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"The nexus of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a fortuitous context within which to examine the theory and practice of biomedical data curation. Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives inasmuch as data-modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both."--

Medical care --- Data processing. --- Data integration (Computer science) --- COVID-19 (Disease) --- Cancer --- Cardiovascular system --- Treatment. --- Diseases --- Data Curation --- Medical Informatics --- Neoplasms --- COVID-19 --- Cardiovascular Diseases --- therapy

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The Identification of the Genetic Components of Autism Spectrum Disorders 2019 will serve as a resource for laboratory and clinical scientists as well as translational-based researchers, primary healthcare providers or physicians, psychologists/psychiatrists, neurologists, developmental pediatricians, clinical geneticists, and other healthcare providers, teachers, caregivers and students involved in autism spectrum disorders (ASD) with the goal to translate information directly to the clinic, education and home setting. Other professionals, students and families might find this textbook of value based on better awareness, causes and understanding of genetic components leading to autism and open avenues for treatment. Genetics play a role with up to 90% of autism, with over 800 currently recognized genes contributing to causes, clinical presentation, treatment, and counseling of family members. This textbook includes 13 chapters divided into three sections (clinical, genetics, other) written by experts in the field dedicated to research and clinical care, description, treatment and generating relevant reviews for ASD and related disorders impacting gene expression, profiling, and pathways. Identification of potential risk factors will be discussed, including obesity, microbiota, malignancy, and the immune system, as well as their direct or indirect contribution to ASD treatment and causation.

autism spectrum disorders (ASD) --- cancer --- overlapping genes and gene profiling --- super-pathways --- phenotypes and diseases --- molecular functions and processes --- 15q11.2 BP1-BP2 microdeletion (Burnside-Butler) syndrome --- imprinting --- parent-of-origin effects --- phenotype-genotype correlation --- autism --- developmental delays --- motor delays --- microbiome --- gut --- ProSAP2 --- Phelan McDermid Syndrome --- gut–brain interaction --- leaky gut --- IL-6 --- SHANK --- collapsin response mediator protein 4 --- autism spectrum disorder --- neurodevelopmental disorder --- whole-exome sequencing --- animal model --- sex different phenotypes --- 15q11.2 BP1–BP2 microdeletion (Burnside–Butler syndrome) --- NIPA1 --- NIPA2 --- CYFIP1 --- TUBGCP5 genes --- Prader–Willi and Angelman syndromes --- magnesium transporters and supplementation --- potential treatment options --- intellectual disability --- AMPA receptors --- NMDA receptors --- guanine nucleotide exchange factor --- synaptic plasticity --- Autism spectrum disorder --- ASD --- Obesity --- Overweight --- Body mass index --- BMI --- autism candidate genes --- synaptotagmin-like protein 4 (SYTL4) --- transmembrane protein 187 (TMEM187) --- SYTL4-protein structure --- STRING-protein-protein interaction --- expression profile --- microRNA- interactions --- autism spectrum disorders --- biological networks --- genomics --- multi-omics --- network diffusion --- data integration --- genetics --- quantitative traits --- stratification by trait severity --- heterogeneity reduction --- case-control association analysis --- fragile X syndrome --- RNA toxicity --- DNA methylation --- mosaicism --- pediatrics --- MS-QMA --- AmplideX --- cytokine --- monocyte --- β-glucan --- T cell cytokine --- trained immunity --- maternal immune activation --- epigenetics --- mice --- postnatal VPA injection --- SAM --- gene expression --- nanostring

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Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

Technology: general issues --- History of engineering & technology --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging

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Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.

Technology: general issues --- History of engineering & technology --- biodiversity --- peatland --- vegetation type --- classification --- hyperspectral --- in situ measurements --- hyperspectral image (HSI) --- multiscale union regions adaptive sparse representation (MURASR) --- multiscale spatial information --- imaging spectroscopy --- airborne laser scanning --- minimum noise fraction --- class imbalance --- Africa --- agroforestry --- tree species --- hyperspectral unmixing --- endmember extraction --- band selection --- spectral variability --- prototype space --- ensemble learning --- rotation forest --- semi-supervised local discriminant analysis --- optical spectral region --- thermal infrared spectral region --- mineral mapping --- data integration --- HyMap --- AHS --- raw material --- remote sensing --- nonnegative matrix factorization --- data-guided constraints --- sparseness --- evenness --- hashing ensemble --- hierarchical feature --- hyperspectral classification --- band expansion process (BEP) --- constrained energy minimization (CEM) --- correlation band expansion process (CBEP) --- iterative CEM (ICEM) --- nonlinear band expansion (NBE) --- Otsu’s method --- sparse unmixing --- local abundance --- nuclear norm --- hyperspectral detection --- target detection --- sprout detection --- constrained energy minimization --- iterative algorithm --- adaptive window --- hyperspectral imagery --- recursive anomaly detection --- local summation RX detector (LS-RXD) --- sliding window --- band selection (BS) --- band subset selection (BSS) --- hyperspectral image classification --- linearly constrained minimum variance (LCMV) --- successive LCMV-BSS (SC LCMV-BSS) --- sequential LCMV-BSS (SQ LCMV-BSS) --- vicarious calibration --- reflectance-based method --- irradiance-based method --- Dunhuang site --- 90° yaw imaging --- terrestrial hyperspectral imaging --- vineyard --- water stress --- machine learning --- tree-based ensemble --- progressive sample processing (PSP) --- real-time processing --- image fusion --- hyperspectral image --- panchromatic image --- structure tensor --- image enhancement --- weighted fusion --- spectral mixture analysis --- fire severity --- AVIRIS --- deep belief networks --- deep learning --- texture feature enhancement --- band grouping --- hyperspectral compression --- lossy compression --- on-board compression --- orthogonal projections --- Gram–Schmidt orthogonalization --- parallel processing --- anomaly detection --- sparse coding --- KSVD --- hyperspectral images (HSIs) --- SVM --- composite kernel --- algebraic multigrid methods --- hyperspectral pansharpening --- panchromatic --- intrinsic image decomposition --- weighted least squares filter --- spectral-spatial classification --- label propagation --- superpixel --- semi-supervised learning --- rolling guidance filtering (RGF) --- graph --- deep pipelined background statistics --- high-level synthesis --- data fusion --- data unmixing --- hyperspectral imaging