Computer used to program numerically controlled milling machine

Computer program automatically directs a numerically controlled milling machine through a series of cutting and trimming actions. It accepts engineering data points, passes smooth curve segments through the points, breaks the resulting curves into a series of closely spaced points, and transforms these points into the form required by the mechanism

The neural basis of centre-surround interactions in visual motion processing

Perception of a moving visual stimulus can be suppressed or enhanced by surrounding context in adjacent parts of the visual field. We studied the neural processes underlying such contextual modulation with fMRI. We selected motion selective regions of interest (ROI) in the occipital and parietal lobes with sufficiently well defined topography to preclude direct activation by the surround. BOLD signal in the ROIs was suppressed when surround motion direction matched central stimulus direction, and increased when it was opposite. With the exception of hMT+/V5, inserting a gap between the stimulus and the surround abolished surround modulation. This dissociation between hMT+/V5 and other motion selective regions prompted us to ask whether motion perception is closely linked to processing in hMT+/V5, or reflects the net activity across all motion selective cortex. The motion aftereffect (MAE) provided a measure of motion perception, and the same stimulus configurations that were used in the fMRI experiments served as adapters. Using a linear model, we found that the MAE was predicted more accurately by the BOLD signal in hMT+/V5 than it was by the BOLD signal in other motion selective regions. However, a substantial improvement in prediction accuracy could be achieved by using the net activity across all motion selective cortex as a predictor, suggesting the overall conclusion that visual motion perception depends upon the integration of activity across different areas of visual cortex

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

The BOLD signal and neurovascular coupling in autism

BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) is commonly used to study differences in neuronal activity between human populations. As the BOLD response is an indirect measure of neuronal activity, meaningful interpretation of differences in BOLD responses between groups relies upon a stable relationship existing between neuronal activity and the BOLD response across these groups. However, this relationship can be altered by changes in neurovascular coupling or energy consumption, which would lead to problems in identifying differences in neuronal activity. In this review, we focus on fMRI studies of people with autism, and comparisons that are made of their BOLD responses with those of control groups. We examine neurophysiological differences in autism that may alter neurovascular coupling or energy use, discuss recent studies that have used fMRI to identify differences between participants with autism and control participants, and explore experimental approaches that could help attribute between-group differences in BOLD signals to either neuronal or neurovascular factors

Bond Length - Bond Valence Relationships for Carbon - Carbon and Carbon - Oxygen Bonds

In the present study, relationships are developed for determining bond orders (also referred to as bond valences or bond numbers) from published bond lengths for carbon-carbon (C-C) and carbon-oxygen (C-O) bonds. The relationships are based on Pauling’s empirical formula s = exp((Ro-R)/b)), where s is the bond order, R is the corresponding bond length, Ro is the unit valence bond length, and b is a fitting parameter. We use a recently derived relationship for the b parameter in terms of the bonding atoms’ published atomic orbital exponents. The resulting equations were checked against published x-ray diffraction (XRD) data for 176 carbon systems with 540 published C-C bond lengths, and 50 oxygen systems having 72 published C-O bond lengths. The C-C and C-O bond length-valence relationships are shown to have sufficient applicability and accuracy for use in any bonding environment, regardless of physical state or oxidation number