Diverse heterochromatin states restricting cell identity and reprogramming.

Heterochromatin is defined as a chromosomal domain harboring repressive H3K9me2/3 or H3K27me3 histone modifications and relevant factors that physically compact the chromatin. Heterochromatin can restrict where transcription factors bind, providing a barrier to gene activation and changes in cell identity. While heterochromatin thus helps maintain cell differentiation, it presents a barrier to overcome during efforts to reprogram cells for biomedical purposes. Recent findings have revealed complexity in the composition and regulation of heterochromatin, and shown that transiently disrupting the machinery of heterochromatin can enhance reprogramming. Here, we discuss how heterochromatin is established and maintained during development, and how our growing understanding of the mechanisms regulating H3K9me3 heterochromatin can be leveraged to improve our ability to direct changes in cell identity.

Enhancing Structure Stability by Mg/Cr Co-Doped for High-Voltage Sodium-Ion Batteries.

P2-Na2/3Ni1/3Mn2/3O2 cathode materials have garnered significant attention due to their high cationic and anionic redox capacity under high voltage. However, the challenge of structural instability caused by lattice oxygen evolution and P2-O2 phase transition during deep charging persists. A breakthrough is achieved through a simple one-step synthesis of Cr, Mg co-doped P2-NaNMCM, resulting in a bi-functional improvement effect. P2-NaNMCM-0.01 exhibits an impressive capacity retention rate of 82% after 100 cycles at 1 C. In situ X-ray diffraction analysis shows that the "pillar effect" of Mg mitigates the weakening of the electrostatic shielding and effectively suppresses the phase transition of P2-O2 during the charging and discharging process. This successfully averts serious volume expansion linked to the phase transition, as well as enhances the Na+ migration. Simultaneously, in situ Raman spectroscopy and ex situ X-ray photoelectron spectroscopy tests demonstrate that the strong oxygen affinity of Cr forms a robust TM─O bond, effectively restraining lattice oxygen evolution during deep charging. This study pioneers a novel approach to designing and optimizing layered oxide cathode materials for sodium-ion batteries, promising high operating voltage and energy density.

OTX2 restricts entry to the mouse germline.

The successful segregation of germ cells from somatic lineages is vital for sexual reproduction and species survival. In the mouse, primordial germ cells (PGCs), precursors of all germ cells, are induced from the post-implantation epiblast1. Induction requires BMP4 signalling to prospective PGCs2 and the intrinsic action of PGC transcription factors3-6. However, the molecular mechanisms that connect BMP4 to induction of the PGC transcription factors that are responsible for segregating PGCs from somatic lineages are unknown. Here we show that the transcription factor OTX2 is a key regulator of these processes. Downregulation of Otx2 precedes the initiation of the PGC programme both in vitro and in vivo. Deletion of Otx2 in vitro markedly increases the efficiency of PGC-like cell differentiation and prolongs the period of PGC competence. In the absence of Otx2 activity, differentiation of PGC-like cells becomes independent of the otherwise essential cytokine signals, with germline entry initiating even in the absence of the PGC transcription factor BLIMP1. Deletion of Otx2 in vivo increases PGC numbers. These data demonstrate that OTX2 functions repressively upstream of PGC transcription factors, acting as a roadblock to limit entry of epiblast cells to the germline to a small window in space and time, thereby ensuring correct numerical segregation of germline cells from the soma.

Evolution of Local Structural Motifs in Colloidal Quantum Dot Semiconductor Nanocrystals Leading to Nanofaceting.

Colloidal nanocrystals (NCs) have shown remarkable promise for optoelectronics, energy harvesting, photonics, and biomedical imaging. In addition to optimizing quantum confinement, the current challenge is to obtain a better understanding of the critical processing steps and their influence on the evolution of structural motifs. Computational simulations and electron microscopy presented in this work show that nanofaceting can occur during nanocrystal synthesis from a Pb-poor environment in a polar solvent. This could explain the curved interfaces and the olivelike-shaped NCs observed experimentally when these conditions are employed. Furthermore, the wettability of the PbS NCs solid film can be further modified via stoichiometry control, which impacts the interface band bending and, therefore, processes such as multiple junction deposition and interparticle epitaxial growth. Our results suggest that nanofaceting in NCs can become an inherent advantage when used to modulate band structures beyond what is traditionally possible in bulk crystals.

Photomodulation of Proton Conductivity by Nitro-Nitroso Transformation in a Metal-Organic Framework.

The design of a highly and photomodulated proton conductor is important for advanced potential applications in chemical sensors and bioionic functions. In this work, a metal-organic framework (MOF; Gd-NO2) with high proton conductivity is synthesized with a photosensitive ligand of 5-nitroisophthalic acid (BDC-NO2), and it provides remote-control photomodulated proton-conducting behavior. The proton conduction of Gd-NO2 reaches 3.66 × 10-2 S cm-1 at 98% relative humidity (RH) and 25 °C, while it decreases by ∼400 times after irradiation with a 355 nm laser. The newly generated and disappearing FT-IR characteristic peaks reveal that this photomodulated process is realized by the photoinduced transformation from BDC-NO2 to 5-nitroso-isophthalic acid (BDC-NO). According to density functional theory, the smaller electronegativity of the -NO group, the longer distance of the hydrogen bond between BDC-NO and H2O molecules, and the lower water adsorption energy of BDC-NO indicate that the irradiated sample possesses a poorer hydrophilicity and has difficulty forming rich hydrogen-bonded networks, which results in the remarkable decrease of proton conductivity.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Applications of machine learning in computational nanotechnology.

Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

Anisotropic thermal transport in twisted bilayer graphene.

Recently, twisted bilayer graphene (TBLG) has attracted enormous attention owing to its peculiar electronic properties. In this work, the anisotropic thermal conductivity of TBLG is comprehensively investigated. It is reported that interlayer twisting can be a practical approach for thermal transport regulation with high accuracy. A strong non-monotonic correlation between anisotropic thermal conductivity and twisting angles is revealed. Extensive phonon behavior analyses reveal the physical mechanism. The anisotropic thermal transport in TBLG is explained by the calculated phonon density of states (PDOS). Meanwhile, the phonon spectra and phonon relaxation times extracted from spectral energy density (SED) profiles explain the decreasing trend of thermal conductivity with increasing twisting angles. The increase in thermal conductivity is attributed to the combined effects of twist and anisotropy. The reported anisotropic thermal conductivity is important to the thermal modulation and our analyses provide a valuable complement to the phonon studies of TBLG.

Application of Deep Learning Workflow for Autonomous Grain Size Analysis.

Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn's method) and planimetric (Saltykov's method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method.

A simple, switchable pili-labelling method by plasmid-based replacement of pilin.

Labelling of Type IV pili (TFP) can greatly improve our understanding of the pivotal roles of TFP in a variety of bacterial activities including motility, surface sensing and DNA-uptake etc. Here we show a simple and switchable pili-labelling method by plasmid-based inducible replacement of PilA without genetic modification in bacterial genome employed by complicated methods. Using this method, we characterized pili morphology and twitching motility of Pseudomonas aeruginosa in details. More importantly, we demonstrate its application in studying the replenishment dynamics of pilin pool of P. aeruginosa.

Thermal boundary resistance at graphene-pentacene interface explored by a data-intensive approach.

As the machinery of artificial intelligence matures in recent years, there has been a surge in applying machine learning (ML) techniques for material property predictions. Artificial neural network (ANN) is a branch of ML and has gained increasing popularity due to its capabilities of modeling complex correlations among large datasets. The interfacial thermal transport plays a significant role in the thermal management of graphene-pentacene based organic electronics. In this work, the thermal boundary resistance (TBR) between graphene and pentacene is comprehensively investigated by classical molecular dynamics simulations combined with the ML technique. The TBR values along thea,bandcdirections of pentacene at 300 K are 5.19 ± 0.18 × 10-8m2K W-1, 3.66 ± 0.36 × 10-8m2K W-1and 5.03 ± 0.14 × 10-8m2K W-1, respectively. Different architectures of ANN models are trained to predict the TBR between graphene and pentacene. Two important hyperparameters, i.e. network layer and the number of neurons are explored to achieve the best prediction results. It is reported that the two-layer ANN with 40 neurons each layer provides the optimal model performance with a normalized mean square error loss of 7.04 × 10-4. Our results provide reasonable guidelines for the thermal design and development of graphene-pentacene electronic devices.

Full-spectrum thermal analysis in twisted bilayer graphene.

It has been recently reported that a magic angle, i.e. 1.1°, exists in twisted bilayer graphene which could lead to intrinsic unconventional superconductivity. Variations of the twisting angle between different graphene layers could lead to altered electronic band structures, which results in the peculiar superconductivity phenomenon. The effects of twisting angles on different properties of bilayer graphene need to be comprehensively investigated in order to fully understand its mechanism. In this work, classical molecular dynamics simulations are performed to calculate the interfacial thermal resistance (R) at twisting angles from 0° to 359°. Due to the symmetric structures of the honeycomb lattice, only angles from 0° to 60° are needed but the full spectrum is explored to generate the complete picture of R with θ. It was reported that the interfacial thermal resistance changes periodically with the twisting angle, with the smallest R values at every 60° starting from 0° and the largest values at every 60° starting from 30°. The phonon density of states and radial distribution functions are calculated to explain the predicted results. The effects of temperature and single- and bi-direction tensile strains on the calculated interfacial thermal resistance are also studied. The results in this work contribute to the fundamental understanding of the thermal properties in twisted bilayer graphene and provide reasonable guidelines to its applications in thermal management devices.

Mesoporous anion-cation-codoped Co9S8 nanorings for enhanced electrocatalytic oxygen evolution reactions.

Recently, the design and synthesis of Co9S8 micro/nanostructures have attracted attention as electrochemical energy storage and conversion devices due to their low cost and environmental friendliness. Herein, Co9S8 nanorings were synthesized via a one-step solvothermal method with the incorporation of Fe ions, subsequently, properly selenized to boost their electrocatalytic performance. The morphology and structure of the series of cation and anion regulated Co9S8 nanorings were characterized, the electrochemical oxygen evolution reaction (OER) properties were assessed. It is worth noting that the as-prepared catalysts, especially the innovative Fe and Se ions double doped Co9S8 nanorings, denoted as Se/Fe-Co9S8-0.14, exhibited good electrocatalytic OER performance with low overpotential (298 mV) and high durability under alkaline conditions. This work provides a new perspective to develop non-noble metal Co9S8-based OER electrocatalysts with a superior electrocatalytic performance.

Imaging features of the initial chest thin-section CT scans from 110 patients after admission with suspected or confirmed diagnosis of COVID-19.

BACKGROUND: In December 2019, an outbreak of a novel coronavirus pneumonia, now called COVID-19, occurred in Wuhan, Hubei Province, China. COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread quickly across China and the rest of the world. This study aims to evaluate initial chest thin-section CT findings of COVID-19 patients after their admission at our hospital. METHODS: Retrospective study in a tertiary referral hospital in Anhui, China. From January 22, 2020 to February 16, 2020, 110 suspected or confirmed COVID-19 patients were examined using chest thin-section CT. Patients in group 1 (n = 51) presented with symptoms of COVID-19 according to the diagnostic criteria. Group 2 (n = 29) patients were identified as a high degree of clinical suspicion. Patients in group 3 (n = 30) presented with mild symptoms and normal chest radiographs. The characteristics, positions, and distribution of intrapulmonary lesions were analyzed. Moreover, interstitial lesions, pleural thickening and effusion, lymph node enlargement, and other CT abnormalities were reviewed. RESULTS: CT abnormalities were found only in groups 1 and 2. The segments involved were mainly distributed in the lower lobes (58.3%) and the peripheral zone (73.8%). The peripheral lesions, adjacent subpleural lesions, accounted for 51.8%. Commonly observed CT patterns were ground-glass opacification (GGO) (with or without consolidation), interlobular septal thickening, and intralobular interstitial thickening. Compared with group 1, patients in group 2 presented with smaller lesions, and all lesions were distributed in fewer lung segments. Localized pleural thickening was observed in 51.0% of group 1 patients and 48.2% of group 2 patients. The prevalence of lymph node enlargement in groups 1 and 2 combined was extremely low (1 of 80 patients), and no significant pleural effusion or pneumothorax was observed (0 of 80 patients). CONCLUSION: The common features of chest thin-section CT of COVID-19 are multiple areas of GGO, sometimes accompanied by consolidation. The lesions are mainly distributed in the lower lobes and peripheral zone, and a large proportion of peripheral lesions are accompanied by localized pleural thickening adjacent to the subpleural region.

Phonon thermal conduction in a graphene-C3N heterobilayer using molecular dynamics simulations.

Two-dimensional (2D) graphene (GRA) and polyaniline (C3N) monolayers are attracting growing research interest due to their excellent electrical and thermal properties. In this work, in-plane and out-of-plane phonon thermal conduction of GRA-C3N heterobilayer are systematically investigated by using classical molecular dynamics simulations. Effects of system size, temperature and interlayer coupling strength on the in-plane thermal conductivity (k) and out-of-plane interfacial thermal resistance (R) are evaluated. Firstly, a monotonic increasing trend of k with increasing system size is observed, while a negative correlation between thermal conductivity and temperature is revealed. The interlayer coupling strength is found to have a weak effect on the in-plane thermal conductivity of the heterobilayer. Secondly, at T = 300 K and χ = 1, the predicted R of GRA â†’ C3N and C3N â†’ GRA are 1.29 × 10-7 K m2 W-1 and 1.35 × 10-7 K m2 W-1, respectively, which indicates that there is no significant thermal rectification phenomenon. It can also be observed that R decreases monotonically with increasing temperature and coupling strength due to the enhanced Umklapp phonon scattering and the phonon transmission probability across the interface. Phonon density of states, phonon dispersions and participation ratios are evaluated to reveal the mechanism of heat conduction in the heterobilayer. This work contributes the valuable thermal information to modulate the phonon behaviors in 2D heterobilayer based nanoelectronics.

Molecular interaction balanced one- and two-dimensional hybrid nanoarchitectures for high-performance supercapacitors.

Hybridisation of one-dimensional (1D) and two-dimensional (2D) materials to enhance charge storage has received considerable attention, but a fundamental understanding of the inherent ratio-dependent charge transfer mechanisms associated with the modulation of their molecular interactions is still within the community. Herein, we examined 1D surface oxidised carbon nanotubes (Ox-CNTs) and 2D reduced graphene oxide (rGO) to understand their ratio-dependent charge transfer and molecular interaction dynamics. We found that stepwise ultrasonication and the self-assembly process can control the thermodynamic molecular interactions, which result in rGO and Ox-CNT suspensions not only well dispersed in N,N-dimethylformamide but also self-organised into sandwiched nanoarchitectures. We reveal that the enhanced charge storage performance originated from the Ox-CNT-mediated low contact resistance between the active material and the current collector, and the incorporation of rGO leads to a significant ion diffusion coefficient and gives rise to numerous ion diffusion channels for high rate retention. Through a systematic electrochemical characterisation, we found that the GC5 : 5 hybrids (mass ratio of rGO to Ox-CNT) provide the best compromise-balance ratio between rGO and Ox-CNT for realising a champion energy density (9 W h kg-1) and power density (10 kW kg-1) beyond the state-of-the-art performance of the individual materials. Our results herald the advent of molecular level hybridisation of 1D-2D materials for high-performance electrochemical energy storage.

Mechanical properties of molybdenum diselenide revealed by molecular dynamics simulation and support vector machine.

Despite the spurring interests in two-dimensional transition metal dichalcogenide (TMDC) materials, knowledge on the mechanical properties of one of their important member, i.e., molybdenum diselenide (MoSe2) is scarce and remains an open topic. In this work, the mechanical properties of h-MoSe2 and t-MoSe2 were systematically investigated using classical molecular dynamics (MD) simulations combined with machine learning (ML) techniques. The effects of chirality, temperature and strain rate on fracture strain, fracture strength and Young's modulus were characterized in both armchair and zigzag directions. For h-MoSe2, the fracture strengths were 13.6 and 13.0 GPa for armchair and zigzag chiralities, respectively, at 1 K and strain rate of 5 × 10-4 ps-1; the corresponding fracture strains were 0.23 and 0.27. The Young's moduli in armchair and zigzag directions exhibited similar values of 100.9 and 99.5 GPa, respectively. For t-MoSe2, much lower fracture strengths of 6.1 and 6.3 GPa, fracture strains of 0.13 and 0.15, and Young's moduli of 83.7 and 83.0 GPa were predicted under the same conditions. A total of 700 MD simulation cases were calculated under different impact factors and initial conditions, which were subsequently fed into the support vector machine (SVM) algorithm for ML modeling. After training, the ML model could predict the mechanical properties of both MoSe2 types given only the input features such as chirality, temperature and strain rate.

Critical fracture properties of puckered and buckled arsenenes by molecular dynamics simulations.

The pioneering prediction and successful synthesis of monolayer arsenene in recent years have promoted intensive studies on this novel two-dimensional (2D) material. Strain-engineered arsenene monolayer can change its geometric structures with tuned charge distribution, which paves the way for achieving novel electronic properties. The practical applications of the strain-driven topological state in arsenene strongly depend on its critical strain value. In this work, mechanical properties such as fracture strain, fracture strength and Young's modulus of two arsenene structures, i.e. buckled arsenene (b-arsenene) and puckered arsenene (p-arsenene), are comprehensively investigated under different modulators such as system dimension, chirality, temperature, strain rate and random surface defect. A maximum fracture strain reduction of 41.7% from 0.24 to 0.14 is observed in armchair b-arsenene when the temperature increases from 100 to 500 K. The most significant impact factor on the mechanical properties of arseneneis found to be surface defects. A maximum fracture strength reduction of 85.7% is predicted in the armchair b-arsenene when the defect ratio increases from 0 to 5%. On the other hand, the strain rate has a negligible effect on the mechanical properties. Our results provide fundamental knowledge on the critical fracture properties of arsenene.

MicroRNA-323a-3p Promotes Pressure Overload-Induced Cardiac Fibrosis by Targeting TIMP3.

BACKGROUND/AIMS: Cardiac fibrosis is a major cause of diverse cardiovascular diseases. MicroRNAs have recently been proven a novel class of regulators of cardiac fibrosis. In this study, we sought to investigate the role of miR-323a-3p and its mechanisms in regulating cardiac fibrosis. METHODS: The transverse aortic constriction (TAC) mice model was induced and neonatal cardiac fibroblasts (CFs) were cultured. MTT (3- [4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide) assay was used to detect the cell viability. Echocardiography was used to evaluate cardiac function. Masson's Trichrome stain was used to evaluate the development of fibrosis. Luciferase activity assay was performed to confirm the miRNA's binding site. Real-time PCR and Western blot were used to evaluate the level of mRNA and protein. RESULTS: MiR-323a-3p was found up-regulated in myocardial tissues subjected to TAC and in CFs cultured with Angiotensin Ⅱ (Ang Ⅱ). Overexpression of miR-323a-3p significantly increased the mRNA levels of collagen Ⅰ, collagen Ⅲ, MMP2 and MMP9, while inhibition of miR-323a-3p prevented the proliferation, collagen production and the protein level of transforming growth factor (TGF-ß) in rat neonatal CFs. Strikingly, injection of antagomiR-323a-3p elevated cardiac function and inhibited the expression of TGF-ß in the TAC mice. TIMP3 was a direct target of miR-323a-3p, as the overexpression of miR-323a-3p decreased the protein and mRNA levels of TIMP3. In the CFs with pre-treatment of Ang Ⅱ, siRNA-TIMP abolished the effects of AMO-323a-3p on the inhibition of the proliferation of CFs, the down-regulation of collagen Ⅰ and collagen Ⅲ, and the expression of TGF-ß. CONCLUSION: Our findings provide evidence that miR-323a-3p promotes cardiac fibrosis via miR-323a-3p-TIMP3-TGF-ß pathway. miR-323a-3p may be a new marker for cardiac fibrosis progression and that inhibition of miR-323a-3p may be a promising therapeutic target for the treatment of cardiac fibrosis.

Effects of PslG on the Surface Movement of Pseudomonas aeruginosa.

PslG attracted a lot of attention recently due to its great potential abilities in inhibiting biofilms of Pseudomonas aeruginosa However, how PslG affects biofilm development still remains largely unexplored. Here, we focused on the surface motility of bacterial cells, which is critical for biofilm development. We studied the effects of PslG on bacterial surface movement in early biofilm development at a single-cell resolution by using a high-throughput bacterial tracking technique. The results showed that compared with no exogenous PslG addition, when PslG was added to the medium, bacterial surface movement was significantly (4 to 5 times) faster and proceeded in a more random way with no clear preferred direction. A further study revealed that the fraction of walking mode increased when PslG was added, which then resulted in an elevated average speed. The differences of motility due to PslG addition led to a clear distinction in patterns of bacterial surface movement and retarded microcolony formation greatly. Our results provide insight into developing new PslG-based biofilm control techniques.IMPORTANCE Biofilms of Pseudomonas aeruginosa are a major cause for hospital-acquired infections. They are notoriously difficult to eradicate and pose serious health hazards to human society. So, finding new ways to control biofilms is urgently needed. Recent work on PslG showed that PslG might be a good candidate for inhibiting/disassembling biofilms of Pseudomonas aeruginosa through Psl-based regulation. However, to fully explore PslG functions in biofilm control, a better understanding of PslG-Psl interactions is needed. Toward this end, we examined the effects of PslG on the surface movement of Pseudomonas aeruginosa in this work. The significance of our work is in greatly enhancing our understanding of the inhibiting mechanism of PslG on biofilms by providing a detailed picture of bacterial surface movement at a single-cell level, which will allow a full understanding of PslG abilities in biofilm control and thus present potential applications in biomedical fields.