Energy normalization of TV viewed optical correlation (automated correlation plane analyzer for an optical processor)

An automatic correlation plane processor that can rapidly acquire, identify, and locate the autocorrelation outputs of a bank of multiple optical matched filters is described. The read-only memory (ROM) stored digital silhouette of each image associated with each matched filter allows TV video to be used to collect image energy to provide accurate normalization of autocorrelations. The resulting normalized autocorrelations are independent of the illumination of the matched input. Deviation from unity of a normalized correlation can be used as a confidence measure of correct image identification. Analog preprocessing circuits permit digital conversion and random access memory (RAM) storage of those video signals with the correct amplitude, pulse width, rising slope, and falling slope. TV synchronized addressing of 3 RAMs permits on-line storage of: (1) the maximum unnormalized amplitude, (2) the image x location, and (3) the image y location of the output of each of up to 99 matched filters. A fourth RAM stores all normalized correlations. A normalization approach, normalization for cross correlations, a system's description with block diagrams, and system's applications are discussed

Preservation of glaciochemical time-series in snow and ice from the Penny Ice Cap, Baffin Island

A detailed investigation of major ion concentrations of snow and ice in the summit region of Penny Ice Cap (PIC) was performed to determine the effects of summer melt on the glaciochemical time-series. While ion migration due to meltwater percolation makes it difficult to confidently count annual layers in the glaciochemical profiles, time-series of these parameters do show good structure and a strong one year spectral component, suggesting that annual to biannual signals are preserved in PIC glaciochemical records

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

We propose a machine learning method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at reduced computational cost. A linear-response model is used as a first step and symmetry-adapted machine learning is employed for the higher-order contributions as a second step. We investigate the performance of the approach for several systems including molecules and extended solids. The method can reduce training set sizes required for accurate dielectric properties and Raman spectra in comparison to a single-step machine learning approach

Dynamic tight binding for large-scale electronic-structure calculations of semiconductors at finite temperatures

Calculating the electronic structure of materials at finite temperatures is important for rationalizing their physical properties and assessing their technological capabilities. However, finite-temperature calculations typically require large system sizes or long simulation times. This is challenging for non-empirical theoretical methods because the involved bottleneck of performing many first-principles calculations can pose a steep computational barrier for larger systems. While machine-learning molecular dynamics enables large-scale/long-time simulations of the structural properties, the difficulty of computing in particular the electronic structure of large and disordered materials still remains. In this work, we suggest an adaptation of the tight-binding formalism which allows for computationally efficient calculations of temperature-dependent properties of semiconductors. Our dynamic tight-binding approach utilizes hybrid-orbital basis functions and a modeling of the distance dependence of matrix elements via numerical integration of atomic orbitals. We show that these design choices lead to a dynamic tight-binding model with a minimal amount of parameters which are straightforwardly optimized using density functional theory. Combining dynamic tight-binding with machine learning molecular dynamics and hybrid density functional theory, we find that it accurately describes finite-temperature electronic properties in comparison to experiment for the prototypical semiconductor gallium-arsenide

Overlapping functions of the cell adhesion molecules Nr-CAM and L1 in cerebellar granule cell development

The structurally related cell adhesion molecules L1 and Nr-CAM have overlapping expression patterns in cerebellar granule cells. Here we analyzed their involvement in granule cell development using mutant mice. Nr-CAM–deficient cerebellar granule cells failed to extend neurites in vitro on contactin, a known ligand for Nr-CAM expressed in the cerebellum, confirming that these mice are functionally null for Nr-CAM. In vivo, Nr-CAM–null cerebella did not exhibit obvious histological defects, although a mild size reduction of several lobes was observed, most notably lobes IV and V in the vermis. Mice deficient for both L1 and Nr-CAM exhibited severe cerebellar folial defects and a reduction in the thickness of the inner granule cell layer. Additionally, anti-L1 antibodies specifically disrupted survival and maintenance of Nr-CAM–deficient granule cells in cerebellar cultures treated with antibodies. The combined results indicate that Nr-CAM and L1 play a role in cerebellar granule cell development, and suggest that closely related molecules in the L1 family have overlapping functions

Lysosomal Acid Lipase Hydrolyzes Retinyl Ester and Affects Retinoid Turnover

Lysosomal acid lipase (LAL) is essential for the clearance of endocytosed cholesteryl ester and triglyceride-rich chylomicron remnants. Humans and mice with defective or absent LAL activity accumulate large amounts of cholesteryl esters and triglycerides in multiple tissues. Although chylomicrons also contain retinyl esters (REs), a role of LAL in the clearance of endocytosed REs has not been reported. In this study, we found that murine LAL exhibits RE hydrolase activity. Pharmacological inhibition of LAL in the human hepatocyte cell line HepG2, incubated with chylomicrons, led to increased accumulation of REs in endosomal/lysosomal fractions. Furthermore, pharmacological inhibition or genetic ablation of LAL in murine liver largely reduced in vitro acid RE hydrolase activity. Interestingly, LAL-deficient mice exhibited increased RE content in the duodenum and jejunum but decreased RE content in the liver. Furthermore, LAL-deficient mice challenged with RE gavage exhibited largely reduced post-prandial circulating RE content, indicating that LAL is required for efficient nutritional vitamin A availability. In summary, our results indicate that LAL is the major acid RE hydrolase and required for functional retinoid homeostasis

Nitration of Hsp90 induces cell death

Oxidative stress is a widely recognized cause of cell death associated with neurodegeneration, inflammation, and aging. Tyrosine nitration in these conditions has been reported extensively, but whether tyrosine nitration is a marker or plays a role in the cell-death processes was unknown. Here, we show that nitration of a single tyrosine residue on a small proportion of 90-kDa heat-shock protein (Hsp90), is sufficient to induce motor neuron death by the P2X7 receptor-dependent activation of the Fas pathway. Nitrotyrosine at position 33 or 56 stimulates a toxic gain of function that turns Hsp90 into a toxic protein. Using an antibody that recognizes the nitrated Hsp90, we found immunoreactivity in motor neurons of patients with amyotrophic lateral sclerosis, in an animal model of amyotrophic lateral sclerosis, and after experimental spinal cord injury. Our findings reveal that cell death can be triggered by nitration of a single protein and highlight nitrated Hsp90 as a potential target for the development of effective therapies for a large number of pathologies