Search

A series of transparent crosslinked colorless polyimide (CPI) films are prepared from 3,3′,4,4′-biphenyltetracarboxylic dianhydride (BPDA), 2,2′-bis(trifluoro-methyl)benzidine (TFMB), and 4,4′-oxydianiline (ODA) by thermal imidization, incorporating varying contents of 2,2′-(1,3-phenylene)bis(2-oxazoline) (1,3-PBO) as the crosslinking agent. Following the incorporation of the crosslinking structure, the CPI films show good optical transparency (approximately 85% winthin visible light range), enhanced glass transition temperature (from 325 °C to 341 °C), and improved thermal stability, and tensile strength. Notably, compared with the pristine uncrosslinked CPI, these crosslinked CPI films significantly increase in elongation at break (from 5.4% to 44.2%). Furthermore, the new approach ensures that crosslinked CPIs improve heat resistance and mechanical properties, while avoiding the embrittlement of materials. This study also offeres straightforward preparation methods for optically transparent crosslinked polyimides without additional processing steps. All these results make this approach can effectively improve the competitive performance of the CPI films for potential applications in microelectronic and optoelectronic fields. [ABSTRACT FROM AUTHOR]

Titanium alloy is widely utilized in diverse industries due to its exceptional specific strength, making it a material with significant potential for advancement. Nevertheless, homogeneous materials are inadequate to meet the demands of various applications. Functional gradient materials (FGMs) have garnered increasing interest for their ability to tailor materials and structures. The continuous transition in FGMs often offers a more uniform and well-connected interface. However, there remains a lack of comprehensive research on the transition interface. In this study, TC4/TC11 double-alloy materials were produced using double-wire additive manufacturing. The wire feeding rates were adjusted to create materials with varying compositions. The grain morphologies, microstructures, and mechanical properties were examined. It was observed that as the TC11 content increased, the grain size decreased, the β content rose, the ultimate tensile strength improved, and the elongation decreased. Additionally, analysis of the fracture morphologies revealed that the dimples became smaller, indicating characteristics of ductile fracture. Following the solid solution aging heat treatment, it is observed that the α phase increases in size, and the mechanical properties are enhanced. These observations indicate that double-wire additive manufacturing can produce diverse interfaces. Furthermore, the heat treatment process has been shown to enhance the material properties, thus establishing an experimental foundation for FGMs. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Activatable theranostic systems combining precise diagnosis and robust immune activation have significant potential in cancer treatment. Herein, we develop a versatile nanoplatform integrating hypoxia-activatable molecular imaging with effective photoimmunotherapy for cancer treatment. Our molecular probe features turn-on near-infrared-II (NIR-II) fluorescence and photoacoustic signals in hypoxic tumor environments. It also induces hypoxia-triggered photodynamic and photothermal effects, promoting immunogenic cell death and activating the STING pathway, engaging both innate and adaptive immunity. The molecular probe is formulated with a vascular disrupting agent to amplify the hypoxia-responsive phototheranostic properties, on which M1-like macrophage membrane is camouflaged to shield against premature release while conferring cancer-targeting affinity. The activatable NIR-II fluorescence and photoacoustic imaging enable precise tumor delineation, while the enhanced phototherapy activates tumor-specific cytotoxic T cells, impeding both primary and distant tumor progression and providing protective immunity against rechallenge in 4T1 tumor-bearing female mice. This work advances activatable theranostic protocols for image-guided immunotherapy. Here the authors report the development and characterization of a nanoplatform that integrates hypoxia-activatable molecular imaging and photo-immunotherapy for cancer diagnosis and immunotherapy. [ABSTRACT FROM AUTHOR]

Purpose: To determine the effects of anti-B cell surface marker CD20 antibody on the occurrence and progression of endometrial cancer to provide insights into clinical treatment in the future. Methods: We performed a two-sample Mendelian randomization analysis (MR) to analyze the causal relationship between EC and immune cell markers. The genome-wide association study (GWAS) data of MS4A1 single nucleotide polymorphism encoding gene of CD20 were collected, and a drug target MR analysis was performed to detect the causal relationship between anti-CD20 antibody and the risk of EC. The risk of follicular lymphoma was used as a positive control, and EC was used as the outcome. Results: EC exerted an effect on 28 immunophenotypes, predominantly on CD20. CD20 in immunoglobulin D+ CD38− B cells, memory B cells, and unsw memory B cells increased after EC onset. In addition, we detected one immunophenotype that exerted a dangerous effect on EC, i.e., CD3/TD CD4+ (mature stage of the T cell group). Simultaneously, CD20 could be used as both a risk factor and a protective factor for EC (P > 0.05). Anti-CD20 antibodies significantly reduced the risk of follicular lymphoma, including two GWAS pooled datasets. In addition, anti-CD20 antibodies exerted a protective effect on EC in two GWAS pooled datasets (P > 0.05). Conclusion: Our study confirms the close relationship between CD20 and EC. Anti-CD20 antibodies may exert a protective effect on EC and reduce the risk of EC morbidity, providing a reference for future clinical diagnosis and treatment. However, further clinical verification of our results is warranted. [ABSTRACT FROM AUTHOR]

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening. A comprehensive benchmarking of existing synthetic lethality (SL) prediction methods is lacking. Here, the authors compare 12 recently developed machine learning methods for SL prediction, assess their performance, and provide guidance on the selection of the most suitable method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the nonlinearity and non-stationary characteristics of groundwater data. Our study leverages the capabilities of ST-GNNs to address these challenges in the Overbetuwe area, Netherlands. We utilize a comprehensive dataset encompassing 395 groundwater level time series and auxiliary data such as precipitation, evaporation, river stages, and pumping well data. The graph-based framework of our ST-GNN model facilitates the integration of spatial interconnectivity and temporal dynamics, capturing the complex interactions within the groundwater system. Our modified Multivariate Time Graph Neural Network model shows significant improvements over traditional methods, particularly in handling missing data and forecasting future groundwater levels with minimal bias. The model's performance is rigorously evaluated when trained and applied with both synthetic and measured data, demonstrating superior accuracy and robustness in comparison to traditional numerical models in long-term forecasting. The study's findings highlight the potential of ST-GNNs in environmental modeling, offering a significant step forward in predictive modeling of groundwater levels. [ABSTRACT FROM AUTHOR]

Red clay as a special soil has a high liquid-plastic limit, water swelling and water loss shrinkage. To solve the problems red clay was modified by calcium carbonate produced in Hezhou, Guangxi. The Liquid-plastic limit, water loss shrinkage, nuclear magnetic resonance (NMR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and consolidated drained (CD) triaxial shear test were carried out on the modified soils. The results show that when the calcium carbonate content is 20%, the plastic limit is 24.38 and the liquid limit is 38.67, which are reduced by 35.04% and 22.16%, respectively. The Montmorillonite content in the modified soil is reduced by 27.7%. The shrinkage coefficient decreased from 0.325 to 0.102. The NMR test shows that the content is 5% and 10% would lead to a decrease in the macropores and an increase in the micropores pores. The phenomenon is the opposite (15% and 20%). All contents led to the porosity increase. The calcium carbonate content of 5% was selected for triaxial shear tests to obtain the stress-strain curves. The Duncan-Zhang was used to predict the modified soil. The model has a large error in the prediction of the peak of the principal stress difference, but the overall trend is relatively consistent. Therefore, the correction coefficient related to the confining pressure was introduced. The corrected model fits the triaxial shear test well. The research provides a method for the liquid-plastic limits and shrinkage properties of modified red clay, explores the influence of calcium carbonate content on microscopic pores, and the correction of the model provides a theoretical basis for practical application. [ABSTRACT FROM AUTHOR]

Acute myocardial infarction (AMI), characterized by high incidence and mortality rates, poses a significant public health threat. Reperfusion therapy, though the preferred treatment for AMI, often exacerbates cardiac damage, leading to myocardial ischemia/reperfusion injury (MI/RI). Consequently, the development of strategies to reduce MI/RI is an urgent priority in cardiovascular therapy. Chinese medicine, recognized for its multi-component, multi-pathway, and multi-target capabilities, provides a novel approach for alleviating MI/RI. A key area of interest is the nuclear factor E2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1) pathway. This pathway is instrumental in regulating inflammatory responses, oxidative stress, apoptosis, endoplasmic reticulum stress, and ferroptosis in MI/RI. This paper presents a comprehensive overview of the Nrf2/HO-1 signaling pathway's structure and its influence on MI/RI. Additionally, it reviews the latest research on leveraging Chinese medicine to modulate the Nrf2/HO-1 pathway in MI/RI treatment. [ABSTRACT FROM AUTHOR]

Tumor metastasis is the primary cause of cancer-related mortality and remains a major hurdle in cancer treatment. Traditional cigarette smoking has been extensively studied for its role in promoting metastasis. However, the impact of e-cigarette (e-cig) on cancer metastasis is not well understood despite their increasing popularity as a supposedly safer alternative. This mini review synthesizes current literature on the effects of e-cig on cancer metastasis, focusing on the processes of dissemination, dormancy, and colonization. It also incorporates recent findings from our laboratory regarding the role of e-cig in tumor progression. E-cig exposure enhances metastatic potential through various mechanisms: it induces epithelial-mesenchymal transition (EMT), increasing cell migratory and invasive capabilities; promotes lymphangiogenesis, aiding tumor cell spread; and alters the pre-metastatic niche to support dormant tumor cells, enhancing their reactivation and colonization. Furthermore, e-cig induce significant epigenetic changes, such as DNA methylation and histone modifications, which regulate genes involved in metastasis. Our data suggest that e-cig upregulate histone demethylases like KDM6B in macrophages, impacting the TME and promoting metastasis. These findings underscore the need for further research to understand the long-term health implications of e-cig use and inform public health policies to reduce e-cig use. [ABSTRACT FROM AUTHOR]

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a 2-layer convolutional neural network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9% at a 1% false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation. [ABSTRACT FROM AUTHOR]

The cortical development of our brains is in a hierarchical manner and promotes the emergence of large-scale functional hierarchy. However, under interindividual heterogenicity, how the spatiotemporal features of brain networks reflect brain development and mental health remains unclear. Here we collect both resting-state electroencephalography and functional magnetic resonance imaging data from the Child Mind Institute Biobank to demonstrate that during brain growth, the global dynamic patterns of brain states become more active and the dominant networks shift from sensory to higher-level networks; the individual functional network patterns become more similar to that of adults and their spatial coupling tends to be invariable. Furthermore, the properties of multimodality brain networks are sufficiently robust to identify healthy brain age and mental disorders at specific ages. Therefore, multimodality brain networks provide new insights into the functional development of the brain and a more reliable and reasonable approach for age prediction and individual diagnosis.Multimodality brain networks using EEG and fMRI reveal functional development of brain in spatio-temporal domains and help us make a more robust assessment of brain age and mental health. [ABSTRACT FROM AUTHOR]

Topological semimetals, possessing topologically non-trivial band structures, serve as excellent platforms for realizing topological superconductivity through hard point-contact experiments. In this study, we successfully induce superconductivity in the three-dimensional Dirac semimetal, cubic PtBi2, using ferromagnetic and paramagnetic tips in hard point contact experiments. The induced superconductivity is proven to be insensitive to ferromagnetism and exhibits unconventional features in the point-contact spectra. The highest superconducting transition temperature ( T c ) reaches approximately 5.1 K, and the T c values are proven to have a positive correlation with the coupling between the tip and the sample. Furthermore, we extend our point-contact experiments to trigonal PtBi2, a material possessing a type-I Weyl semimetal band structure and triply degenerate points proximate to the Fermi level. Utilizing both ferromagnetic Ni tips and paramagnetic Ag tips, we successfully enhance superconductivity with a T c of up to 3.0 K in this material. The findings from point-contact measurements reveal that the enhanced superconductivity is compatible with ferromagnetism and the magnetism of the tip can affect the symmetry of the enhanced superconducting state. Given that the lattice structure remains stable under pressure up to 51.2 GPa for cubic PtBi2 and 12.9 GPa for trigonal PtBi2, the emergent superconducting states observed in these two PtBi2 materials could inherit their topological nontrivial nature and be promising candidates for topological superconductor. [ABSTRACT FROM AUTHOR]

The central nervous system (CNS) comprises a diverse range of brain cell types with distinct functions and gene expression profiles. Although single-cell RNA sequencing (scRNA-seq) provides new insights into the brain cell atlases, integrating large-scale CNS scRNA-seq data still encounters challenges due to the complexity and heterogeneity among CNS cell types/subtypes. In this study, we introduce a self-supervised contrastive learning method, called scCM, for integrating large-scale CNS scRNA-seq data. scCM brings functionally related cells close together while simultaneously pushing apart dissimilar cells by comparing the variations of gene expression, effectively revealing the heterogeneous relationships within the CNS cell types/subtypes. The effectiveness of scCM is evaluated on 20 CNS datasets covering 4 species and 10 CNS diseases. Leveraging these strengths, we successfully integrate the collected human CNS datasets into a large-scale reference to annotate cell types and subtypes in neural tissues. Results demonstrate that scCM provides an accurate annotation, along with rich spatial information of cell state. In summary, scCM is a robust and promising method for integrating large-scale CNS scRNA-seq data, enabling researchers to gain insights into the cellular and molecular mechanisms underlying CNS functions and diseases. scCM, a self-supervised contrastive learning method, effectively integrates large-scale CNS scRNA-seq data by clustering functionally related cells and separating dissimilar ones. [ABSTRACT FROM AUTHOR]

Radiomics offers a noninvasive avenue for predicting clinicopathological factors. However, thorough investigations into a robust breast cancer outcome-predicting model and its biological significance remain limited. This study develops a robust radiomic model for prognosis prediction, and further excavates its biological foundation and transferring prediction performance. We retrospectively collected preoperative dynamic contrast-enhanced MRI data from three distinct breast cancer patient cohorts. In FUSCC cohort (n = 466), Lasso was used to select features correlated with patient prognosis and multivariate Cox regression was utilized to integrate these features and build the radiomic risk model, while multiomic analysis was conducted to investigate the model's biological implications. DUKE cohort (n = 619) and I-SPY1 cohort (n = 128) were used to test the performance of the radiomic signature in outcome prediction. A thirteen-feature radiomic signature was identified in the FUSCC cohort training set and validated in the FUSCC cohort testing set, DUKE cohort and I-SPY1 cohort for predicting relapse-free survival (RFS) and overall survival (OS) (RFS: p = 0.013, p = 0.024 and p = 0.035; OS: p = 0.036, p = 0.005 and p = 0.027 in the three cohorts). Multiomic analysis uncovered metabolic dysregulation underlying the radiomic signature (ATP metabolic process: NES = 1.84, p-adjust = 0.02; cholesterol biosynthesis: NES = 1.79, p-adjust = 0.01). Regarding the therapeutic implications, the radiomic signature exhibited value when combining clinical factors for predicting the pathological complete response to neoadjuvant chemotherapy (DUKE cohort, AUC = 0.72; I-SPY1 cohort, AUC = 0.73). In conclusion, our study identified a breast cancer outcome-predicting radiomic signature in a multicenter radio-multiomic study, along with its correlations with multiomic features in prognostic risk assessment, laying the groundwork for future prospective clinical trials in personalized risk stratification and precision therapy. [ABSTRACT FROM AUTHOR]

Accumulative evidences have indicated the interaction between cellular senescence and ferroptosis. This study intends to investigate the ferroptosis-related molecular markers in TNF-α-induced endothelial senescence. The microarray expression dataset (GSE195517) was used to identify the differently expressed ferroptosis-related genes (DEFRGs) through weighted gene co-expressed network analysis (WGCNA). GO and KEGG were performed to explore the biological function. Furthermore, hub genes were identified after protein-protein interaction (PPI) analysis and validated through real-time qPCR (RT-qPCR). Then, a drug-gene network was established to predict potential drugs for the hub genes. Seven DEFRGs were recognized in the TNF-α-induced HUVEC senescence. Moreover, four hub genes (PTGS2, TNFAIP3, CXCL2, and IL6 are upregulated) were identified by PPI analysis and validated by RT-qPCR. Further analysis exhibited that PTGS2 was subcellularly located in the plasma membrane. Furthermore, after aminosalicylic acid (ASA) was identified as ferroptosis inhibitor for targeting PTGS2 in senescent HUVECs, 5-ASA and 4-ASA were verified to alleviate TNF-α-induced HUVEC senescence through ferroptosis. PTGS2 might play a role in TNF-α-induced HUVEC senescence and ASA may be the potential drug for alleviating TNF-α-induced HUVEC senescence through ferroptosis. [ABSTRACT FROM AUTHOR]

In this paper, the Hadfield steel matrix composite reinforced with dispersed high chromium cast iron (HCCI) was prepared by using the hot-rolling forming method. The microstructure and mechanical properties of the bimetal composites were analyzed. The experimental results showed that the brittle HCCI was necked and broken into uneven blocks or particles after hot-rolling deformation. The fractured hard HCCI was encased in Hadfield steel to form HCCI-reinforced Hadfield steel composite. Hot rolling results in good metallurgical bonding of the two metals, and Mn, Cr, Fe and Si elements were diffused at the interface. The wear property of the composite was higher than that of Hadfield steel. The hard HCCI played an important role in protecting the Hadfield steel matrix from wearing. The average impact toughness of the composite reached 38.2 J cm−2. The impact property of the composite was between Hadfield steel and HCCI, but significantly higher than that of as-cast iron. [ABSTRACT FROM AUTHOR]

Social norms and altruistic punitive behaviours are both based on the integration of information from multiple contexts. Individual behavioural performance can be altered by loss and gain contexts, which produce different mental states and subjective perceptions. In this study, we used event-related potential and time-frequency techniques to examine performance on a third-party punishment task and to explore the neural mechanisms underlying context-dependent differences in punishment decisions. The results indicated that individuals were more likely to reject unfairness in the context of loss (vs. gain) and to increase punishment as unfairness increased. In contrast, fairness appeared to cause an early increase in cognitive control signal enhancement, as indicated by the P2 amplitude and theta oscillations, and a later increase in emotional and motivational salience during decision-making in gain vs. loss contexts, as indicated by the medial frontal negativity and beta oscillations. In summary, individuals were more willing to sanction violations of social norms in the loss context than in the gain context and rejecting unfair losses induced more equity-related cognitive conflict than accepting unfair gains, highlighting the importance of context (i.e., gain vs. loss) in equity-related social decision-making processes. [ABSTRACT FROM AUTHOR]

Altruistic punishment is a primary response to social norms violations; its neural mechanism has also attracted extensive research attention. In the present studies, we applied a low-frequency repetitive transcranial magnetic stimulation (rTMS) to the bilateral dorsolateral prefrontal cortex (DLPFC) while participants engaged in a modified Ultimatum Game (Study 1) and third-party punishment game (Study 2) to explore how the bilateral DLPFC disruption affects people's perception of violation of fairness norms and altruistic punishment decision in the gain and loss contexts. Typically, punishers intervene more often against and show more social outrage towards Dictators/Proposers who unfairly distribute losses than those who unfairly share gains. We found that disrupting the function of the left DLPFC in the second-party punishment and the bilateral DLPFC in the third-party punishment with rTMS effectively obliterated this difference, making participants punish unfairly shared gains as often as they usually would punish unfairly shared losses. In the altruistic punishment of maintaining the social fairness norms, the inhibition of the right DLPFC function will affect the deviation of individual information integration ability; the inhibition of the left DLPFC function will affect the assessment of the degree of violation of fairness norms and weaken impulse control, leading to attenuate the moderating effect of gain and loss contexts on altruistic punishment. Our findings emphasize that DLPFC is closely related to altruistic punishment and provide causal neuroscientific evidence. [ABSTRACT FROM AUTHOR]

We study the entanglement and work statistics in a two-qubit system under periodic driving. The regulation of periodic driving offers greater versatility and universality in contrast to reservoir engineering in static systems. Our research indicates that the external drive can effectively amplify the quasi-steady state entanglement in specific parameter regimes. Furthermore, the drive extends the range of temperatures or temperature differences at which entanglement can emerge. The quasi-steady state is determined by the alteration of inter-qubit coupling and system-bath coupling due to the addition of the driving, as viewed from the effective Hamiltonian. The quasi-periodic nature of the effective Hamiltonian gives rise to the emergence of a revival island in stroboscopic entanglement under certain driving conditions and temperatures. We have also investigated the work statistics of the driven system, which acts as a continuous quantum thermal machine and outputs work continuously and steadily at the quasi-steady state. By changing the driving, there is a distinct operation of modes and corresponding performance. The effective Hamiltonian is changed by the drive, which in turn changes the modes of energy exchange between the system and the baths as well as the work reservoir. [ABSTRACT FROM AUTHOR]

Due to the excellent energy-absorbing properties of the tubular structures, they are commonly used as energy-absorbing elements. In this paper, two types of hollow glass microspheres (HGMs) are mixed with epoxy resin to prepare composite foams. Three densities of composite foams were used as reinforcing materials for carbon fiber tubes (CFRTs). The CFRTs were reinforced with uniform and gradient structures (A, X, O). The axial compression tests were conducted under quasi-static loading to observe the mechanical properties, failure modes, and crashworthiness. The specific energy absorption (SEA) of the CFRTs reinforced with gradient structures to compare with metal energy-absorbing structures. The distribution and fragmentation of HGMs in the epoxy resin by were observed using a scanning electron microscope (SEM). The results show the C20/60 exhibits the highest peak force (PF) of 85.17 kN. Different types of composite foam and gradient designs have different effects on the structure's failure modes, including tearing of the tube walls, shearing, and compression failure of the core. The SEM observed the HGMs of C20 are the most broken. The X-gradient composite foam-filled tube demonstrates superior crashworthiness compared to C20/60, except for the PF. The energy absorption (EA), SEA, meaning crushing force (MCF), and crushing force efficiency (CFE) have improved by 9.8%, 17.1%, 9.8%, and 25.9%, respectively. The SEA of X is higher than the aluminum round tube, the aluminum alloy secondary nested square tube, and the magnesium round tube, the value is 3.6, 2.7, and 1.3 times, respectively. Therefore, the composite foam-reinforced CFRTs are an ideal energy-absorbing configuration. [ABSTRACT FROM AUTHOR]

The Universal Stress Protein (USP) primarily participates in cellular responses to biotic and abiotic stressors, playing a pivotal role in plant growth, development, and Stress responses to adverse environmental conditions. Totals of 23, 26 and 26 USP genes were recognized in Arabidopsis thaliana, Zea mays, and Oryza sativa, respectively. According to USP genes physicochemical properties, proteins from USP I class were identified as hydrophilic proteins with high stability. Based on phylogenetic analysis, USP genes family were classified into nine groups, USP II were rich in motifs. Additionally, members of the same subgroup exhibited similar numbers of introns/exons, and shared conserved domains, indicating close evolutionary relationships. Motif analysis results demonstrated a high degree of conservation among USP genes. Chromosomal distribution suggested that USP genes might have undergone gene expansion through segmental duplication in Arabidopsis thaliana, Zea mays, and Oryza sativa. Most Ka/Ks ratios were found to be less than 1, suggesting that USP genes in Arabidopsis thaliana, Zea mays, and Oryza sativa have experienced purifying selection. Expression profile analysis revealed that USP genes primarily respond to drought stress in Oryza sativa, temperature, and drought stress in Zea mays, and cold stress in Arabidopsis thaliana. Gene collinearity analysis can reveal correlations between genes, aiding subsequent in-depth investigations. This study sheds new light on the evolution of USP genes in monocots and dicots and lays the foundation for a better understanding of the biological functions of the USP genes family. [ABSTRACT FROM AUTHOR]

The present study investigated parental antecedents of children's learning interests in numeracy and literacy. Parents of 110 five- to six-year-old Chinese kindergarten children were recruited. One parent of each child completed the questionnaire on demographics, parental attitudes, home environment, home activity, parental involvement in school, and children's learning interests in numeracy and literacy. The results indicated that parents' attitudes could exert direct impacts on their children's learning interests in numeracy and literacy when demographic variables and parental practices were controlled for. Parents' attitudes toward their own learning were also indirectly correlated with their children's learning interests via their attitudes toward their children's learning. Moreover, parental practices explained the relationship between parental attitudes and children's learning interests, albeit slightly differently across literacy and numeracy. Home activity and parental involvement in school significantly mediated the relationship between parental attitudes and children's learning interests in numeracy, while in literacy, only home learning activity was a significant mediator. These findings have important implications for guiding parents in cultivating the kindergarten children's learning interests in numeracy and literacy. [ABSTRACT FROM AUTHOR]

Chronic cerebral hypoperfusion is an important pathological factor in many neurodegenerative diseases, such as cerebral small vessel disease (CSVD). One of the most used animal models for chronic cerebral hypoperfusion is the bilateral common carotid artery stenosis (BCAS) mouse. For the therapy of CSVD and other diseases, it will be beneficial to understand the pathological alterations of the BCAS mouse, particularly vascular pathological changes. A mouse model of BCAS was used, and 8 weeks later, cognitive function of the mice was examined by using novel object recognition test and eight-arm radial maze test. 11.7 T magnetic resonance imaging (MRI) and luxol fast blue staining were used to evaluate the injury of the corpus callosum (CC), anterior commissure (AC), internal capsule (IC), and optic tract (Opt) in the cerebral white matter of mice. Three-dimensional vascular images of the whole brain of mice were acquired using fluorescence micro-optical sectioning tomography (fMOST) with a high resolution of 0.32 × 0.32 × 1.00 μm3. Then, the damaged white matter regions were further extracted to analyze the vessel length density, volume fraction, tortuosity, and the number of vessels of different internal diameters. The mouse cerebral caudal rhinal vein was also extracted and analyzed for its branch number and divergent angle in this study. BCAS modeling for 8 weeks resulted in impaired spatial working memory, reduced brain white matter integrity, and myelin degradation in mice, and CC showed the most severe white matter damage. 3D revascularization of the whole mouse brain showed that the number of large vessels was reduced and the number of small vessels was increased in BCAS mice. Further analysis revealed that the vessel length density and volume fraction in the damaged white matter region of BCAS mice were significantly reduced, and the vascular lesions were most noticeable in the CC. At the same time, the number of small vessels in the above white matter regions was significantly reduced, while the number of microvessels was significantly increased in BCAS mice, and the vascular tortuosity was also significantly increased. In addition, the analysis of caudal rhinal vein extraction revealed that the number of branches and the average divergent angle in BCAS mice were significantly reduced. The BCAS modeling for 8 weeks will lead to vascular lesions in whole brain of mice, and the caudal nasal vein was also damaged, while BCAS mice mainly mitigated the damages by increasing microvessels. What is more, the vascular lesions in white matter of mouse brain can cause white matter damage and spatial working memory deficit. These results provide evidence for the vascular pathological alterations caused by chronic hypoperfusion. [ABSTRACT FROM AUTHOR]

Objectives: To explore the diagnostic performance of targeted biopsy (TB) combined with regional systematic biopsy (RSB) in patients with different Prostate Imaging Reporting and Data System (PI-RADS) and histologic zones for prostate lesions. Methods: This retrospective study included 1301 patients who underwent multiparametric MRI followed by combined MRI/US fusion-guided TB+systematic biopsy (SB) between January 2019 and October 2022. RSB was defined as the four perilesional SB cores adjacent to an MRI-positive lesion. Cancer detection rates were calculated for TB + SB, TB, SB, and TB + RSB, while the McNemar test was utilized for multiple comparisons among them. Subgroup analyses were performed based on different Pl-RADS and histologic zones. Results: Of 1301 included participants (median age, 68 years; interquartile range, 63–74 years), 16,104 total biopsy cores were performed. TB + RSB detected clinically significant prostate cancer in 70.9% (922/1301) of patients, which was significantly higher than TB (67.4%, p < 0.001) or SB (67.5%, p < 0.001) but similar to TB + SB (71.0%, p = 0.50). Compared with TB + SB, TB + RSB required fewer median biopsy cores (6.3 vs. 12.4, p < 0.001) and had a higher proportion of positive cores (56.3% vs. 39.0%, p < 0.001). Subgroup analysis showed that TB had outstanding sensitivity for detecting PI-RADS 5 lesions in the PZ. Conclusions: Compared with TB + SB, TB + RSB achieved a similar clinically significant prostate cancer detection rate while requiring fewer biopsy cores and exhibiting higher diagnostic efficiency. Critical relevance statement: For MRI-positive prostate lesions, targeted biopsy combined with regional systematic biopsy could serve as an alternative diagnostic approach to targeted biopsy combined with systematic biopsy. Key Points: The scheme of prostate biopsy needs to be optimized. Regional systematic biopsy decreases the total number of cores taken. Targeted biopsies combined with regional systematic biopsies improve prostate diagnostic efficiency. [ABSTRACT FROM AUTHOR]

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows. In proteomics, identifying differentially expressed proteins (DEPs) is critical for uncovering biomarkers and drug targets. However, constructing optimal workflows to achieve maximal identification of DEPs is challenging. Here, the authors performed 34,576 combinatorial experiments on 24 gold standard spike-in datasets to discern optimal workflows. [ABSTRACT FROM AUTHOR]

With the increasingly harsh service conditions, higher requirements are put forward for the cage of ceramic bearings. In this study, dynamic model of full ceramic ball bearing, in which the motion characteristics of cage is considered, under condition of non-lubrication is established, and the model accuracy is also verified by experiments. The change of contact angles, influence of ball-pocket clearance and guide clearance on the dynamic characteristics of cage are studied. Dynamic characteristics of the cage under different axial loads and rotation speeds are studied. The results show that the influence of rotation speed, radial load and axial load on contact angles are basically the same, while the increase in axial load helps to reduce the difference between internal and external contact angles. With the increase in ball-pocket clearance and guide clearance, the slip ratio of cage decreases first and then increases, so there is an optimal ball-pocket clearance and guide clearance, which makes the cage stable. Compared with the rotational speed, the axial load has a greater impact on the dynamic characteristics of the cage. The results have certain reference value for the optimal design and manufacture of full ceramic bearing cage under non-lubrication conditions. [ABSTRACT FROM AUTHOR]

Objective: To investigate the main components and potential mechanism of Shuxuening Injection (SXNI) in the treatment of myocardial ischemia-reperfusion injury (MIRI) through network pharmacology and in vivo research. Methods: The Traditional Chinese Medicine Systems Pharmacology (TCMSP) and PharmMapper databases were used to extract and evaluate the effective components of Ginkgo biloba leaves, the main component of SXNI. The Online Mendelian Inheritance in Man (OMIM) and GeneCards databases were searched for disease targets and obtain the drug target and disease target intersections. The active ingredient-target network was built using Cytoscape 3.9.1 software. The STRING database, Metascape online platform, and R language were used to obtain the key targets and signaling pathways of the anti-MIRI effects of SXNI. In order to verify the therapeutic effect of different concentrations of SXNI on MIRI in rats, 60 rats were first divided into 5 groups according to random number table method: the sham operation group, the model group, SXNI low-dose (3.68 mg/kg), medium-dose (7.35 mg/kg), and high-dose (14.7 mg/kg) groups, with 12 rats in each group. Then, another 60 rats were randomly divided into 5 groups: the sham operation group, the model group, SXNI group (14.7 mg/kg), SXNI+LY294002 group, and LY294002 group, with 12 rats in each group. The drug was then administered intraperitoneally at body weight for 14 days. The main biological processes were validated using in vivo testing. Evans blue/triphenyltetrazolium chloride (TTC) double staining, hematoxylin-eosin (HE) staining, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, enzyme-linked immunosorbent assay (ELISA), and Western blot analysis were used to investigate the efficacy and mechanism of SXNI in MIRI rats. Results: Eleven core targets and 30 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were selected. Among these, the phosphoinositide 3-kinase (PI3K)/ protein kinase B (AKT) pathway was closely related to SXNI treatment of MIRI. In vivo experiments showed that SXNI reduced the myocardial infarction area in the model group, improved rat heart pathological damage, and reduced the cardiomyocyte apoptosis rate (all P<0.01). After SXNI treatment, the p-PI3K/PI3K and p-AKT/AKT ratios as well as B-cell lymphoma-2 (Bcl-2) protein expression in cardiomyocytes were increased, while the Bax and cleaved caspase 3 protein expression levels were decreased (all P<0.05). LY294002 partially reversed the protective effect of SXNI on MIRI. Conclusion: SXNI protects against MIRI by activating the PI3K/AKT signaling pathway. [ABSTRACT FROM AUTHOR]

Pediatric papillary thyroid carcinomas (PPTCs) exhibit high inter-tumor heterogeneity and currently lack widely adopted recurrence risk stratification criteria. Hence, we propose a machine learning-based objective method to individually predict their recurrence risk. We retrospectively collect and evaluate the clinical factors and proteomes of 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) nodules, and quantify 10,426 proteins by mass spectrometry. We find 243 and 121 significantly dysregulated proteins from PM vs. PB and PM vs. AM, respectively. Function and pathway analyses show the enhanced activation of the inflammatory and immune system in PM patients compared with the others. Nineteen proteins are selected to predict recurrence using a machine learning model with an accuracy of 88.24%. Our study generates a protein-based personalized prognostic prediction model that can stratify PPTC patients into high- or low-recurrence risk groups, providing a reference for clinical decision-making and individualized treatment. Papillary thyroid carcinoma has a heterogenous outcome, particularly in paediatric patients. Here, the authors utilise machine learning to create a protein-based prognostic model to predict recurrence risk. [ABSTRACT FROM AUTHOR]

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194 days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments. [ABSTRACT FROM AUTHOR]

We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure–activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification. Scientific contribution: Hit identification is a time-consuming and costly step in drug discovery process. Here we developed a molecule generative pipeline called Pocket Crafter that can speed up this process greatly. This workflow utilized 3D generative modeling method Pocket2Mol for the de novo design of molecules in spatial perspective for the target and applies filters for chemical-physical properties and drug-likeness to generate top virtual hits with further structure–activity relationship analysis and clustering to output a focused set of hit compounds, which led to the success of hit finding as it showed in our demo case. [ABSTRACT FROM AUTHOR]

Third-party punishment (TPP) has evolved as a vital mechanism for enforcing social fairness, to reduce tit-for-tat escalations among peers. Third parties intervene in conflicts between individuals both in private and in front of audiences and larger groups. To explore the influence of group environment on TPP and its neural mechanism and to investigate the moderating effect of gain and loss contexts on TPP, this study employed a modified third-party punishment task based on the dictator game (DG) and used the within-subject experimental design to collect the EEG data of 25 Chinese college students for analysis. Neural results showed that the group environment induced a larger P2 than the individual environment. In addition, when third parties observed dictators unfairly sharing losses with recipients, the presence of a group activated greater mid-frontal delta and theta band activation than environments where individuals acted alone. P2 can help to identify or reflect risk assessment to account for social and reputation assessments and future group benefits. Delta band activation is involved in the disgust response in moral decision-making, suggesting that the group environment affects the emotional factors involved in third-party punishment when viewing unfairly distributed losses. Mid-frontal theta band activation indicates altruistic motivation, and it is also activated more sharply in the loss context in the presence of a group, suggesting that there is a stronger neural signal to intervene when the group environment is potentially at issue. This study provides electrophysiological evidence from EEG of how group environments interact with norm violations to influence how individuals enact third-party punishment to maintain social fairness. [ABSTRACT FROM AUTHOR]

Objective: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. Methods: A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest. Results: The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88–0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70–0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI. Conclusions: The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters. [ABSTRACT FROM AUTHOR]

In this paper we consider the Laguerre operator L = - d 2 d x 2 - α x d dx + x 2 on the Euclidean space R + . The main aim of this article is to develop a theory of homogeneous Besov type spaces associated to the Laguerre operator. To achieve our expected goals, Schwartz type spaces on R + are introduced and then tempered type distributions are constructed. Using a suitable distribution of the Laguerre operator, the Calderón reproducing formula and the Harnack type inequality for subharmonic functions are established. With these tools in hand, we define the Besov type spaces B ˙ p , q s , L , m and obtain the molecular decompositions of B ˙ p , q s , L , m . As applications, the embedding theorem and square functions characterization of Besov type spaces B ˙ p , q s , L , m are also investigated. [ABSTRACT FROM AUTHOR]

Gravitational wave (GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries (MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise. Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation. [ABSTRACT FROM AUTHOR]

SLFN11 is abnormally expressed and associated with survival outcomes in various human cancers. However, the role of SLFN11 in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to investigate the clinical value and potential functions of SLFN11 in ccRCC. Comprehensive bioinformatics analyses were performed using online databases. Quantitative real-time PCR (qPCR) and western blotting were used to validate the expression data. CCK8, flow cytometry analysis, and EdU staining were performed to determine the level of cell proliferation. Flow cytometry analysis was also used to detect cell apoptosis. Wound-healing assay and Transwell assays were performed to assess cell migration and invasion capability, respectively. SLFN11 was overexpressed and was an independent prognostic factor in ccRCC. SLFN11 knockdown inhibited cell proliferation, migration, and invasion and promoted apoptosis. Functional and pathway enrichment analyses suggested that SLFN11 may have an impact on tumorigenesis in ccRCC through regulation of the inflammatory response, the PI3K/AKT signaling pathway and other effectors. Furthermore, SLFN11 knockdown inhibited the phosphorylation of the PI3K/AKT signaling pathway and could be activated by 740 Y-P. Finally, we demonstrated that miR-183 may specifically target SLFN11, and miR-183 expression was correlated with predicted survival. SLFN11 may play a critical role in ccRCC progression and may serve as a novel prognostic biomarker in ccRCC. [ABSTRACT FROM AUTHOR]

The friction coefficient and wear rate of pretreated graphite with liquid nitrogen were obtained by using a ball-on-disk tester, and the wear of GCr15–graphite seal pairs with the low-temperature time-dependent pretreatment was discussed by comparing the wear morphology. The results show that liquid nitrogen pretreatment can affect the hardness and interlayer spacing of graphite. The range of the friction coefficients of pretreated graphite changes from 0.17 to 0.22. With the increase of liquid nitrogen pretreatment time, the wear mechanism of graphite would change from dominated three-body wear to adhesion wear. The experimental results of the mechanical seal with liquid nitrogen pretreatment show that the wear rate of stator is less than 0.00165 mm3·N−1·m−1, and the graphite shows a good low-temperature compatibility. [ABSTRACT FROM AUTHOR]

Nanoscale optoelectrodes hold the potential to stimulate optically individual neurons and intracellular organelles, a challenge that demands both a high-density of photoelectron storage and significant charge injection. Here, we report that zinc porphyrin, commonly used in dye-sensitized solar cells, can be self-assembled into nanorods and then coated by TiO2. The J-aggregated zinc porphyrin array enables long-range exciton diffusion and allows for fast electron transfer into TiO2. The formation of TiO2(e−) attracts positive charges around the neuron membrane, contributing to the induction of action potentials. Far-field cranial irradiation of the motor cortex using a 670 nm laser or an 850 nm femtosecond laser can modulate local neuronal firing and trigger motor responses in the hind limb of mice. The pulsed photoelectrical stimulation of neurons in the subthalamic nucleus alleviates parkinsonian symptoms in mice, improving abnormal stepping and enhancing the activity of dopaminergic neurons. Our results suggest injectable nanoscopic optoelectrodes for optical neuromodulation with high efficiency and negligible side effects. Nanoscale optoelectrodes hold the potential to optically stimulate individual neuron. Here, the authors form nanoscale capacitive optoelectrodes by incorporating zinc porphyrin into nanorods, coated by TiO2, a design that allows for far-field optical modulation of neurons with efficiency and negligible side effects. [ABSTRACT FROM AUTHOR]

Reactive oxygen species (ROS) act as a group of signaling molecules in rice functioning in regulation of development and stress responses. Respiratory burst oxidase homologues (Rbohs) are key enzymes in generation of ROS. However, the role of the nine Rboh family members was not fully understood in rice multiple disease resistance and yield traits. In this study, we constructed mutants of each Rboh genes and detected their requirement in rice multiple disease resistance and yield traits. Our results revealed that mutations of five Rboh genes (RbohA, RbohB, RbohE, RbohH, and RbohI) lead to compromised rice blast disease resistance in a disease nursery and lab conditions; mutations of five Rbohs (RbohA, RbohB, RbohC, RbohE, and RbohH) result in suppressed rice sheath blight resistance in a disease nursery and lab conditions; mutations of six Rbohs (RbohA, RbohB, RbohC, RbohE, RbohH and RbohI) lead to decreased rice leaf blight resistance in a paddy yard and ROS production induced by PAMPs and pathogen. Moreover, all Rboh genes participate in the regulation of rice yield traits, for all rboh mutants display one or more compromised yield traits, such as panicle number, grain number per panicle, seed setting rate, and grain weight, resulting in reduced yield per plant except rbohb and rbohf. Our results identified the Rboh family members involved in the regulation of rice resistance against multiple pathogens that caused the most serious diseases worldwide and provide theoretical supporting for breeding application of these Rbohs to coordinate rice disease resistance and yield traits. [ABSTRACT FROM AUTHOR]

This study investigates the modification of red clay with calcium carbonate. The pore distribution was obtained using a combination of mercury intrusion porosimetry (MIP) and nuclear magnetic resonance (NMR) techniques. The influence of calcium carbonate content, density, confining pressure, and shear strain on the pores was studied. Introducing pore classification to assess the extent of the damage under different conditions and the relationship between different types of pores. The results show that the content of added calcium carbonate has little effect on pore distribution when the density is low (1.4 and 1.5 g/cm3). The content of calcium carbonate has an influence on pore distribution when the density is high (1.6 g/cm3). With the density increases, the number of macropores and mesopores between soil particles decreases, while the small pores increase. The confining pressure results in the preferential drainage of free water from macropores. The porosity decreases as the confining pressure increases. Under the same confining pressure, as the shear strain increases, the large pores decrease while the small pores increase. Based on the analysis of damage degree, the shear strain is positively linearly related to the damage degree of intragranular mesopores, which forms a convex function relationship with intragranular macropores and intergranular mesopores (except under a confining pressure of 100 kPa). [ABSTRACT FROM AUTHOR]

Green finance is considered a novel tool of financing to promote the development of the green economy, which has huge investment attractiveness. Previous studies detected the impacts of economic policy uncertainty (EPU) on the green financial markets are mixed. To this end, this study deeply investigates the asymmetric and heterogeneous impacts of EPU on the green bond and green stock markets based on the quantile-on-quantile (QQ) method. Using the data of China Economic Policy Uncertainty Index (CNEPU) and green financial indices, we get some interesting results. (1) EPU has an overall negative effect on the green financial markets, and the green stock market reacts more strongly than the green bond market. (2) For green bond market, the higher quantiles of EPU on a bear market have more significant effect than that on a bull market. (3) For green stock market, a negative effect of EPU on green stock market is observed for the lower quantiles of EPU, while a positive effect is noted at the highest quantiles of EPU. This paper could provide a reference for investors making green investment strategies and for policymakers making policies to promote green development. [ABSTRACT FROM AUTHOR]

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms. [ABSTRACT FROM AUTHOR]

Leucine-rich repeat receptor-like kinases (LRR-RLKs) are well known to act in plant growth, development, and defense responses. Plant LRR-RLKs locate on cell surface to sense and initiate responsive signals to a variety of extracellular stimuli, such as microbe-associated molecular patterns (MAMPs) released from microorganisms. LRR-RLKs are also present in microbes and function in microbial growth and development, but their roles in communicating with hosts are largely unknown. A recent study published in Nature Communications uncovered that a microbial LRR-RLK, PsRLK6, is required for oospore development in the sexual reproduction of Phytophthora sojae, an oomycete pathogen that causes root and stem rot in soybean. Meanwhile, PsRLK6 is recognized as a novel type of MAMP by an unknown plant LRR receptor-like protein and triggers immune responses in soybean, tomato, and Nicotiana benthamiana. The findings reveal dual roles of a pathogen LRR-RLK in determining both life through sexual reproduction and death through triggering plant immunity. [ABSTRACT FROM AUTHOR]