Correlation between low skeletal muscle index and 3D anthropometric data measured by 3D body scanner: screening sarcopenia

BackgroundThe screening tools for sarcopenia are measuring calf circumference, SARC-F or SPPB. However, not all of these tools have high sensitivity, specificity, and low margins of error. This research investigates potential of 3D anthropometry of the lower extremities on screening of sarcopenia.MethodsFrom October 2022 to February 2023, we retrospectively analyzed results of 3D body scanner and bio-impedance analysis for patients aged 45 to 85 at risk of sarcopenia. The 3D scanner measured the surface and volume values of both thighs and calves. When skeletal muscle index (SMI) is less than 5.7, patients were classified to Low SMI group, indicative of sarcopenia.ResultsA total six out of 62 patients were classified to Low SMI group, showing significantly lower values of right, left, mean calf volumes and mean calf surface than the other patients (right calf volume 2.62 L vs. 3.34 L, p = 0.033; left calf volume 2.62 L vs. 3.25 L, p = 0.044; mean calf volume 2.62 L vs. 3.29 L, p = 0.029; mean calf surface 0.12 m2 vs. 0.13 m2, p = 0.049). There was no statistical difference in thigh volume and surface. Through AUC-ROC analysis, mean calf volume was the most significant cut-off value (right calf volume 2.80 L, AUC = 0.768; left calf volume 2.75 L, AUC = 0.753; mean calf volume 3.06 L, AUC = 0.774; mean calf surface 0.12 m2, AUC = 0.747).ConclusionThe calf volume and surface values have significant relationship with low SMI, and the mean calf volume was the most significant cut-off screening value for Low SMI. The 3D scanner demonstrated its value as a new means for screening sarcopenia

Singular Hall response from a correlated ferromagnetic flat nodal-line semimetal

Topological quantum phases have been largely understood in weakly correlated systems, which have identified various quantum phenomena such as spin Hall effect, protected transport of helical fermions, and topological superconductivity. Robust ferromagnetic order in correlated topological materials particularly attracts attention, as it can provide a versatile platform for novel quantum devices. Here, we report singular Hall response arising from a unique band structure of flat topological nodal lines in combination with electron correlation in an itinerant, van der Waals ferromagnetic semimetal, Fe3GaTe2, with a high Curie temperature of Tc=360 K. High anomalous Hall conductivity violating the conventional scaling, resistivity upturn at low temperature, and a large Sommerfeld coefficient are observed in Fe3GaTe2, which implies heavy fermion features in this ferromagnetic topological material. Our circular dichroism in angle-resolved photoemission spectroscopy and theoretical calculations support the original electronic features in the material. Thus, low-dimensional Fe3GaTe2 with electronic correlation, topology, and room-temperature ferromagnetic order appears to be a promising candidate for robust quantum devices

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Myotis rufoniger genome sequence and analyses: M-rufoniger's genomic feature and the decreasing effective population size of Myotis bats

Myotis rufoniger is a vesper bat in the genus Myotis. Here we report the whole genome sequence and analyses of the M. rufoniger. We generated 124 Gb of short-read DNA sequences with an estimated genome size of 1.88 Gb at a sequencing depth of 66x fold. The sequences were aligned to M. brandtii bat reference genome at a mapping rate of 96.50% covering 95.71% coding sequence region at 10x coverage. The divergence time of Myotis bat family is estimated to be 11.5 million years, and the divergence time between M. rufoniger and its closest species M. davidii is estimated to be 10.4 million years. We found 1,239 function-altering M. rufoniger specific amino acid sequences from 929 genes compared to other Myotis bat and mammalian genomes. The functional enrichment test of the 929 genes detected amino acid changes in melanin associated DCT, SLC45A2, TYRP1, and OCA2 genes possibly responsible for the M. rufoniger's red fur color and a general coloration in Myotis. N6AMT1 gene, associated with arsenic resistance, showed a high degree of function alteration in M. rufoniger. We further confirmed that the M. rufoniger also has batspecific sequences within FSHB, GHR, IGF1R, TP53, MDM2, SLC45A2, RGS7BP, RHO, OPN1SW, and CNGB3 genes that have already been published to be related to bat's reproduction, lifespan, flight, low vision, and echolocation. Additionally, our demographic history analysis found that the effective population size of Myotis clade has been consistently decreasing since similar to 30k years ago. M. rufoniger's effective population size was the lowest in Myotis bats, confirming its relatively low genetic diversity

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby ???tag??? variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation

A Phenome-Based Functional Analysis of Transcription Factors in the Cereal Head Blight Fungus, Fusarium graminearum

Fusarium graminearum is an important plant pathogen that causes head blight of major cereal crops. The fungus produces mycotoxins that are harmful to animal and human. In this study, a systematic analysis of 17 phenotypes of the mutants in 657 Fusarium graminearum genes encoding putative transcription factors (TFs) resulted in a database of over 11,000 phenotypes (phenome). This database provides comprehensive insights into how this cereal pathogen of global significance regulates traits important for growth, development, stress response, pathogenesis, and toxin production and how transcriptional regulations of these traits are interconnected. In-depth analysis of TFs involved in sexual development revealed that mutations causing defects in perithecia development frequently affect multiple other phenotypes, and the TFs associated with sexual development tend to be highly conserved in the fungal kingdom. Besides providing many new insights into understanding the function of F. graminearum TFs, this mutant library and phenome will be a valuable resource for characterizing the gene expression network in this fungus and serve as a reference for studying how different fungi have evolved to control various cellular processes at the transcriptional level