Bending Deformation Driven by Molecular Rotation

In recent years, certain molecular crystals have been reported to possess surprising flexibility by undergoing significant elastic or plastic deformation in response to mechanical loads. However, despite this experimental evidence, there currently exists no atomistic mechanism to explain the physical origin of this phenomenon from numerical simulations. In this study, we investigate the mechanical behavior of three naphthalene diimide derivatives, which serve as representative examples, using direct molecular dynamics simulations. Our simulation trajectory analysis suggests that molecular rotational freedom is the key factor in determining a crystal's mechanical response, ranging from brittle fracture to elastic or plastic deformation under mechanical bending. Additionally, we propose a rotation-dependent potential energy surface as a means to classify organic materials' mechanical responses and identify new candidates for future investigation.Comment: 10 figures, 11 page

Atomic cluster expansion force field based thermal property material design with density functional theory level accuracy in non-equilibrium molecular dynamics calculations over sub-million atoms

Non-equilibrium molecular dynamics (NEMD) techniques are widely used for investigating lattice thermal conductivity. Recently, machine learning force fields (MLFFs) have emerged as a promising approach to enhance the precision in NEMD simulations. This study is aimed at demonstrating the potential of MLFFs in realizing NEMD calculations for large-scale systems containing over 100,000 atoms with density functional theory (DFT)-level accuracy. Specifically, the atomic cluster expansion (ACE) force field is employed, using Si as an example. The ACE potential incorporates 4-body interactions and features a training dataset consisting of 1000 order structures from first-principles molecular dynamics calculations, resulting in a highly accurate vibrational spectrum. Moreover, the ACE potential can reproduce thermal conductivity values comparable with those derived from DFT calculations via the Boltzmann equation. To demonstrate the application of MLFFs to systems containing over 100,000 atoms, NEMD simulations are conducted on thin films ranging from 100 nm to 500 nm, with the 100 nm films exhibiting defect rates of up to 1.5%. The results show that the thermal conductivity deviates by less than 5% from DFT or theoretical results in both scenarios, which highlights the ability of the ACE potential in calculating the thermal conductivity on a large scale with DFT-level accuracy. The proposed approach is expected to promote the application of MLFFs in various fields and serve as a feasible alternative to virtual experiments. Furthermore, this work demonstrates the potential of MLFFs in enhancing the accuracy of NEMD simulations for investigating lattice thermal conductivity for systems with over 100,000 atoms.Comment: 24 pages including with supporting infomatio

Freshwater mussels (Bivalvia: Unionidae) from the rising sun (Far East Asia): phylogeny, systematics, and distribution

Freshwater mussels (Bivalvia: Unionidae) is a diverse family with around 700 species being widespread in the Northern Hemisphere and Africa. These animals fulfill key ecological functions and provide important services to humans. Unfortunately, populations have declined dramatically over the last century, rendering Unionidae one of the world’s most imperiled taxonomic groups. In Far East Asia (comprising Japan, Korea, and Eastern Russia), conservation actions have been hindered by a lack of basic information on the number, identity, distribution and phylogenetic relationships of species. Available knowledge is restricted to studies on national and sub-national levels. The present study aims to resolve the diversity, biogeography and evolutionary relationships of the Far East Asian Unionidae in a globally comprehensive phylogenetic and systematic context.We reassessed the systematics of all Unionidae species in the region, including newly collected specimens from across Japan, South Korea, and Russia, based on molecular (including molecular species delineation and a COI + 28S phylogeny) and comparative morphological analyses. Biogeographical patterns were then assessed based on available species distribution data from the authors and previous reference works.We revealed that Unionidae species richness in Far East Asia is 30% higher than previously assumed, counting 43 species (41 native + 2 alien) within two Unionidae subfamilies, the Unioninae (32 + 1) and Gonideinae (9 + 1). Four of these species are new to science, i.e. Beringiana gosannensis sp. nov., Beringiana fukuharai sp. nov., Buldowskia kamiyai sp. nov., and Koreosolenaia sitgyensis gen. & sp. nov. We also propose a replacement name for Nodularia sinulata, i.e. Nodularia breviconcha nom. nov. and describe a new tribe (Middendorffinaiini tribe nov.) within the Unioninae subfamily. Biogeographical patterns indicate that this fauna is related to that from China south to Vietnam until the Mekong River basin. The Japanese islands of Honshu, Shikoku, Kyushu, Hokkaido, and the Korean Peninsula were identified as areas of particularly high conservation value, owing to high rates of endemism, diversity and habitat loss. The genetically unique species within the genera Amuranodonta, Obovalis, Koreosolenaia gen. nov., and Middendorffinaia are of high conservation concern

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives

Acceleration of material discovery has been tackled by informatics and laboratory automation. Here we show a semi-automated material exploration scheme to modelize the solubility of tetraphenylporphyrin derivatives. The scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automated measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was similar to 7% and similar to 4%, respectively) with a small number of evaluations (10 molecules: 0.13% of all targeted molecules). The derived binary classification models predicted 'good solvents' with an accuracy >0.8. Overall, we confirmed the effectivity of the proposed semi-automated scheme in early-stage material search projects for accelerating a wider range of material research

Semi-automatic scheme for early-stage material search: developing solvent-solubility prediction of tetraphenylporphyrin derivatives securing chemical-space coverage

This study developed and implemented a semi-automatic material exploration scheme to modelize the solvent-solubility of tetraphenylporphyrin derivatives. In particular, the scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automatic measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was ~7% and ~4%, respectively) with a small number of evaluations (10 molecules: 0.13% of all targeted molecules). The derived binary classification models predicted ‘good solvents’ with an accuracy > 0.8. Overall, we confirmed the effectivity of the proposed semi-automatic scheme in early-stage material search projects for accelerating a wider range of material research

デンシレイキ サレタ コウオン ブッシツ ノ ヒダンネツ リョウシ ブンシ ドウリキガクテキ ケンキュウ

博士(理学)熊本大

Thermal Conductivity Calculation using Homogeneous Non-equilibrium Molecular Dynamics Simulation with Allegro

In this study, we derive the heat flux formula for the Allegro model, one of machine-learning interatomic potentials using the equivariant deep neural network, to calculate lattice thermal conductivity using the homogeneous non-equilibrium molecular dynamics (HNEMD) method based on the Green-Kubo formula. Allegro can construct more advanced atomic descriptors than conventional ones, and can be applied to multicomponent and large-scale systems, providing a significant advantage in estimating the thermal conductivity of anharmonic materials, such as thermoelectric materials. In addition, the spectral heat current (SHC) method, recently developed for the HNEMD framework (HNEMD-SHC), allows the calculation of not only the total thermal conductivity but also its frequency components. The verification of the heat flux and the demonstration of HNEMD-SHC method are performed for the extremely anharmonic low-temperature phase of Ag$_2$Se