Diagnosis of Alzheimer's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera

After decades of Alzheimer's disease (AD) research, the development of a definitive diagnostic test for this disease has remained elusive. The discovery of blood-borne biomarkers yielding an accurate and relatively non-invasive test has been a primary goal. Using human protein microarrays to characterize the differential expression of serum autoantibodies in AD and non-demented control (NDC) groups, we identified potential diagnostic biomarkers for AD. The differential significance of each biomarker was evaluated, resulting in the selection of only 10 autoantibody biomarkers that can effectively differentiate AD sera from NDC sera with a sensitivity of 96.0% and specificity of 92.5%. AD sera were also distinguishable from sera obtained from patients with Parkinson's disease and breast cancer with accuracies of 86% and 92%, respectively. Results demonstrate that serum autoantibodies can be used effectively as highly-specific and accurate biomarkers to diagnose AD throughout the course of the disease

Tauopathic Changes in the Striatum of A53T α-Synuclein Mutant Mouse Model of Parkinson's Disease

Tauopathic pathways lead to degenerative changes in Alzheimer's disease and there is evidence that they are also involved in the neurodegenerative pathology of Parkinson's disease [PD]. We have examined tauopathic changes in striatum of the α-synuclein (α-Syn) A53T mutant mouse. Elevated levels of α-Syn were observed in striatum of the adult A53T α-Syn mice. This was accompanied by increases in hyperphosphorylated Tau [p-Tau], phosphorylated at Ser202, Ser262 and Ser396/404, which are the same toxic sites also seen in Alzheimer's disease. There was an increase in active p-GSK-3β, hyperphosphorylated at Tyr216, a major and primary kinase known to phosphorylate Tau at multiple sites. The sites of hyperphosphorylation of Tau in the A53T mutant mice were similar to those seen in post-mortem striata from PD patients, attesting to their pathophysiological relevance. Increases in p-Tau were not due to alterations on protein phosphatases in either A53T mice or in human PD, suggesting lack of involvement of these proteins in tauopathy. Extraction of striata with Triton X-100 showed large increases in oligomeric forms of α-Syn suggesting that α-Syn had formed aggregates the mutant mice. In addition, increased levels of p-GSK-3β and pSer396/404 were also found associated with aggregated α-Syn. Differential solubilization to measure protein binding to cytoskeletal proteins demonstrated that p-Tau in the A53T mutant mouse were unbound to cytoskeletal proteins, consistent with dissociation of p-Tau from the microtubules upon hyperphosphorylation. Interestingly, α-Syn remained tightly bound to the cytoskeleton, while p-GSK-3β was seen in the cytoskeleton-free fractions. Immunohistochemical studies showed that α-Syn, pSer396/404 Tau and p-GSK-3β co-localized with one another and was aggregated and accumulated into large inclusion bodies, leading to cell death of Substantia nigral neurons. Together, these data demonstrate an elevated state of tauopathy in striata of the A53T α-Syn mutant mice, suggesting that tauopathy is a common feature of synucleinopathies

Design of the ECCE Detector for the Electron Ion Collider

Preprint submitted to Nuclear Instruments and Methods A. The file archived on this institutional repository has not been certified by peer review.32 pages, 29 figures, 9 tablesThe EIC Comprehensive Chromodynamics Experiment (ECCE) detector has been designed to address the full scope of the proposed Electron Ion Collider (EIC) physics program as presented by the National Academy of Science and provide a deeper understanding of the quark-gluon structure of matter. To accomplish this, the ECCE detector offers nearly acceptance and energy coverage along with excellent tracking and particle identification. The ECCE detector was designed to be built within the budget envelope set out by the EIC project while simultaneously managing cost and schedule risks. This detector concept has been selected to be the basis for the EIC project detector.Office of Science in the Department of Energy, the National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR; This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05- 00OR22725. The work of AANL group are supported by the Science Committee of RA, in the frames of the research project # 21AG-1C028. And we gratefully acknowledge that support of Brookhaven National Lab and the Thomas Jefferson National Accelerator Facility which are operated under contracts DESC0012704 and DE-AC05-06OR23177 respectivel

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

arXiv preprint [v2] Fri, 20 May 2022 03:23:44 UTC (2,296 KB) made available under a Creative Commons (CC BY) Attribution Licence, now in press, published by Elsevier: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, available online 17 November 2022 at: https://doi.org/10.1016/j.nima.2022.167748The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.Office of Nuclear Physics in the Office of Science in the Department of Energy; National Science Foundation, and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD) 20200022DR