Neurovascular Network Explorer 1.0: a database of 2-photon single-vessel diameter measurements with MATLAB® graphical user interface

We present a database client software—Neurovascular Network Explorer 1.0 (NNE 1.0)—that uses MATLAB® based Graphical User Interface (GUI) for interaction with a database of 2-photon single-vessel diameter measurements from our previous publication (Tian et al., 2010). These data are of particular interest for modeling the hemodynamic response. NNE 1.0 is downloaded by the user and then runs either as a MATLAB script or as a standalone program on a Windows platform. The GUI allows browsing the database according to parameters specified by the user, simple manipulation and visualization of the retrieved records (such as averaging and peak-normalization), and export of the results. Further, we provide NNE 1.0 source code. With this source code, the user can database their own experimental results, given the appropriate data structure and naming conventions, and thus share their data in a user-friendly format with other investigators. NNE 1.0 provides an example of seamless and low-cost solution for sharing of experimental data by a regular size neuroscience laboratory and may serve as a general template, facilitating dissemination of biological results and accelerating data-driven modeling approaches

Efficient non-degenerate two-photon excitation for fluorescence microscopy

Non-degenerate two-photon excitation (ND-TPE) has been explored in two-photon excitation microscopy. However, a systematic study of the efficiency of ND-TPE to guide the selection of fluorophore excitation wavelengths is missing. We measured the relative non-degenerate two-photon absorption cross-section (ND-TPACS) of several commonly used fluorophores (two fluorescent proteins and three small-molecule dyes) and generated 2-dimensional ND-TPACS spectra. We observed that the shape of a ND-TPACS spectrum follows that of the corresponding degenerate two-photon absorption cross-section (D-TPACS) spectrum, but is higher in magnitude. We found that the observed enhancements are higher than theoretical predictions.Published versio

Baseline oxygen consumption decreases with cortical depth

The cerebral cortex is organized in cortical layers that differ in their cellular density, composition, and wiring. Cortical laminar architecture is also readily revealed by staining for cytochrome oxidase—the last enzyme in the respiratory electron transport chain located in the inner mitochondrial membrane. It has been hypothesized that a high-density band of cytochrome oxidase in cortical layer IV reflects higher oxygen consumption under baseline (unstimulated) conditions. Here, we tested the above hypothesis using direct measurements of the partial pressure of O2 (pO2) in cortical tissue by means of 2-photon phosphorescence lifetime microscopy (2PLM). We revisited our previously developed method for extraction of the cerebral metabolic rate of O2 (CMRO2) based on 2-photon pO2 measurements around diving arterioles and applied this method to estimate baseline CMRO2 in awake mice across cortical layers. To our surprise, our results revealed a decrease in baseline CMRO2 from layer I to layer IV. This decrease of CMRO2 with cortical depth was paralleled by an increase in tissue oxygenation. Higher baseline oxygenation and cytochrome density in layer IV may serve as an O2 reserve during surges of neuronal activity or certain metabolically active brain states rather than reflecting baseline energy needs. Our study provides to our knowledge the first quantification of microscopically resolved CMRO2 across cortical layers as a step towards better understanding of brain energy metabolism.publishedVersio

SALL1 enforces microglia-specific DNA binding and function of SMADs to establish microglia identity

Spalt-like transcription factor 1 (SALL1) is a critical regulator of organogenesis and microglia identity. Here we demonstrate that disruption of a conserved microglia-specific super-enhancer interacting with the Sall1 promoter results in complete and specific loss of Sall1 expression in microglia. By determining the genomic binding sites of SALL1 and leveraging Sall1 enhancer knockout mice, we provide evidence for functional interactions between SALL1 and SMAD4 required for microglia-specific gene expression. SMAD4 binds directly to the Sall1 super-enhancer and is required for Sall1 expression, consistent with an evolutionarily conserved requirement of the TGFβ and SMAD homologs Dpp and Mad for cell-specific expression of Spalt in the Drosophila wing. Unexpectedly, SALL1 in turn promotes binding and function of SMAD4 at microglia-specific enhancers while simultaneously suppressing binding of SMAD4 to enhancers of genes that become inappropriately activated in enhancer knockout microglia, thereby enforcing microglia-specific functions of the TGFβ–SMAD signaling axis.</p

Image-based modeling of human gaits with higher-order statistics

Keywords: Image-based modeling and rendering, higher-order statistics, dynamic independent component analysis, human motion modeling, visual recognition, animation We present a novel approach to modeling human gaits such as walking and running. We represent the trajectories of a certain number of salient features on the human body as the output of a dynamical system driven by an unknown stochastic input. We present techniques for inferring model parameters and input signal distributions corresponding to different optimality criteria, and evaluate the corresponding models for accuracy and predictive power. In particular, we exploit the higherorder statistical information content in motion capture data to arrive at input signals with independent components. We show that human gaits synthesized from non-Gaussian inputs best capture the dynamic complexities of the original gait data.

Dynamical Models for Human Gait Synthesis

We present a novel approach to modeling human gaits such as walking and running. We represent the trajectories of a certain number of salient features on the human body as the output of a dynamical system that is made up of two subsystems, an autonomous subsystem and a subsystem driven by an unknown stochastic input. We present techniques for inferring model parameters and input signal distributions from data. In particular, we exploit the higher-order statistical information content in motion capture data to arrive at input signals with independent components. We show that we can synthesize arbitrarily long sequences of human gaits that capture the dynamic complexities and the distinct character of the original gait sequences. 1

Modeling human gaits with subtleties

We present a novel approach to modeling subtleties in human motion. We represent the trajectories of a certain number of salient features on the human body as the output of a dynamical system driven by an unknown stochastic input. We present several techniques for inferring model parameters and input signal distributions corresponding to different optimality criteria, and evaluate the corresponding models for accuracy and predictive power. In particular we exploit the higher order statistical information content in motion data to arrive at input signals with independent components and show that the human motion synthesized from non-Gaussian inputs capture best the subtle complexities of the motion data

Gait Recognition using Dynamic Affine Invariants

We present a method for recognizing classes of human gaits from video sequences. We propose a novel image based representation for human gaits. At any instance of time a gait is represented by a vector of affine invariant moments. The invariants are computed on the binary silhouettes corresponding to the moving body. We represent the time trajectories of the affine moment invariant vector as the output of a linear dynamical system driven by white noise. The problem of gait classification is then reduced to formulating distances and performing recognition in the space of linear dynamical systems. Results demonstrating the discriminate power of the proposed methods are discussed at the end