Atomic-scale representation and statistical learning of tensorial properties

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Demonstration of Metabolic and Cellular Effects of Portal Vein Ligation Using Multi-Modal PET/MRI Measurements in Healthy Rat Liver.

OBJECTIVES: In the early recognition of portal vein ligation (PVL) induced tumor progression, positron emission tomography and magnetic resonance imaging (PET/MRI) could improve diagnostic accuracy of conventionally used methods. It is unknown how PVL affects metabolic patterns of tumor free hepatic tissues. The aim of this preliminary study is to evaluate the effect of PVL on glucose metabolism, using PET/MRI imaging in healthy rat liver. MATERIALS AND METHODS: Male Wistar rats (n = 30) underwent PVL. 2-deoxy-2-(18F)fluoro-D-glucose (FDG) PET/MRI imaging (nanoScan PET/MRI) and morphological/histological examination were performed before (Day 0) and 1, 2, 3, and 7 days after PVL. Dynamic PET data were collected and the standardized uptake values (SUV) for ligated and non-ligated liver lobes were calculated in relation to cardiac left ventricle (SUVVOI/SUVCLV) and mean liver SUV (SUVVOI/SUVLiver). RESULTS: PVL induced atrophy of ligated lobes, while non-ligated liver tissue showed compensatory hypertrophy. Dynamic PET scan revealed altered FDG kinetics in both ligated and non-ligated liver lobes. SUVVOI/SUVCLV significantly increased in both groups of lobes, with a maximal value at the 2nd postoperative day and returned near to the baseline 7 days after the ligation. After PVL, ligated liver lobes showed significantly higher tracer uptake compared to the non-ligated lobes (significantly higher SUVVOI/SUVLiver values were observed at postoperative day 1, 2 and 3). The homogenous tracer biodistribution observed before PVL reappeared by 7th postoperative day. CONCLUSION: The observed alterations in FDG uptake dynamics should be taken into account during the assessment of PET data until the PVL induced atrophic and regenerative processes are completed

Retrieval of Snow Depth on Arctic Sea Ice From Surface-Based, Polarimetric, Dual-Frequency Radar Altimetry

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry-derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual-frequency, fully polarized Ku- and Ka-band radar was deployed in “stare” nadir-looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual-frequency, dual-polarization and waveform shape, and compared to independent snow depth measurements. Novel dual-polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub-banded to CryoSat-2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co-polarized dual-frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters

Association Testing Of Copy Number Variants in Schizophrenia and Autism Spectrum Disorders

Background: Autism spectrum disorders and schizophrenia have been associated with an overlapping set of copynumber variant loci, but the nature and degree of overlap in copy number variants (deletions compared toduplications) between these two disorders remains unclear.Methods: We systematically evaluated three lines of evidence: (1) the statistical bases for associations of autismspectrum disorders and schizophrenia with a set of the primary CNVs thus far investigated, from previous studies;(2) data from case series studies on the occurrence of these CNVs in autism spectrum disorders, especially amongchildren, and (3) data on the extent to which the CNVs were associated with intellectual disability anddevelopmental, speech, or language delays. We also conducted new analyses of existing data on these CNVs inautism by pooling data from seven case control studies.Results: Four of the CNVs considered, dup 1q21.1, dup 15q11-q13, del 16p11.2, and dup 22q11.21, showed clearstatistical evidence as autism risk factors, whereas eight CNVs, del 1q21.1, del 3q29, del 15q11.2, del 15q13.3, dup16p11.2, dup 16p13.1, del 17p12, and del 22q11.21, were strongly statistically supported as risk factors forschizophrenia. Three of the CNVs, dup 1q21.1, dup 16p11.2, and dup 16p13.1, exhibited statistical support as riskfactors for both autism and schizophrenia, although for each of these CNVs statistical significance was nominal fortests involving one of the two disorders. For the CNVs that were statistically associated with schizophrenia but werenot statistically associated with autism, a notable number of children with the CNVs have been diagnosed withautism or ASD; children with these CNVs also demonstrate a high incidence of intellectual disability anddevelopmental, speech, or language delays.Conclusions: These findings suggest that although CNV loci notably overlap between autism and schizophrenia,the degree of strongly statistically supported overlap in specific CNVs at these loci remains limited. These analysesalso suggest that relatively severe premorbidity to CNV-associated schizophrenia in children may sometimes bediagnosed as autism spectrum disorder