Determining how neurons transform synaptic input and encode information in action potential (AP) firing output is required for understanding dendritic integration, neural transforms and encoding. Limitations in the speed of imaging 3D volumes of brain encompassing complex dendritic arbors using conventional galvanometer mirror-based laser-scanning microscopy has hampered fully capturing fluorescent sensors of activity throughout an individual neuron's entire complement of synaptic inputs and somatic APs. To address this problem, we have developed a two-photon microscope that achieves high-speed scanning by employing inertia-free acousto-optic deflectors (AODs) for laser beam positioning, enabling random-access sampling of hundreds to thousands of points-of-interest restricted to a predetermined neuronal structure, avoiding wasted scanning of surrounding extracellular tissue. This system is capable of comprehensive imaging of the activity of single neurons within the intact and awake vertebrate brain. Here, we demonstrate imaging of tectal neurons within the brains of albino tadpoles labeled using single-cell electroporation for expression of a red space-filling fluorophore to determine dendritic arbor morphology, and either the calcium sensor jGCaMP7s or the glutamate sensor iGluSnFR as indicators of neural activity. Using discrete, point-of-interest scanning we achieve sampling rates of 3 Hz for saturation sampling of entire arbors at 2 μm resolution, 6 Hz for sequentially sampling 3 volumes encompassing the dendritic arbor and soma, and 200-250 Hz for scanning individual planes through the dendritic arbor. This system allows investigations of sensory-evoked information input-output relationships of neurons within the intact and awake brain.

Aphids exhibit unique attributes, such as polyphenisms and specialized cells to house endosymbionts, that make them an interesting system for studies at the interface of ecology, evolution and development. Here we present a comprehensive characterization of the developmental genes in the pea aphid, Acyrthosiphon pisum, and compare our results to other sequenced insects. We investigated genes involved in fundamental developmental processes such as establishment of the body plan and organogenesis, focusing on transcription factors and components of signalling pathways. We found that most developmental genes were well conserved in the pea aphid, although many lineage-specific gene duplications and gene losses have occurred in several gene families. In particular, genetic components of transforming growth factor beta (TGFbeta) Wnt, JAK/STAT (Janus kinase/signal transducer and activator of transcription) and EGF (Epidermal Growth Factor) pathways appear to have been significantly modified in the pea aphid.

Inner ear cochlear spiral ganglion neurons (SGNs) transmit auditory information to the brainstem. Recent single cell RNA-Seq studies have revealed heterogeneities within SGNs. Nonetheless, much remains unknown about the transcriptome of SGNs, especially which genes are specifically expressed in SGNs. To address these questions we needed a deeper and broader gene coverage than that in previous studies. We performed bulk RNA-Seq on mouse SGNs at five ages, and on two reference cell types (hair cells and glia). Their transcriptome comparison identified genes previously unknown to be specifically expressed in SGNs. To validate our dataset and provide useful genetic tools for this research field, we generated two knockin mouse strains: and . Our comprehensive analysis confirmed the SGN-selective expression of the candidate genes, testifying to the quality of our transcriptome data. These two mouse strains can be used to temporally label SGNs or to sort them.

We present a compressed domain scheme that is able to recognize and localize actions in real-time. The recognition problem is posed as performing a video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a large action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our classification results compare favorably with existing methods at only a fraction of their computational cost.

SIGNIFICANCE: one-photon fluorescence imaging of calcium and voltage indicators expressed in neurons enables noninvasive recordings of neural activity with submillisecond precision. However, data acquisition speed is limited by the frame rate of cameras.

AIM: We developed a compressive streak fluorescence microscope to record fluorescence in individual neurons at high speeds ( frames per second) exceeding the nominal frame rate of the camera by trading off spatial pixels for temporal resolution.

APPROACH: Our microscope leverages a digital micromirror device for targeted illumination, a galvo mirror for temporal scanning, and a ridge regression algorithm for fast computational reconstruction of fluorescence traces with high temporal resolution.

RESULTS: In simulations, the ridge regression algorithm reconstructs traces of high temporal resolution with limited signal loss. Validation experiments with fluorescent beads and experiments in larval zebrafish demonstrate accurate reconstruction with a data compression ratio of 10 and accurate recordings of neural activity with 200- to 400-Hz sampling speeds.

CONCLUSIONS: Our compressive microscopy enables new experimental capabilities to monitor activity at a sampling speed that outpaces the nominal frame rate of the camera.

Recent advances in computation-based protein engineering offer opportunities to introduce or modify the biophysical characteristics of proteins at will. The power of computational design comes from the ability to surpass the combinatorial and physical limitations inherent to laboratory-based high-throughput or trial-and-error methods. As a result, modifications that require significant changes to the amino acid sequence of a protein are now accessible to the protein engineering community. Hydrophobic cores of proteins have been repacked to increase their thermostability. Binding sites in proteins have been modified to increase affinity or alter specificity for proteins, peptides, and small molecules. Enzymes have been designed de novo. Non-natural protein folds have been created. For the most part, these achievements have been applied to proteins that make good model systems in academic settings. How can these computational methods be applied to therapeutically relevant proteins? This review will focus on the ground-breaking achievements of computation-based protein engineering and on recent applications of rational design to improve therapeutic proteins. - See more at: http://www.eurekaselect.com/90585/article/computation-based-design-and-engineering-protein-and-antibody-therapeutics#sthash.kSt3UaNE.dpuf

In this review, we discuss the emerging field of computational behavioral analysis-the use of modern methods from computer science and engineering to quantitatively measure animal behavior. We discuss aspects of experiment design important to both obtaining biologically relevant behavioral data and enabling the use of machine vision and learning techniques for automation. These two goals are often in conflict. Restraining or restricting the environment of the animal can simplify automatic behavior quantification, but it can also degrade the quality or alter important aspects of behavior. To enable biologists to design experiments to obtain better behavioral measurements, and computer scientists to pinpoint fruitful directions for algorithm improvement, we review known effects of artificial manipulation of the animal on behavior. We also review machine vision and learning techniques for tracking, feature extraction, automated behavior classification, and automated behavior discovery, the assumptions they make, and the types of data they work best with. Expected final online publication date for the Annual Review of Neuroscience Volume 39 is July 08, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.

We describe a general computational method for designing proteins that self-assemble to a desired symmetric architecture. Protein building blocks are docked together symmetrically to identify complementary packing arrangements, and low-energy protein-protein interfaces are then designed between the building blocks in order to drive self-assembly. We used trimeric protein building blocks to design a 24-subunit, 13-nm diameter complex with octahedral symmetry and a 12-subunit, 11-nm diameter complex with tetrahedral symmetry. The designed proteins assembled to the desired oligomeric states in solution, and the crystal structures of the complexes revealed that the resulting materials closely match the design models. The method can be used to design a wide variety of self-assembling protein nanomaterials.

A key step toward understanding a metagenomics data set is the identification of functional sequence elements within it, such as protein coding genes and structural RNAs. Relative to protein coding genes, structural RNAs are more difficult to identify because of their reduced alphabet size, lack of open reading frames, and short length. Infernal is a software package that implements "covariance models" (CMs) for RNA homology search, which harness both sequence and structural conservation when searching for RNA homologs. Thanks to the added statistical signal inherent in the secondary structure conservation of many RNA families, Infernal is more powerful than sequence-only based methods such as BLAST and profile HMMs. Together with the Rfam database of CMs, Infernal is a useful tool for identifying RNAs in metagenomics data sets.