has many more degrees of freedom than the first approach, thus requiring more data for accurate estimation. We test the feasibility of these methods on synthetic data, and on responses from a neuron in rodent barrel cortex. The models are shown to predict responses to novel data accurately, and to recover several important neuronal response properties.

Probing chromatin structure with nucleases is a well-established method for determining the accessibility of DNA to gene regulatory proteins and measuring competency for transcription. A hallmark of many silent genes is the presence of translationally positioned nucleosomes over their promoter regions, which can be inferred by the sensitivity of the underlying DNA to nucleases, particularly micrococcal nuclease. The quality of this data is highly dependent upon the nuclear preparation, especially if the digestion products are analyzed by high-resolution detection methods such as reiterative primer extension. Here we describe a method to isolate highly purified nuclei from the budding yeast Saccharomyces cerevisiae and the use of micrococcal nuclease to map the positions of nucleosomes at the RNR3 gene. Nuclei isolated by this procedure are competent for many of the commonly used chromatin mapping and detection procedures.

Juvenile hormone (JH) given at pupariation inhibits bristle formation and causes pupal cuticle formation in the abdomen of Drosophila melanogaster due to its prolongation of expression of the transcription factor Broad (BR). In a microarray analysis of JH-induced gene expression in abdominal integument, we found that Krüppel homolog 1 (Kr-h1) was up-regulated during most of adult development. Quantitative real-time PCR analyses showed that Kr-h1 up-regulation began at 10h after puparium formation (APF), and Kr-h1 up-regulation occurred in imaginal epidermal cells, persisting larval muscles, and larval oenocytes. Ectopic expression of Kr-h1 in abdominal epidermis using T155-Gal4 to drive UAS-Kr-h1 resulted in missing or short bristles in the dorsal midline. This phenotype was similar to that seen after a low dose of JH or after misexpression of br between 21 and 30 h APF. Ectopic expression of Kr-h1 prolonged the expression of BR protein in the pleura and the dorsal tergite. No Kr-h1 was seen after misexpression of br. Thus, Kr-h1 mediates some of the JH signaling in the adult abdominal epidermis and is upstream of br in this pathway. We also show for the first time that the JH-mediated maintenance of br expression in this epidermis is patterned and that JH delays the fusion of the imaginal cells and the disappearance of Dpp in the dorsal midline.

Time invariant description of synaptic connectivity in cortical circuits may be precluded by the ongoing growth and retraction of dendritic spines accompanied by the formation and elimination of synapses. On the other hand, the spatial arrangement of axonal and dendritic branches appears stable. This suggests that an invariant description of connectivity can be cast in terms of potential synapses, which are locations in the neuropil where an axon branch of one neuron is proximal to a dendritic branch of another neuron. In this paper, we attempt to reconstruct the potential connectivity in local cortical circuits of the cat primary visual cortex (V1). Based on multiple single-neuron reconstructions of axonal and dendritic arbors in 3 dimensions, we evaluate the expected number of potential synapses and the probability of potential connectivity among excitatory (pyramidal and spiny stellate) neurons and inhibitory basket cells. The results provide a quantitative description of structural organization of local cortical circuits. For excitatory neurons from different cortical layers, we compute local domains, which contain their potentially pre- and postsynaptic excitatory partners. These domains have columnar shapes with laminar specific radii and are roughly of the size of the ocular dominance column. Therefore, connections between most excitatory neurons in the ocular dominance column can be implemented by local synaptogenesis. Structural connectivity involving inhibitory basket cells is generally weaker than excitatory connectivity. Here, only nearby neurons are capable of establishing more than one potential synapse, implying that within the ocular dominance column these connections have more limited potential for circuit remodeling.

We consider the problem of establishing visual correspondences in a distributed and rate-efficient fashion by broadcasting compact descriptors. Establishing visual correspondences is a critical task before other vision tasks can be performed in a camera network. We use coarsely quantized random projections of descriptors to build binary hashes, and use the hamming distance between binary hashes as a matching criterion. In this work, we show that the hamming distance between the binary hashes has a binomial distribution, with parameters that are a function of the number of random projections and the euclidean distance between the original descriptors. We present experimental results that verify our result, and show that for the task of finding visual correspondences, sending binary hashes is more rate-efficient than prior approaches.

Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenPept and on sequences from selected metagenomics projects. Pfam is available on the web from the consortium members using a new, consistent and improved website design in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/), as well as from mirror sites in France (http://pfam.jouy.inra.fr/) and South Korea (http://pfam.ccbb.re.kr/).

The conditional expression of hairpin constructs in Drosophila melanogaster has emerged in recent years as a method of choice in functional genomic studies. To date, upstream activating site-driven RNA interference constructs have been inserted into the genome randomly using P-element-mediated transformation, which can result in false negatives due to variable expression. To avoid this problem, we have developed a transgenic RNA interference vector based on the phiC31 site-specific integration method.