Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co‐kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) methods which present (sometimes prohibitively) slow mixing and convergence because they require the simulation of high‐dimensional random effect vectors from their posteriors given large datasets. To enable fast inference in big data spatial problems, we propose the recursive nearest neighbor co‐kriging (RNNC) model. Based on this model, we develop two computationally efficient inferential procedures: (a) the collapsed RNNC which reduces the posterior sampling space by integrating out the latent processes, and (b) the conjugate RNNC, an MCMC free inference which significantly reduces the computational time without sacrificing prediction accuracy. An important highlight of conjugate RNNC is that it enables fast inference in massive multifidelity data sets by avoiding expensive integration algorithms. The efficient computational and good predictive performances of our proposed algorithms are demonstrated on benchmark examples and the analysis of the High‐resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites in which we managed to reduce the computational time from multiple hours to just a few minutes

Recursive Nearest Neighbor Co-Kriging Models for Big Multiple Fidelity Spatial Data Sets

Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co-kriging models, with the help of scalable techniques, have been able to combine such datasets by using spatially varying bias corrections. The associated Bayesian inference for these models is usually facilitated via Markov chain Monte Carlo (MCMC) methods which present (sometimes prohibitively) slow mixing and convergence because they require the simulation of high-dimensional random effect vectors from their posteriors given large datasets. To enable fast inference in big data spatial problems, we propose the recursive nearest neighbor co-kriging (RNNC) model. Based on this model, we develop two computationally efficient inferential procedures: a) the collapsed RNNC which reduces the posterior sampling space by integrating out the latent processes, and b) the conjugate RNNC, an MCMC free inference which significantly reduces the computational time without sacrificing prediction accuracy. An important highlight of conjugate RNNC is that it enables fast inference in massive multifidelity data sets by avoiding expensive integration algorithms. The efficient computational and good predictive performances of our proposed algorithms are demonstrated on benchmark examples and the analysis of the High-resolution Infrared Radiation Sounder data gathered from two NOAA polar orbiting satellites in which we managed to reduce the computational time from multiple hours to just a few minutes.Comment: arXiv admin note: text overlap with arXiv:2004.0134

Traffic restrictions during the 2008 Olympic Games reduced urban heat intensity and extent in Beijing

Satellite thermal remote sensing has been utilized to examine the urban heat dynamics in relation to the urban traffic restriction policy. During the 2008 Olympic Games in Beijing, the traffic volume was approximately cut off by half through the road space rationing. Based on daily MODIS satellite thermal observations on the surface temperature, statistical models were developed to analyze the contribution of traffic volume reduction to the urban heat intensity and spatial extent. Our analyses show that cutting off half of the traffic volume has led to a marked decrease in the mean surface temperature by 1.5–2.4 °C and shrinkage of the heat extent by 820 km2 in Beijing. This research suggests that the impact of urban traffic on heat intensity is considerably larger than previously thought, and the management of urban traffic and vehicle fossil fuel use should be included in the future urban heat mitigation plan

Spontaneous Isomerization of Long-Lived Proteins Provides a Molecular Mechanism for the Lysosomal Failure Observed in Alzheimer's Disease.

Proteinaceous aggregation is a well-known observable in Alzheimer's disease (AD), but failure and storage of lysosomal bodies within neurons is equally ubiquitous and actually precedes bulk accumulation of extracellular amyloid plaque. In fact, AD shares many similarities with certain lysosomal storage disorders though establishing a biochemical connection has proven difficult. Herein, we demonstrate that isomerization and epimerization, which are spontaneous chemical modifications that occur in long-lived proteins, prevent digestion by the proteases in the lysosome (namely, the cathepsins). For example, isomerization of aspartic acid into l-isoAsp prevents digestion of the N-terminal portion of Aβ by cathepsin L, one of the most aggressive lysosomal proteases. Similar results were obtained after examination of various target peptides with a full series of cathepsins, including endo-, amino-, and carboxy-peptidases. In all cases peptide fragments too long for transporter recognition or release from the lysosome persisted after treatment, providing a mechanism for eventual lysosomal storage and bridging the gap between AD and lysosomal storage disorders. Additional experiments with microglial cells confirmed that isomerization disrupts proteolysis in active lysosomes. These results are easily rationalized in terms of protease active sites, which are engineered to precisely orient the peptide backbone and cannot accommodate the backbone shift caused by isoaspartic acid or side chain dislocation resulting from epimerization. Although Aβ is known to be isomerized and epimerized in plaques present in AD brains, we further establish that the rates of modification for aspartic acid in positions 1 and 7 are fast and could accrue prior to plaque formation. Spontaneous chemistry can therefore provide modified substrates capable of inducing gradual lysosomal failure, which may play an important role in the cascade of events leading to the disrupted proteostasis, amyloid formation, and tauopathies associated with AD

Insulin Sensitivity Is Retained in Mice with Endothelial Loss of Carcinoembryonic Antigen Cell Adhesion Molecule 1

CEACAM1 regulates endothelial barrier integrity. Because insulin signaling in extrahepatic target tissues is regulated by insulin transport through the endothelium, we aimed at investigating the metabolic role of endothelial CEACAM1. To this end, we generated endothelial cell-specific Ceacam1 null mice (VECadCre+Cc1(fl/fl)) and carried out their metabolic phenotyping and mechanistic analysis by comparison to littermate controls. Hyperinsulinemic-euglycemic clamp analysis showed intact insulin sensitivity in VECadCre+Cc1(fl/fl) mice. This was associated with the absence of visceral obesity and lipolysis and normal levels of circulating non-esterified fatty acids, leptin, and adiponectin. Whereas the loss of endothelial Ceacam1 did not affect insulin-stimulated receptor phosphorylation, it reduced IRS-1/Akt/eNOS activation to lower nitric oxide production resulting from limited SHP2 sequestration. It also reduced Shc sequestration to activate NF-kappaB and increase the transcription of matrix metalloproteases, ultimately inducing plasma IL-6 and TNFalpha levels. Loss of endothelial Ceacam1 also induced the expression of the anti-inflammatory CEACAM1-4L variant in M2 macrophages in white adipose tissue. Together, this could cause endothelial barrier dysfunction and facilitate insulin transport, sustaining normal glucose homeostasis and retaining fat accumulation in adipocytes. The data assign a significant role for endothelial cell CEACAM1 in maintaining insulin sensitivity in peripheral extrahepatic target tissues

The AAA+ ATPase Thorase Regulates AMPA Receptor-Dependent Synaptic Plasticity and Behavior

SummaryThe synaptic insertion or removal of AMPA receptors (AMPAR) plays critical roles in the regulation of synaptic activity reflected in the expression of long-term potentiation (LTP) and long-term depression (LTD). The cellular events underlying this important process in learning and memory are still being revealed. Here we describe and characterize the AAA+ ATPase Thorase, which regulates the expression of surface AMPAR. In an ATPase-dependent manner Thorase mediates the internalization of AMPAR by disassembling the AMPAR-GRIP1 complex. Following genetic deletion of Thorase, the internalization of AMPAR is substantially reduced, leading to increased amplitudes of miniature excitatory postsynaptic currents, enhancement of LTP, and elimination of LTD. These molecular events are expressed as deficits in learning and memory in Thorase null mice. This study identifies an AAA+ ATPase that plays a critical role in regulating the surface expression of AMPAR and thereby regulates synaptic plasticity and learning and memory