Risk of type 1 diabetes at 3 years is high for initially multiple and single Ab+ IT and multiple Ab+ NT. Genetic predisposition, age, and male sex are significant risk factors for development of Ab+ in twins.

<p>Small-scale fluctuations in vertical wind velocity, unresolved by climate and weather forecast models play a particularly important role in determining vapor and tracer fluxes, turbulence and cloud formation. Fluctuations in vertical wind velocity are challenging to represent since they depend on orography, large scale circulation features, convection and wind shear. Parameterizations developed using data retrieved at specific locations typically lack generalization and may introduce error when applied on a wide range of different conditions. Retrievals of vertical wind velocity are also difficult and subject to large uncertainty. This work develops a new data-driven, neural network representation of subgrid scale variability in vertical wind velocity. Using a novel deep learning technique, the new parameterization merges data from high-resolution global cloud resolving model simulations with high frequency Radar and Lidar retrievals.  Our method aims to reproduce observed statistics rather than fitting individual measurements. Hence it is resilient to experimental uncertainty and robust to generalization. The neural network parameterization can be driven by weather forecast and reanalysis products to make real time estimations. It is shown that the new parameterization generalizes well outside of the training data and reproduces much better the statistics of vertical wind velocity than purely data-driven models.</p>

Today's SEM experimentalist can acquire prodigious amounts of data in short amounts of time. High-stability FEG-SEMs equipped with high-throughput SDD detectors are widely available. Commercial microanalysis systems control motorized specimen stages, acquiring hundreds of spatially aligned fields of view (FOV) in an overnight or weekend SEM session [1]. The resulting mosaic of EDS datacubes can easily comprise hundreds of gigabytes.Commercial phase analysis software is optimized for rapid, automated data exploration. In real time, powerful, proprietary algorithms reduce spectral dimensions via principal components analysis (PCA) and/or automated element identification, and identify and quantify phases via spectral and/or cluster analysis [2][3][4]. However, dataset size is limited by computer RAM so that algorithms can be completed in real time. Further, because PCA and cluster results are unique to a given field of view, phase analysis results cannot be applied to other specimens.We have developed a set of software tools in Matlab that extend datacube-processing capabilities to datasets much larger than available RAM. Our process maintains a database of individual datacubes stored on disk. Subsets of this database are temporarily loaded into RAM, processing templates are applied, and the result from each FOV is added to an output database. The output database takes the form of a tiled RGB TIFF image, which can itself be retrieved and re-processed in RAM-sized chunks by Matlab, or viewed with various image processing packages. A distinct advantage of our approach is that templates created for a single dataset can be applied to subsequent datasets for direct comparison of similar specimens.The workflow is described in Figure 1. Input data include a matched suite of BSE images, RAW datacubes, and metadata files which describe the location and dimensions of each datacube. Our data were collected with Oxford INCA software and exported to RAW form with INCABatch. Data flow between hard drive and RAM is managed by Matlab's blockproc function. Dimension-reducing options currently include PCA, spectral color mapping, and element ROI coloring. Cluster algorithms include k-means and supervised nearest-neighbor. Future capabilities may incorporate 4-channel output, hierarchical clustering, and other algorithms. After code review we will release all source code to the Matlab File Exchange website for others to use and improve [5].

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