A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data.

Background: Multiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment. Methods: A total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors. Results: We found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p <.01). Conclusion: These data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.

Glycyrrhetinic acid loaded in milk-derived extracellular vesicles for inhalation therapy of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and life-threatening lung disease for which treatment options are limited. Glycyrrhetinic acid (GA) is a triterpenoid with multiple biological effects, such as anti-inflammatory and anti-fibrotic properties. Herein, inhalable milk-derived extracellular vesicles (mEVs) encapsulating GA (mEVs@GA) were screened and evaluated for IPF treatment. The results indicated that the loading efficiency of GA in mEVs@GA was 8.65%. Therapeutic effects of inhalable mEVs@GA were investigated in vitro and in vivo. The mEVs@GA demonstrated superior anti-inflammatory effects on LPS-stimulated MHS cells. Furthermore, repeated noninvasive inhalation delivery of mEVs@GA in bleomycin-induced IPF mice could decrease the levels of transforming growth factors ß1 (TGF-ß1), Smad3 and inflammatory cytokines IL-6, IL-1ß and TNF-α. The mEVs@GA effectively diminished the development of fibrosis and improved pulmonary function in the IPF mice model at a quarter of the dose compared with the pirfenidone oral administration group. Additionally, compared to pirfenidone-loaded mEVs, mEVs@GA demonstrated superior efficacy at the same drug concentration in the pharmacodynamic study. Overall, inhaled mEVs@GA have the potential to serve as an effective therapeutic option in the treatment of IPF.

Macrophage hitchhiking for systematic suppression in postablative multifocal hepatocellular carcinoma.

BACKGROUND AIMS: Hepatocellular carcinoma (HCC), particularly the multifocal HCC, features aggressive invasion and dismal prognosis. Locoregional treatments were often refractory to eliminate tumor tissue, resulting in residual tumor cells persisting and subsequent progression. Owing to problematic delivery to the tumor tissue, systemic therapies, such as lenvatinib (LEN) therapy, show limited clinical benefit in preventing residual tumor progression. Therefore, more advanced strategies for postablative multifocal HCC are urgently needed. APPROACH RESULTS: Motivated by the chemotaxis in tumor penetration of macrophages, we report a strategy named microinvasive ablation-guided macrophage hitchhiking (MAMH) for the targeted therapy toward HCC. In this study, the strategy leverages the natural inflammatory gradient induced by ablation to guide LEN-loaded macrophages toward tumor targeting, which increased by ~10-fold the delivery efficiency of LEN in postablative HCC in vivo. MAMH has demonstrated significant antitumor activity in various HCC models, including the hydrodynamic tail vein injection multifocal HCC mouse model and the orthotopic xenograft HCC rabbit model, systematically inhibiting residual tumor progression after ablation and prolonging the median survival of tumor-bearing mice. The potential antitumor mechanism was explored using techniques such as flow cytometry, enzyme-linked immunosorbent assay, and immunohistochemistry. We found that the strategy significantly suppressed tumor cell proliferation and neovascularization, and such enhanced delivery of LEN stimulated systemic immune responses and induced durable immune memory. CONCLUSIONS: The macrophage hitchhiking strategy demonstrates exceptional therapeutic efficacy and biosafety across various species, offering promising prospects for clinical translation in controlling residual tumor progression and improving outcomes following HCC ablation.

Teachers' Emotional Intelligence and Organizational Commitment: A Moderated Mediation Model of Teachers' Psychological Well-Being and Principal Transformational Leadership.

Given the global challenge of increasing teacher attrition and turnover rates, the exploration of factors and mechanisms that improve teachers' organizational commitment has become a pivotal topic in educational research. In this context, the present study examines the influence of teachers' emotional intelligence on their organizational commitment, with a specific inquiry into the mediating role of teachers' psychological well-being and the moderating role of principal transformational leadership, as informed by the broaden-and-build theory of positive emotions and the trait activation theory. We verified this study's hypotheses based on 768 valid questionnaires collected from Chinese primary and secondary school teachers. The results reveal that teachers' emotional intelligence can predict their organizational commitment both directly and indirectly through the mediating role of psychological well-being. Additionally, principal transformational leadership amplifies the positive effect of teachers' emotional intelligence on psychological well-being and, subsequently, organizational commitment. These findings theoretically deepen our understanding of the psychological pathways and the boundary conditions linking teachers' emotional intelligence to their organizational commitment, while also offering valuable practical implications for building a stable and effective teaching workforce.

A Study of the Adsorption Properties of Individual Atoms on the Graphene Surface: Density Functional Theory Calculations Assisted by Machine Learning Techniques.

In this research, the adsorption performance of individual atoms on the surface of monolayer graphene surface was systematically investigated using machine learning methods to accelerate density functional theory. The adsorption behaviors of over thirty different atoms on the graphene surface were computationally analyzed. The adsorption energy and distance were extracted as the research targets, and the basic information of atoms (such as atomic radius, ionic radius, etc.) were used as the feature values to establish the dataset. Through feature engineering selection, the corresponding input feature values for the input-output relationship were determined. By comparing different models on the dataset using five-fold cross-validation, the mathematical model that best fits the dataset was identified. The optimal model was further fine-tuned by adjusting of the best mathematical ML model. Subsequently, we verified the accuracy of the established machine learning model. Finally, the precision of the machine learning model forecasts was verified by the method of comparing and contrasting machine learning results with density functional theory. The results suggest that elements such as Zr, Ti, Sc, and Si possess some potential in controlling the interfacial reaction of graphene/aluminum composites. By using machine learning to accelerate first-principles calculations, we have further expanded our choice of research methods and accelerated the pace of studying element-graphene interactions.

Exploring the Super-Relaxed State of Human Cardiac ß-Myosin by Molecular Dynamics Simulations.

Human ß-cardiac myosin plays a critical role in generating the mechanical forces necessary for cardiac muscle contraction. This process relies on a delicate dynamic equilibrium between the disordered relaxed state (DRX) and the super-relaxed state (SRX) of myosin. Disruptions in this equilibrium due to mutations can lead to heart diseases. However, the structural characteristics of SRX and the molecular mechanisms underlying pathogenic mutations have remained elusive. To bridge this gap, we conducted molecular dynamics simulations and free energy calculations to explore the conformational changes in myosin. Our findings indicate that the size of the phosphate-binding pocket can serve as a valuable metric for characterizing the transition from the DRX to SRX state. Importantly, we established a global dynamic coupling network within the myosin motor head at the residue level, elucidating how the pathogenic mutation E483K impacts the equilibrium between SRX and DRX through allosteric effects. Our work illuminates molecular details of SRX and offers valuable insights into disease treatment through the regulation of SRX.

Allosteric pathways of SARS and SARS-CoV-2 spike protein identified by neural relational inference.

The receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein must undergo a crucial conformational transition to invade human cells. It is intriguing that this transition is accompanied by a synchronized movement of the entire spike protein. Therefore, it is possible to design allosteric regulators targeting non-RBD but hindering the conformational transition of RBD. To understand the allosteric mechanism in detail, we establish a computational framework by integrating coarse-grained molecular dynamic simulations and a state-of-the-art neural network model called neural relational inference. Leveraging this framework, we have elucidated the allosteric pathway of the SARS-CoV-2 spike protein at the residue level and identified the molecular mechanisms involved in the transmission of allosteric signals. The movement of D614 is coupled with that of Q321. This interaction subsequently influences the movement of K528/K529, ultimately coupling with the movement of RBD during conformational changes. Mutations that weaken the interactions within this pathway naturally block the allosteric signal transmission, thereby modulating the conformational transitions. This observation also offers a rationale for the distinct allosteric patterns observed in the SARS-CoV spike protein. Our result provides a useful method for analyzing the dynamics of potential viral variants in the future.

Preparation and characterization of nisin-loaded chitosan nanoparticles functionalized with DNase I for the removal of Listeria monocytogenes biofilms.

Listeria monocytogenes biofilms represent a continuous source of contamination, leading to serious food safety concerns and economic losses. This study aims to develop novel nisin-loaded chitosan nanoparticles (CSNPs) functionalized with DNase I and evaluate its antibiofilm activity against L. monocytogenes on food contact surfaces. Nisin-loaded CSNPs (CS-N) were first prepared by ionic cross-linking, and DNase I was covalently grafted on the surface (DNase-CS-N). The NPs were subsequently characterized by Zetasizer Nano, transmission electron microscopy, Fourier transform infrared (FT-IR), and X-ray diffraction (XRD). The antibiofilm activity of NPs was evaluated against L. monocytogenes on polyurethane (PU). The DNase-CS-N was fabricated and characterized with quality attributes (particle size-427.0 ± 15.1 nm, polydispersity [PDI]-0.114 ± 0.034, zeta potential-+52.5 ± 0.2 mV, encapsulation efficiency-46.5% ± 3.6%, DNase conjugate rate-70.4% ± 0.2). FT-IR and XRD verified the loading of nisin and binding of DNase I with chitosan. The DNase-CS-N caused a 3 log colony-forming unit (CFU)/cm2 reduction of L. monocytogenes biofilm cells, significantly higher than those in CSNPs (1.4 log), CS-N (1.8 log), and CS-N in combination with DNase I (2.2 log) treatment groups. In conclusion, nisin-loaded CSNPs functionalized with DNase I were successfully prepared and characterized with smooth surface and nearly spherical shape, high surface positive charge, and good stability, which is effective to eradicate L. monocytogenes biofilm cells on food contact surfaces, exhibiting great potential as antibiofilm agents in food industry. PRACTICAL APPLICATION: Listeria monocytogenes biofilms are a common safety hazard in food processing. In this study, novel nanoparticles were successfully constructed and are expected to be a promising antibiofilm agent in the food industry.

The discrepancy distribution of macrophage subsets in preeclampsia placenta with or without fetal growth restriction from a small cohort.

OBJECTIVES: To identify the effect of distribution characteristic of macrophages on placental function and angiogenesis in pregnancies with preeclampsia (PE) in presence of fetal growth restriction (FGR) or preeclampsia without FGR. MATERIAL AND METHODS: The study tested the hypothesis that there was association between distribution characteristic of macrophage subsets (marked by CD68, CD163, respectively) and placental capillary development, leading to placental dysfunction in PE pregnancies with FGR (n = 36). Changes in placental parameters related with efficiency and angiogenesis and macrophage phenotypes (CD68 and CD163) were evaluated by immunohistochemistry. Pearson correlation analysis was performed to analysis the association between macrophage phenotype and placental function as well the CD34 staining, respectively. Additionally, the localization of CD68 and CD163 was assessed by using immunoflurorescence staining. RESULTS: Pearson correlation analysis had shown the positive association between CD68 expression and microvessel formation and the reverse linear relationship between CD163 staining and placental sufficiency in PE + FGR placenta. The co-localization of CD163 and CD34 may pointed to the compensatory role of CD163 distribution involved in prompting neovascularization. CONCLUSIONS: The association between disturbed distribution of macrophages and placental efficiency and angiogenesis were only found in PE with FGR not in PE pregnancies without FGR, underlying the discrepancy role of macrophage subsets depending on the clinical phenotype of PE pregnancies.

The correlation between serum total bile acid and alanine aminotransferase of pregnant women and the disorders of neonatal hyperbilirubinemia-related amino acid metabolism.

BACKGROUND: To explore the association between liver metabolism-related indicators in maternal serum and neonatal hyperbilirubinemia (NHB), and further investigate the predictive value of these indicators in NHB-related amino acid metabolism disorders. METHODS: 51 NHB and 182 No-NHB newborns and their mothers who treated in the Fourth Hospital of Shijiazhuang from 2018 to 2022 were participated in the study. The differences in clinical data were compared by the Mann-Whitney U test and Chi-square test. Multivariate logistic regression was used to analyze the relationship between maternal serum indicators and the occurrence of NHB. The correlation analysis and risk factor assessment of maternal serum indicators with NHB-related amino acid metabolic disorders were performed using Spearman correlation analysis and multivariate logistic regression. RESULTS: Compared to the non NHB group, the NHB group had higher maternal serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT/AST, and total bile acid (TBA), while lower levels of serum albumin (ALB), total cholesterol (TC) and high-density lipoprotein (HDL). The levels of alanine (ALA), valine (VAL), ornithine (ORN), and proline (PRO) in the newborns were reduced in NHB group, while arginine (ARG) showed a tendency to be elevated. Multiple logistic regression analysis showed that maternal ALT, AST, ALT/AST, and TBA levels were all at higher risk with the development of NHB, whereas ALB, TC, and HDL levels were negatively associated with NHB development. Increasing maternal TBA level was associated with lower ALA (r=-0.167, p = 0.011), VAL (r=-0.214, p = 0.001), ORN (r=-0.196, p = 0.003), and PRO in the newborns (r=-0.131, p = 0.045). Maternal ALT level was negatively associated with ALA (r=-0.135, p = 0.039), VAL (r=-0.177, p = 0.007), ORN (r=-0.257, p < 0.001), while ALT/AST was positively correlated with ARG (r = 0.133, p = 0.013). After adjustment for confounding factors, maternal serum TBA and ALT were the independent risk factor for neonatal ORN metabolic disorders [(adjusted odds ratio (AOR) = 0.379, 95%CI = 0.188-0.762, p = 0.006), (AOR = 0.441, 95%CI = 0.211-0.922, p = 0.030)]. Maternal ALT level was an independent risk factor for neonatal VAL metabolic disorders (AOR = 0.454, 95%CI = 0.218-0.949, p = 0.036). CONCLUSIONS: The levels of high TBA, ALT, AST, and low HDL, TC of maternal were associated with the risk of NHB. Maternal TBA and ALT levels were independent risk factors for NHB-related amino acid disturbances which have value as predictive makers.

A Scalable Method for Readable Tree Layouts.

Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some basic requirements: node labels should not overlap, edges should not cross, edge lengths should be preserved, and the output should be compact. There are many algorithms for drawing trees, although very few take node labels or edge lengths into account, and none optimizes all requirements above. With this in mind, we propose a new scalable method for readable tree layouts. The algorithm guarantees that the layout has no edge crossings and no label overlaps, and optimizes one of the remaining aspects: desired edge lengths and compactness. We evaluate the performance of the new algorithm by comparison with related earlier approaches using several real-world datasets, ranging from a few thousand nodes to hundreds of thousands of nodes. Tree layout algorithms can be used to visualize large general graphs, by extracting a hierarchy of progressively larger trees. We illustrate this functionality by presenting several map-like visualizations generated by the new tree layout algorithm.

Dipyridyl-Fused Quinoxalineimide (DPQI): A Strong Electron-Withdrawing Building Block for n-Type Polymer Semiconductors.

Exploration of new electron-withdrawing building blocks plays a key role in the development of n-type organic semiconductors. Herein, a strong electron-withdrawing building block, dipyridyl-fused quinoxalineimide (DPQI), was successfully designed and synthesized. Single-crystal structure reveals that DPQI molecule possesses a completely planar backbone, which is beneficial for charge transport. For comparison, dibenzo-fused quinoxalineimide (DBQI) was also synthesized. The frontier molecular orbital (FMO) energy levels downshift with the incorporation of nitrogen atoms onto the π-conjugated backbone of quinoxalineimide. Two acceptor-acceptor (or all-acceptor) polymers P(BTI-DBQI) and P(BTI-DPQI) based on DBQI and DPQI were synthesized, respectively. Two polymers exhibit deep lowest-unoccupied molecular orbital (LUMO) levels (~-3.5 eV). Additionally, P(BTI-DPQI) exhibits unipolar n-type charge transport with µe of 1.4×10-4  cm2  V-1 s-1 in the organic field-effect transistors (OFET), which render them highly attractive for developing n-type semiconductors device. This work demonstrates that DPQI is a promising building block for constructing n-type polymer semiconductors.

Biallelic truncating TTN variants in M-band encoding exons cause a fetal lethal titinopathy.

To report two novel TTN variants associated with fetal recessive titinopathy, thereby broadening the range of TTN variants that can lead to titinopathy. Clinical information on the fetus and parents was gathered, and genomic DNAs were extracted from the fetal tissue and family members' peripheral blood samples. Exome sequencing on fetal DNA was performed and following bioinformatics analysis, the suspected pathogenic variants were confirmed through Sanger sequencing. Prenatal ultrasound performed at 29 weeks of gestation revealed hydrops fetalis, decreased fetal movements, multiple joint contractures and polyhydramnios. Intrauterine fetal death was noted in the third trimester. Exome sequencing revealed compound heterozygous variants in the TTN gene: a paternally inherited allele c.101227C>T (p.Arg33743Ter) and a maternally inherited c.104254C>T (p.Gln34752Ter) allele. These variants have not been previously reported and are evaluated to be likely pathogenic according to the American College of Medical Genetics and Genomics guidelines. We report a fetus with hydrops fetalis and arthrogryposis multiplex congenita associated with a compound heterozygote in the TTN gene. Our report broadens the clinical and genetic spectrum associated with the TTN-related conditions.

Binding asymmetry and conformational studies of the AtGSDA dimer.

Guanosine deaminase (GSDA) is an important deaminase that converts guanosine to xanthosine, a key intermediate in nitrogen recycling in plants. We previously solved complex structures of Arabidopsis thaliana GSDA bound by various ligands and examined its catalytic mechanism. Here, we report cocrystal structures of AtGSDA bound by inactive guanosine derivatives, which bind relatively weakly to the enzyme and mostly have poor binding geometries. The two protomers display unequal binding performances, and molecular dynamics simulation identified diverse conformations during the enzyme-ligand interactions. Moreover, intersubunit, tripartite salt bridges show conformational differences between the two protomers, possibly acting as "gating" systems for substrate binding and product release. Our structural and biochemical studies provide a comprehensive understanding of the enzymatic behavior of this intriguing enzyme.

Endoplasmic reticulum stress in the pathogenesis of alcoholic liver disease.

The endoplasmic reticulum (ER) plays a pivotal role in protein synthesis, folding, and modification. Under stress conditions such as oxidative stress and inflammation, the ER can become overwhelmed, leading to an accumulation of misfolded proteins and ensuing ER stress. This triggers the unfolded protein response (UPR) designed to restore ER homeostasis. Alcoholic liver disease (ALD), a spectrum disorder resulting from chronic alcohol consumption, encompasses conditions from fatty liver and alcoholic hepatitis to cirrhosis. Metabolites of alcohol can incite oxidative stress and inflammation in hepatic cells, instigating ER stress. Prolonged alcohol exposure further disrupts protein homeostasis, exacerbating ER stress which can lead to irreversible hepatocellular damage and ALD progression. Elucidating the contribution of ER stress to ALD pathogenesis may pave the way for innovative therapeutic interventions. This review delves into ER stress, its basic signaling pathways, and its role in the alcoholic liver injury.

Exploration of Solid Solutions and the Strengthening of Aluminum Substrates by Alloying Atoms: Machine Learning Accelerated Density Functional Theory Calculations.

In this paper, we studied the effects of a series of alloying atoms on the stability and micromechanical properties of aluminum alloy using a machine learning accelerated first-principles approach. In our preliminary work, high-throughput first-principles calculations were explored and the solution energy and theoretical stress of atomically doped aluminum substrates were extracted as basic data. By comparing five different algorithms, we found that the Catboost model had the lowest RMSE (0.24) and lowest MAPE (6.34), and this was used as the final prediction model to predict the solid solution strengthening of the aluminum matrix by the elements. Calculations show that alloying atoms such as K, Na, Y and Tl are difficult to dissolve in the aluminum matrix, whereas alloy atoms like Sc, Cu, B, Zr, Ni, Ti, Nb, V, Cr, Mn, Mo, and W exerted a strengthening influence. Theoretical studies on solid solutions and the strengthening effect of various alloy atoms in an aluminum matrix can offer theoretical guidance for the subsequent selection of suitable alloy elements. The theoretical investigation of alloy atoms in an aluminum matrix unveils the fundamental aspects of the solution strengthening effect, contributing significantly to the expedited development of new aluminum alloys.

Broad antagonism of coronaviruses nsp5 to evade the host antiviral responses by cleaving POLDIP3.

Coronaviruses (CoVs) are a family of the largest RNA viruses that typically cause respiratory, enteric, and hepatic diseases in animals and humans, imposing great threats to the public safety and animal health. Porcine deltacoronavirus (PDCoV), a newly emerging enteropathogenic coronavirus, causes severe diarrhea in suckling piglets all over the world and poses potential risks of cross-species transmission. Here, we use PDCoV as a model of CoVs to illustrate the reciprocal regulation between CoVs infection and host antiviral responses. In this study, downregulation of DNA polymerase delta interacting protein 3 (POLDIP3) was confirmed in PDCoV infected IPEC-J2 cells by isobaric tags for relative and absolute quantification (iTRAQ) and Western blotting analysis. Overexpression of POLDIP3 inhibits PDCoV infection, whereas POLDIP3 knockout (POLDIP3-/-) by CRISPR-Cas9 editing significantly promotes PDCoV infection, indicating POLDIP3 as a novel antiviral regulator against PDCoV infection. Surprisingly, an antagonistic strategy was revealed that PDCoV encoded nonstructural protein 5 (nsp5) was responsible for POLDIP3 reduction via its 3C-like protease cleavage of POLDIP3 at the glutamine acid 176 (Q176), facilitating PDCoV infection due to the loss of antiviral effects of the cleaved fragments. Consistent with the obtained data in IPEC-J2 cell model in vitro, POLDIP3 reduction by cleavage was also corroborated in PDCoV infected-SPF piglets in vivo. Collectively, we unveiled a new antagonistic strategy evolved by PDCoV to counteract antiviral innate immunity by nsp5-mediated POLDIP3 cleavage, eventually ensuring productive virus replication. Importantly, we further demonstrated that nsp5s from PEDV and TGEV harbor the conserved function to cleave porcine POLDIP3 at the Q176 to despair POLDIP3-mediated antiviral effects. In addition, nsp5 from SARS-CoV-2 also cleaves human POLDIP3. Therefore, we speculate that coronaviruses employ similar POLDIP3 cleavage mechanisms mediated by nsp5 to antagonize the host antiviral responses to sustain efficient virus infection.

Fiber Sedimentation and Layer-By-Layer Assembly Strategy for Designing Biomimetic Quasi-Ordered Mullite Fiber Aerogels as Extreme Conditions Thermal Insulators.

Ceramic fiber aerogels are attractive thermal insulating materials. In a thermomechanical coupling environment, however, they often show limited mechanical strength and considerably increased heat transfer which can lead to thermal runaway. In this paper, inspired by bird's nest and nacre, we demonstrate a sample strategy combining fiber sedimentation and layer-by-layer assembly to fabricate ultrastrong mullite fiber aerogels (MFAs) with quasi-ordered structures. The fibrous layers and fiber bridges are constructed in a fiber sedimentation self-assembly process. The fiber sedimentation technique optimizes the structure of the MFAs by regulating the fiber orientation. Owing to the quasi-ordered structure, the fabricated MFAs exhibit the integrated properties of high compression fatigue resistance, temperature-invariant compression resilience from -196 to 1300 °C, and low thermal conductivity (0.034 W·m-1·K-1). By deliberately pressing multilayer MFAs into a thin paper, we substantially enhance the load-bearing capacity of the MFAs and achieve large temperature differences (563 °C) between the cold and hot surfaces by using a thin layer of MFAs (3-5 mm) under the simulated high-temperature (685 °C) and high-pressure (0.9 MPa) environment test. The combination of compression resistance, mechanical flexibility, and excellent thermal insulation provides an appealing material for efficient thermal insulation in extreme environments.

Accelerating First Principles Calculation of Multi-Component Alloy Steady-State Structure and Elastic Properties in Full Component Space.

The FeNiCrAlCoCuTi alloy system has great advantages in mechanical properties such as high hardness and toughness. It has high performance potential and research value and the key in research is designing alloy compositions with target properties. The traditional method, experimental analysis, is highly inefficient to properly exploit the intrinsic relationship between material characteristics and properties for multi-component alloys, especially in investigating the whole composition space. In this work, we present a research way that uses first principles calculation to obtain the properties of multi-component alloys and uses machine learning to accelerate the research. The FeNiCrAlCoCuTi alloy system with its elastic properties is used as an example to demonstrate this process. We specifically design models for each output, all of which have RMSE values of less than 1.1, and confirm their effectiveness through experimental data in the literature, showing that the relative error is below 5%. Additionally, we perform an interpretable analysis on the models, exposing the underlying relationship between input features and output. By means of spatial transformation, we achieve the prediction of the full-component spatial performance from binary to multiple components. Taking the FeNiCrAlM (M = Co, Cu, Ti) quinary alloy system as an example, we design a single-phase BCC structure composed of an Fe0.23Cr0.23Al0.23Ni0.03Cu0.28 alloy with a Young's modulus of 273.10 GPa, as well as a single-phase BCC structure composed of an Fe0.01Cr0.01Al0.01Ni0.44Co0.53 alloy with a shear modulus of 103.6 GPa. Through this research way, we use machine learning to accelerate the calculation, which greatly shortens research time and costs. This work overcomes the drawbacks of traditional experiments and directly obtains element compositions and composition intervals with excellent performance.