Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders.

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.journal articl

First-principles simulations of high-order harmonics generation in thin films of wide bandgap materials

High-order harmonics generation (HHG) is the only process that enables table-top-size sources of extreme-ultraviolet (XUV) light. The HHG process typically involves light interactions with gases or plasma material phases that hinder wider adoption of such sources. This motivates the research in HHG from nanostructured solids. Here we investigate theoretically material platforms for HHG at the nanoscale using first-principle supercomputer simulations. We reveal that wide-bandgap semiconductors, aluminium nitride AlN and silicon nitride \Sin, are highly-promising for XUV light generation when compared to one of the most common nonlinear nanophotonic material silicon. In our calculations we assume excitation with 100 fs pulse duration, \Wcm{1}{13} peak power and 800 nm central wavelenght. We demonstrate that in AlN material the interplay between the crystal symmetry and the incident light direction and polarization can enable the generation of both even and odd harmonics. Our results should advance the developments of high-harmonics generation of XUV light from nanostructured solids.journal articl

FENDL: A library for fusion research and applications

The Fusion Evaluated Nuclear Data Library (FENDL) is a comprehensive and validated collection of nuclear cross section data coordinated by the International Atomic Energy Agency (IAEA) Nuclear Data Section (NDS). FENDL assembles the best nuclear data for fusion applications selected from available nuclear data libraries and has been under development for decades. FENDL contains sub- libraries for incident neutron, proton, and deuteron cross sections including general purpose and activation files used for particle transport and nuclide inventory calculations. In this work, we describe the history, selection of evaluations for the various sub-libraries (neutron, proton, deuteron) with the focus on transport and reactor dosimetry applications, the processing of the nuclear data for application codes (e.g. MCNP), and the development of the TENDL-2017 library which is the currently recommended activation library for FENDL. We briefly describe the IAEA IRDFF library as the recommended library for dosimetry fusion applications. We also present work on validation of the neutron sub-library using a variety of fusion relevant computational and experimental benchmarks using the MCNP transport code and ACE-formatted cross section libraries. A variety of cross section libraries are used for the validation work including FENDL-2.1, FENDL-3.1d, FENDL-3.2, ENDF/B-VIII.0, and JEFF-3.2 with the emphasis on the FENDL libraries. The results of the validation using computational benchmarks showed generally good agreement among the tested neutron cross section libraries for neutron flux, nuclear heating, and primary displacement damage (dpa). Gas production (H/He) in structural materials showed substantial differences to the reference FENDL-2.1 library. The results of the experimental validation showed that the performance of FENDL-3.2b is at least as good and in most cases better than FENDL-2.1. Future work will consider improved evaluations developed by the International Nuclear Data Evaluation Network (INDEN) for materials such as O, Cu, W, Li, B, and F. Additionally, work will need to be done to investigate differences in gas production in structural materials. Covariance matrices will need to be developed or updated as availability of consistent and comprehensive uncertainty information will be needed as fusion technology and facility construction matures. Finally, additional validation work for high energy neutrons, protons and deuterons, as well as validation work for the activation library will be needed.journal articl

Association between mammillary body atrophy and memory impairment in retired athletes with a history of repetitive mild traumatic brain injury.

Cognitive dysfunction, especially memory impairment, is a typical clinical feature of long-term symptoms caused by repetitive mild traumatic brain injury (rmTBI). The current study aims to investigate the relationship between regional brain atrophy and cognitive impairments in retired athletes with a long history of rmTBI. Overall, 27 retired athletes with a history of rmTBI (18 boxers, 3 kickboxers, 2 wrestlers, and 4 others; rmTBI group) and 23 age/sex-matched healthy participants (control group) were enrolled. MPRAGE on 3?T MRI was acquired and segmented. The TBV and TBV-adjusted regional brain volumes were compared between groups, and the relationship between the neuropsychological test scores and the regional brain volumes were evaluated. Total brain volume (TBV) and regional brain volumes of the mammillary bodies (MBs), hippocampi, amygdalae, thalami, caudate nuclei, and corpus callosum (CC) were estimated using the SPM12 and ITK-SNAP tools. In the rmTBI group, the regional brain volume/TBV ratio (rmTBI vs. control group, Mann-Whitney U test, p?<?0.05) underwent partial correlation analysis, adjusting for age and sex, to assess its connection with neuropsychological test results. Compared with the control group, the rmTBI group showed significantly lower the MBs volume/TBV ratio (0.13?±?0.05 vs. 0.19?±?0.03?×?10, p?<?0.001). The MBs volume/TBV ratio correlated with visual memory, as assessed, respectively, by the Rey-Osterrieth Complex Figure test delayed recall (ρ?=?0.62, p?<?0.001). In conclusion, retired athletes with rmTBI have MB atrophy, potentially contributing to memory impairment linked to the Papez circuit disconnection.journal articl

Large-scale cranial window for in vivo mouse brain imaging utilizing fluoropolymer nanosheet and light-curable resin

Two-photon microscopy enables in vivo imaging of neuronal activity in mammalian brains at high resolution. However, two-photon imaging tools for stable, long-term, and simultaneous study of multiple brain regions in same mice are lacking. Here, we propose a method to create large cranial windows covering such as the whole parietal cortex and cerebellum in mice using fluoropolymer nanosheets covered with light-curable resin (termed the ‘Nanosheet Incorporated into light-curable REsin’ or NIRE method). NIRE method can produce cranial windows conforming the curved cortical and cerebellar surfaces, without motion artifacts in awake mice, and maintain transparency for >5 months. In addition, we demonstrate that NIRE method can be used for in vivo two-photon imaging of neuronal ensembles, individual neurons and subcellular structures such as dendritic spines. The NIRE method can facilitate in vivo large-scale analysis of heretofore inaccessible neural processes, such as the neuroplastic changes associated with maturation, learning and neural pathogenesis.journal articl

Experimental Elucidation of a Cubane Water Cluster in the Hydrophobic Cavity of UiO-66

ナノスケールの水は、材料の特性と機能を決定する上で極めて重要な役割を果たしており、その量と原子スケールの秩序構造の正確な制御は、ナノテクノロジーと化学の焦点となっている。本研究では、小さな水クラスターである水八量体がMOFの疎水性空洞内に水素結合がない状態で存在し、秩序化した水がナノ空間に浸透していることを明らかにした。journal articl

Ion Tracks and Nanohillocks Created in Natural Zirconia Irradiated with Swift Heavy Ions

Natural monoclinic zirconia (baddeleyite) was irradiated with 340 MeV Au ions, and the irradiation-induced nanostructures (i.e., ion tracks and nanohillocks) were observed using transmission electron microscopy. The diameter of the nanohillocks was approximately 10 nm, which was similar to the maximum molten region size calculated using the analytical thermal spike model. Ion tracks were imaged as strained regions that maintained their crystalline structure. The cross-sections of most of the ion tracks were imaged as rectangular contrasts as large as 10 nm. These results strongly indicated that the molten region was recrystallized anisotropically, reflecting the lattice structure. Futhermore, low-density track cores were formed in the center of the ion tracks. The formation of low-density track cores can be attributed to the ejection of molten matter toward the surface. A comparison of the ion tracks in the synthetic zirconia nanoparticles and those in larger natural zirconia samples showed that the inerface between the strained track contrast and the matrix was less clear in the former than in the latter. These findings suggest that the recrystallization process was affected by the size of the irradiated samples.journal articl

Ductility evaluation for small plate specimens by fracture surface shape change

In this study, tensile tests were conducted on small plate specimens of varying thicknesses to assess the impact of specimen geometry on ductility evaluation. The findings revealed that both fracture elongation and reduction in fracture surface area decreased as plate thickness diminished. Finite element analysis indicated that stress triaxiality at the center of the specimen increased with decreasing plate thickness, contributing to a reduction in fracture strain. Examination of the fracture surfaces showed that the rate of thickness reduction due to deformation was higher for thinner plates. For small plate specimens with a thickness of t=0.75 (SS-J3), the reduction rate of the fracture surface in the width direction was only marginally less than that of the round bar, and the modes of deformation and fracture closely resembled those of the round-bar. Therefore, in SS-J3 specimens, deformation of the fracture surface in the width direction may serve as a reliable indicator for ductility evaluation.journal articl

ATTEMPT TO RE-ESTIMATE ORGAN DOSES OF VICTIMS IN NON-HOMOGENEOUS EXPOSURE ACCIDENT BY MEANS OF THE STATE-OF-THE-ART MESH-TYPE REFERENCE COMPUTATIONAL PHANTOM - A CASE STUDY OF AN IR-192 SOURCE ACCIDENT -

An attempt was made to estimate organ doses of a victim in a high-dose non-homogeneous exposure accident caused by a sealed 192Ir gamma ray source. The Gilan accident was selected as a case study. Organ doses including testis, red bone marrow and so on were properly estimated by applying the Monte Carlo calculation with the state-of-the-art adult male Mesh type Reference Computational Phantom. By introducing a complicated exposure scenario, the dose distribution on the right chest of the victim in the Gilan accident could be reproduced to a certain extent.journal articl

Automation of etch pit analyses on solid-state nuclear track detectors with machine learning for laser-driven ion acceleration

Solid-state nuclear track detectors (SSNTDs) are often used as ion detectors in laser-driven ion acceleration experiments and are considered to be the most reliable ion diagnostics since they are sensitive only to ions and measure ions one by one. However, the ion pit analyses require tremendous time and effort in chemical etching, microscope scanning, and ion pit identification by eyes. From a laser-driven ion acceleration experiment, there are typically millions of microscope images, and it is practically impossible to analyze all of them by hand. This research aims to improve the efficiency and automation of SSNTD analyses for laser-driven ion acceleration. We use two sets of data obtained from calibration experiments with a conventional accelerator where ions with known nuclides and energies are generated and from actual laser experiments, using SSNTDs. After chemical etching and scanning the SSNTDs with an optical microscope, we use machine learning to distinguish the ion etch pits from noises. From the results of the calibration experiment, we confirm highly accurate etch-pit detection with machine learning. We are also able to detect etch pits with machine learning from the laser-driven ion acceleration experiment, which is much noisier than calibration experiments. By using machine learning, we successfully identify ion etch pits ~ 10^5 from more than 10,000 microscope images with a precision of >~ 95%. A million microscope images can be examined with a recent entry-level computer within a day with high precision. Machine learning tremendously reduces the time consumption on ion etch pit analyses detected on SSNTDs.journal articl