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Abstract Background Since the outbreak of the Coronavirus Disease 2019 (COVID-19), front-line nurses have faced not only daily work stress but also a high risk of infection and excessive workload, leading to unsatisfactory professional quality of life (ProQOL). This study aimed to explore whether self-compassion (SC) and benefit finding (BF) play a chain mediating role between work stress and ProQOL among Chinese nurses during the COVID-19 pandemic. Methods From March to April 2022, a sample of 13,936 Chinese nurses was recruited through snowball sampling. Demographic information, work stress, SC, BF, and ProQOL were assessed. The SPSS 25.0 software and Amos 24.0 software were used for statistical analysis. The bootstrap method was employed to construct and examine the chain mediating structural equation model. Results The ProQOL level of Chinese nurses during the COVID-19 pandemic was moderate. The overall fit indices for the compassion satisfaction (CS), burnout (BO), and secondary traumatic stress (STS) models were satisfactory (χ²/df = 2.486, 3.256, 2.553, RMSEA = 0.011, 0.014, 0.011, the GFI, AGFI, NFI, and CFI values were all above 0.90). Work stress had direct effects on CS, BO, and STS (β=–0.171, 0.334, 0.222, P

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Self-directed learning (SDL) is a basic individual ability in modern society. It is of great value to explore SDL and its relationship to learners’ online learning effectiveness. This study explored the relationships among online learners’ personality traits (neuroticism and extraversion), SDL (SDL-approach and SDL-attitude), and perceived online learning ineffectiveness (POLI). A total of 668 high school students who had received online learning completed the survey. Results revealed that neuroticism negatively predicted SDL-approach and SDL-attitude, whereas extraversion was positively correlated with SDL-approach and SDL-attitude, and SDL-approach and SDL-attitude were negatively correlated with POLI. In addition, the correlation between personality traits and POLI was significantly mediated by the two types of SDL. The findings provide educators with empirical evidence and insights about improving students’ SDL and understanding the importance of personality traits in online learning.

Abstract Amino acid metabolism plays a pivotal role in tumor microenvironment, influencing various aspects of cancer progression. The metabolic reprogramming of amino acids in tumor cells is intricately linked to protein synthesis, nucleotide synthesis, modulation of signaling pathways, regulation of tumor cell metabolism, maintenance of oxidative stress homeostasis, and epigenetic modifications. Furthermore, the dysregulation of amino acid metabolism also impacts tumor microenvironment and tumor immunity. Amino acids can act as signaling molecules that modulate immune cell function and immune tolerance within the tumor microenvironment, reshaping the anti-tumor immune response and promoting immune evasion by cancer cells. Moreover, amino acid metabolism can influence the behavior of stromal cells, such as cancer-associated fibroblasts, regulate ECM remodeling and promote angiogenesis, thereby facilitating tumor growth and metastasis. Understanding the intricate interplay between amino acid metabolism and the tumor microenvironment is of crucial significance. Expanding our knowledge of the multifaceted roles of amino acid metabolism in tumor microenvironment holds significant promise for the development of more effective cancer therapies aimed at disrupting the metabolic dependencies of cancer cells and modulating the tumor microenvironment to enhance anti-tumor immune responses and inhibit tumor progression.

Abstract Friction as a fundamental physical phenomenon dominates nature and human civilization, among which the achievement of molecular rolling lubrication is desired to bring another breakthrough, like the macroscale design of wheel. Herein, an edge self-curling nanodeformation phenomenon of graphite nanosheets (GNSs) at cryogenic temperature is found, which is then used to promote the formation of graphite nanorollers in friction process towards molecular rolling lubrication. The observation of parallel nanorollers at the friction interface give the experimental evidence for the occurrence of molecular rolling lubrication, and the graphite exhibits abnormal lubrication performance in vacuum with ultra-low friction and wear at macroscale. The molecular rolling lubrication mechanism is elucidated from the electronic interaction perspective. Experiments and theoretical simulations indicate that the driving force of the self-curling is the uneven atomic shrinkage induced stress, and then the shear force promotes the intact nanoroller formation, while the constraint of atomic vibration decreases the dissipation of driving stress and favors the nanoroller formation therein. It will open up a new pathway for controlling friction at microscale and nanostructural manipulation.

ABSTRACT Pancreatic cancer, predominantly pancreatic ductal adenocarcinoma (PDAC), is one of the most malignant tumors of the digestive system. Emerging evidence suggests the involvement of the microbiome and metabolic substances in the development of PDAC, yet the results remain contradictory. This study aims to identify the alterations and relationships in intratumoral microbiome and metabolites in PDAC. We collected matched tumor and normal adjacent tissue (NAT) samples from 105 PDAC patients and performed a 6-year follow-up. 2bRAD-M sequencing, untargeted liquid chromatography-tandem mass spectrometry, and untargeted gas chromatography-mass spectrometry were performed. Compared with NATs, microbial α-diversity decreased in PDAC tumors. The relative abundance of Staphylococcus aureus, Cutibacterium acnes, and Cutibacterium granulosum was higher in PDAC tumor after adjusting for confounding factors body mass index and M stage, and the presence of Ralstonia pickettii_B was found associated with a worse overall survival. Metabolomic analysis revealed distinctive differences in composition between PDAC and NAT, with 553 discriminative metabolites identified. Differential metabolites were revealed to originate from the microbiota and showed significant interactions with shifted bacterial species through KO (KEGG Orthology) genes. These findings suggest that the PDAC microenvironment harbors unique microbial-derived enzymatic reactions, potentially influencing the occurrence and development of PDAC by modulating the levels of glycerol-3-phosphate, succinate, carbonate, and beta-alanine.IMPORTANCEWe conducted a large sample-size pancreatic adenocarcinoma microbiome study using a novel microbiome sequencing method and two metabolomic assays. Two significant outcomes of our analysis are: (i) commensal opportunistic pathogens Staphylococcus aureus, Cutibacterium acnes, and Cutibacterium granulosum were enriched in pancreatic ductal adenocarcinoma (PDAC) tumors compared with normal adjacent tissues, and (ii) worse overall survival was found related to the presence of Ralstonia pickettii_B. Microbial species affect the tumorigenesis, metastasis, and prognosis of PDAC via unique microbe-enzyme-metabolite interaction. Thus, our study highlights the need for further investigation of the potential associations between pancreatic microbiota-derived omics signatures, which may drive the clinical transformation of microbiome-derived strategies toward therapy-targeted bacteria.

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

BackgroundTo examine the associations of serum sodium and frailty with the risk of mild cognitive impairment (MCI) among hospitalized older adults with chronic diseases.MethodsA cross-sectional study was conducted in 403 hospitalized older adults with chronic diseases. Serum sodium concentration was assessed by the ion-selective electrode method, frailty status was evaluated by the FRAIL scale, and MCI was determined by the Montreal Cognitive Assessment (MoCA). Multiple logistic regression models were used to estimate the associations of serum sodium and frailty with MCI.ResultsParticipants with the lowest tertile of serum sodium had a higher risk of MCI than those in the middle tertile group (OR = 1.75, 95% CI: 1.01–3.04). Below 143 mmol/L, the risk of MCI was 1.38 (95% CI: 1.03–1.84) for per 1 SD decrease in serum sodium. Compared with the robust group, frailty was significantly associated with an increased risk of MCI (OR = 3.94, 95% CI: 1.92–8.10). Moreover, in comparison with participants with the middle tertile of serum sodium and who were robust/prefrail, those with frailty and either the lowest (OR = 5.53, 95% CI: 2.08–14.67) or the highest tertile of serum sodium (OR = 3.48, 95% CI: 1.20–10.05) had higher risks of MCI.ConclusionBoth lower and higher serum sodium impose a significantly higher risk for MCI in older adults with frailty. This could inform the design of clinical trials and the development of guidelines and recommendations for correcting serum sodium and frailty in hospitalized older adults with chronic diseases.

Pancreatic cancer is an extremely malignant tumor. PVT1 and MYC signaling has been considered as a therapeutic target recently. Nonetheless, the prognostic values and critical regulatory networks of PVT1-MYC duet in pancreatic cancer remain unclear. Firstly, we identified PVT1-MYC duet-related genes using public databases. Then we analyzed our Hi-C and ChIP-seq data to confirm PVT1-MYC duet. We performed LASSO regression and multivariate Cox regression analysis to build a prognostic model whose effectiveness and robustness were validated by Cox regression, ROC analysis, calibration curve, and nomogram. Besides, we conducted functional enrichment analyses, mutation profiles analyses and the immune features analyses to compare low- and high-risk group. Functional enrichment analyses revealed that several terms associated with cancer progression were enriched in the high-risk group. Mutation profile analysis showed that high-risk group had higher tumor mutation burden, and immune analysis demonstrated high-risk group had more immunosuppressive tumor microenvironment. Finally, we detected PVT1 expression in pancreatic cancer and paracancer tissues from the PUMCH cohort, which showed that PVT1 was significantly upregulated in pancreatic cancer and associated with invasion, metastasis, and poor prognosis. We further performed transwell and proliferation assays and found that PVT1, CDC6, and COL17A1 could promote migration or proliferation of PDAC cells. This study constructed a prognostic model based on three PVT1-MYC duet-related genes, which had a significant potential in predicting the prognosis and tumor microenvironment of pancreatic cancer. These results suggested that targeting PVT1-MYC duet or its regulatory processes could be a therapeutic option with great interests.

Microbialite reservoirs are of great importance in oil and gas exploration. However, there is still a lack of comprehensive studies on the formation mechanisms of thrombolitic reservoirs, a specific type of microbialite. This research focuses on the oldest thrombolitic dolomite reservoir located within the Precambrian Dengying Formation in the central Sichuan Basin, southwestern China. A multi-disciplinary approach was employed to characterize different thrombolite facies and elucidate the formation mechanism of thrombolitic dolomite reservoirs and their controlling factors, involving core observation, thin-section analysis, cathodoluminescence, scanning electron microscope (SEM) microscopy, elemental analysis using LA-ICP-MS, and carbon and oxygen stable isotope analysis. Based on variations in texture, four types of thrombolite were identified: 1) distinct clotted thrombolite, 2) diffuse and regular clotted thrombolite, 3) diffuse and irregular clotted thrombolite, and 4) composite clotted thrombolite. Notably, the diffuse clotted thrombolitic dolomite is the prevalent lithology in the reservoir. Through modification by meteoric water, organic acid fluids, and hydrothermal fluids, a reservoir with dominant porosity in the form of primary growth-framework pores, dissolution pores, and vugs was formed. This resulted in the development of two high-quality reservoir intervals within the Second Member and at the top of the Fourth Member of the Dengying Formation. The growth-framework porosity of the thrombolites, epigenetic karstification, and tectonic fracturing were mainly conducive to reservoir development. However, various types of cementation have reduced porosity and connectivity within the reservoir. Overall, this study is a valuable example of the methodology required to understand meso- and microstructures of deep-buried thrombolitic dolomite reservoirs, including their heterogeneities and diagenesis, as the original structures influence diagenesis.

BackgroundPsychological capital has become a prominent focus in positive psychology, highlighting the positive influence of higher psychological capital on individuals. Self-directed learning ability is a fundamental skill for students, vital for enhancing academic performance and professional development, and is integral to the continuous learning process of nursing students. Recognizing the relationship between psychological capital and self-directed learning ability is crucial for the progress and development of undergraduate nursing students.ObjectiveThis study aims to investigate the correlation between psychological capital and self-directed learning ability in undergraduate nursing students, as well as to explore the factors that influence these variables.MethodsA cross-sectional survey was conducted with 667 full-time undergraduate nursing students from a nursing school in Taizhou, China. Psychological capital and self-directed learning ability were assessed using the Psychological Capital Questionnaire and Self-Directed Learning Scale, respectively. Correlation and stepwise multiple regression analyses were then carried out to evaluate the relationship between psychological capital and self-directed learning ability among the participants.ResultsThe study revealed that the psychological capital score averaged at 103.24 ± 15.51, while the self-directed learning scale score averaged at 230.67 ± 27.66. Variations in psychological capital scores were noted based on factors including grade level, being an only child, growth environment, monthly living expenses, parental education level, voluntary selection of nursing major, and club experience. Similarly, differences in self-directed learning scores were associated with factors such as grade level, gender, parental education level, and voluntary selection of nursing major. Moreover, a positive correlation was identified between the overall psychological capital scores and the total self-directed learning ability scores among nursing students. Notably, the multiple regression analysis highlighted that optimism and resilience played significant roles as predictors of self-directed learning ability.ConclusionPsychological capital is positively correlated with the self-directed learning ability of nursing students, with optimism and resilience identified as crucial predictors. Nursing educators can utilize strategies rooted in positive psychology and perseverance to improve the self-directed learning ability of nursing students.

The ecological relationships between tree growth and climate factors are important for the adaptation, growth prediction, and forest management of tree species against a background of global warming. Yet how these relationships for drought-sensitive conifer trees are changed and affected by elevation remains unclear. To investigate the effect of elevation on spruce tree growth–climate interactions, we collected a total of 1886 tree-ring cores (two cores per tree) from four spruce species, including Picea crassifolia Kom., Picea meyeri Rehd. et Wils., Picea schrenkiana Fisch. et Mey., and Picea wilsonii Mast., in 10 pure forests at a pair of high- and low-elevation alpine sites with the same slope across two aridity zones in China. We then constructed 20 stand tree-ring chronologies for the low- and high-elevation sites. Overall, the growth of spruce trees was more sensitive to drought variability at sites in the semi-arid zone whereas it was weakly limited by drought in the semi-humid zone. Further, spruce trees growing at high-elevation sites were sensitive to temperature unlike the stronger positive drought responses of those at low-elevation sites. In the latter half of the 20th century, the positive drought–growth relationships of spruce were enhanced at both high and low elevation across both aridity zones due to warming and increased drought intensity. Our dendrochronology-based findings suggest that spruce trees’ radial growth in stands at high elevation may have faced drought stress during three recent decades. Nevertheless, the low-elevation trees incurred stronger drought stress compared to the first half of the 20th century.

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy. Methods Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored. Results A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group. Conclusions We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell–cell communication in the tumor microenvironment.

Abstract Purpose The pathways underpinning suicide ideation (SI) and certain physical and psychological factors in patients with advanced breast cancer remain unclear. This study develops and validates a mediation model that delineates the associations between several multidimensional variables and SI in Chinese patients with advanced breast cancer. Methods Patients with advanced breast cancer (n = 509) were recruited as study participants from 10 regional cancer centers across China from August 2019 to December 2020. Participants were required to complete five questionnaires using an electronic patient-reported outcomes (ePRO) system: 9 item- Patient Health Questionnaire (PHQ-9), Hospital Anxiety and Depression Scale (HADS), Insomnia Severity Index (ISI), 5-level EQ-5D (EQ-5D-5L), and MD Anderson Symptom Inventory (MDASI). Risk factors for SI were identified using multivariable logistic regression, and inputted into serial multiple mediation models to elucidate the pathways linking the risk factors to SI. Results SI prevalence was 22.8% (116/509). After adjusting for covariates, depression (odds ratio [OR] = 1.384), emotional distress (OR = 1.107), upset (OR = 0.842), and forgetfulness (OR = 1.236) were identified as significant independent risk factors (all p

The accuracy of neural networks heavily depends on numerous precisely annotated samples. But in actual datasets, the number of samples for each category varies greatly. In the dataset, some classes may have a large sample size, called majority classes, while others may have a small sample size, called minority classes. Training model with imbalanced data is often conducive to the majority classes, while unfair to minority classes. As a result, the outputs return good performance on majority classes and bad performance on minority classes. This article proposes a new loss function called dynamic-recall focal loss (DRFL), which can solve the problem of imbalanced data categories in image classification and medical segmentation tasks. The DRFL assigns different weight coefficient to the classes according to their dynamic recall based on focal loss. Experimental results have shown that the newly proposed loss function DRFL can effectively improve the classification and segmentation accuracy of two imbalanced datasets.

The filamentous fungus Magnaporthe oryzae is widely recognized as a notorious plant pathogen responsible for causing rice blasts. With rapid advancements in molecular biology technologies, numerous regulatory mechanisms have been thoroughly investigated. However, most recent studies have predominantly focused on infection-related pathways or host defence mechanisms, which may be insufficient for developing novel structure-based prevention strategies. A substantial body of literature has utilized cryo-electron microscopy and X-ray diffraction to explore the relationships between functional components, shedding light on the identification of potential drug targets. Owing to the complexity of protein extraction and stochastic nature of crystallization, obtaining high-quality structures remains a significant challenge for the scientific community. Emerging computational tools such as AlphaFold for structural prediction, docking for interaction analysis, and molecular dynamics simulations to replicate in vivo conditions provide novel avenues for overcoming these challenges. In this review, we aim to consolidate the structural biological advancements in M. oryzae, drawing upon mature experimental experiences from other species such as Saccharomyces cerevisiae and mammals. We aim to explore the potential of protein construction to address the invasion and proliferation of M. oryzae, with the goal of identifying new drug targets and designing small-molecule compounds to manage this disease.

Abstract Ecosystem‐scale photosynthetic efficiency (EPE) is proposed as an effective indicator to quantify gross primary productivity (GPP), but how the coupling between EPE and GPP varies as vegetation resilience decreases has not been evaluated. Here, we quantified forest resilience with optimized Bayesian models. With the use of multisource satellite and modeling data, our study revealed that forests on the Loess Plateau and in the Qinba Mountains in China are experiencing rapid resilience loss and are already facing mortality warnings after 2010. Reductions in resilience also drove the marked decoupling of GPP from EPE. Notably, the decline in resilience was accompanied by a decrease in EPE in about 74% of the forests while GPP increased. The mechanism underlying this decoupling could be attributed to enhanced atmospheric water demand and soil water constraints. The dynamic relationships found here could help to improve forest mortality models and enhance photosynthesis‐based GPP evaluation.

Abstract Brown carbon (BrC) in China is of great interest to the regional and global climate due to its strong absorption of sunlight. However, the contribution of BrC to total carbonaceous aerosol light absorption and its direct radiative effects (DRE) in China remains largely uncertain. To better assess its climate impact in China, we develop an explicit BrC scheme and implement it in a global climate model, which includes optical parameters of primary BrC derived from local measurements, secondary BrC absorption, and a photobleaching parameterization of BrC. By comparing with multi‐type observational data, we find that with the implementation of this scheme, the model captures the seasonal variations of BrC light absorption well in China. The model estimates that BrC contributes 19% and 12% to the total light absorption of carbonaceous aerosol in China in winter and summer, resulting in 0.110 and 0.205 W m−2 of DRE, respectively.

Abstract Numerous Earth system models exhibit excessive aerosol effective forcing at the top of the atmosphere (TOA), including the Department of Energy's Energy Exascale Earth System Model (E3SM). Here, in the context of the E3SM version 3 effort, the predicted particle property (P3) stratiform cloud microphysics scheme and an enhanced deep convection parameterization suite (ZM_plus) are implemented into E3SM. The ZM_plus includes a convective cloud microphysics scheme, a multi‐scale coherent structure parameterization for mesoscale convective systems, and a revised cloud base mass flux formulation considering impacts of the large‐scale environment. The P3 scheme improved cloud and radiation particularly over the Northern Hemisphere and the frequency of heavy precipitation over the tropics, and the ZM_plus improved clouds in the tropics. P3 decreases aerosol effective forcing by 0.15 W m−2, while the ZM_plus increases it by 0.27 W m−2, resulting from excessive direct (0.31 W m−2) and indirect forcing (−1.79 W m−2). The excessive aerosol forcings are due to aerosol overestimation associated with insufficient aerosol wet removal. By improving the physical treatments in the aerosol wet removal, we effectively mitigate anthropogenic aerosol overestimation and thus attenuate direct (0.09 W m−2) and indirect aerosol forcing (−1.52 W m−2). Adjustment to primary organic matter hygroscopicity reduces direct and indirect forcing to more reasonable values: −0.13 W m−2 and −1.31 W m−2, respectively. On climatology, improved aerosol treatments mitigate overestimation of aerosol optical depth.

Small-molecule prodrug nanoassembly technology with a unique advantage in off-target toxicity reduction has been widely used for antitumor drug delivery. However, prodrug activation remains a rate-limiting step for exerting therapeutic actions, which requires to quickly reach the minimum valid concentrations of free drugs. Fortunately, we find that a natural compound (BL-193) selectively improves the chemotherapy sensitivity of breast cancer cells to podophyllotoxin (PPT) at ineffective dose concentrations. Based on this, we propose to combine prodrug nanoassembly with chemotherapy sensitization to fully unleash the chemotherapeutic potential of PPT. Specifically, a redox-sensitive prodrug (PSSF) of PPT is synthesized by coupling 9-fluorenyl-methanol (Fmoc-OH) with PPT linked via disulfide bond. Intriguingly, PSSF with a π-conjugated structure readily co-assembles with BL-193 into stable nanoassembly. Significantly, BL-193 serves as an excellent chemosensitizer that creates an ultra-low-dose chemotherapeutic window for PPT. Moreover, prodrug design and precise hybrid nanoassembly well manage off-target toxicity. As expected, such a BL-193-empowered prodrug nanoassembly elicits potent antitumor responses. This study offers a novel paradigm to magnify chemotherapy efficacy-toxicity benefits.

Background: This research aimed to create a predictive model and an innovative risk classification system for patients with gallbladder cancer who undergo radical surgery. Methods: A cohort of 1387 patients diagnosed with gallbladder cancer was selected from the SEER database. The researchers devised a prognostic tool known as a nomogram, which was subjected to assessment and fine-tuning using various statistical measures such as the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve, decision curve analysis (DCA), and risk stratification were included in the catalog of comparisons. An external validation set comprising 93 patients from Nanchong Central Hospital was gathered for evaluation purposes. Results: The nomogram effectively incorporated seven variables and demonstrated satisfactory discriminatory ability, as evidenced by the C-index (training cohort: 0.737, validation cohort: 0.730) and time-dependent AUC (>0.7). Additionally, calibration plots confirmed the excellent alignment between the nomogram and actual observations. Our investigation unveiled NRI scores of 0.79, 0.81, and 0.81 in the training group, while the validation group exhibited NRI values of 0.82, 0.77, and 0.78. Additionally, when evaluating CSS at three-, six-, and nine-year intervals using DCA curves, our established nomograms demonstrated significantly improved performance compared to the old model (P

IntroductionFusarium wilt (FW) caused by Fusarium oxysporum f. sp. cucumerinum (Foc) is a destructive soil-borne disease in cucumber (Cucumis sativus. L). However, there remains limited knowledge on the molecular mechanisms underlying FW resistance-mediated defense responses in cucumber.MethodsIn this study, metabolome and transcriptome profiling were carried out for two FW resistant (NR) and susceptible (NS), near isogenic lines (NILs) before and after Foc inoculation. NILs have shown consistent and stable resistance in multiple resistance tests conducted in the greenhouse and in the laboratory. A widely targeted metabolomic analysis identified differentially accumulated metabolites (DAMs) with significantly greater NR accumulation in response to Foc infection, including many phenolic acid and flavonoid compounds from the flavonoid biosynthesis pathway.ResultsTranscriptome analysis identified differentially expressed genes (DEGs) between the NILs upon Foc inoculation including genes for secondary metabolite biosynthesis and transcription factor genes regulating the flavonoid biosynthesis pathway. Joint analysis of the metabolomic and transcriptomic data identified DAMs and DEGs closely associated with the biosynthesis of phenolic acid and flavonoid DAMs. The association of these compounds with NR-conferred FW resistance was exemplified by in vivo assays. These assays found two phenolic acid compounds, bis (2-ethylhexyl) phthalate and diisooctyl phthalate, as well as the flavonoid compound gallocatechin 3-O-gallate to have significant inhibitory effects on Foc growth. The antifungal effects of these three compounds represent a novel finding.DiscussionTherefore, phenolic acids and flavonoids play important roles in NR mediated FW resistance breeding in cucumber.

Analcime plays a significant role in sandstone reservoirs as an authigenic diagenetic mineral in the Junggar Basin (northwestern China). However, the origin and controls on the reservoirs have received remarkably little attention. This study investigates the formation mechanism of analcime in the Middle Permian strata in the Jinan sag (southern Junggar Basin) through petrography and geochemistry. The results show that analcime is formed through early alkaline hydrolysis of volcanic materials under specific temperature and pressure conditions. The reservoir rocks primarily consist of various lithic sandstones, including volcanic debris such as basalt, andesite, and tuff. Analcime is characterized as rich in aluminium and poor in sodium, classified as low-silica analcime with a low Si-Al ratio (1.98–2.38). Furthermore, various other diagenetic minerals, such as glauconite, chlorite, albite, and calcite have been identified. The primary reservoir space chiefly consists of intragranular dissolved pores of analcime, while secondary pores are formed by intragranular pores of feldspar and lithic, along with some remaining intergranular pores. Cementation of analcime during early diagenesis changes primary pore structures and reduces reservoir properties. The low-silica analcime dissolves due to acidic pore fluids associated with three stages of oil and gas charging, transforming into albite and creating numerous secondary pores, thereby enhancing reservoir quality.

Porcine reproductive and respiratory syndrome virus (PRRSV) continues to cause significant economic losses to the global swine industry, yet effective prevention and control measures remain elusive. The development of novel antivirals is thus urgently needed. Rifampicin (RFP), a semisynthetic derivative of rifamycin, has been previously reported to inhibit the replication of certain mammalian DNA viruses as well as RNA viruses. In this study, we unveil RFP as a potent inhibitor of PRRSV both in Marc-145 cells (half-maximal inhibitory concentration 61.26 μM) and porcine alveolar macrophages (half-maximal inhibitory concentration 53.09 μM). The inhibitory effect of RFP occurred during viral replication rather than binding, internalization and release. We also demonstrated that RFP inhibits PRRSV proteins production in the early stage of infection, without inhibiting host protein synthesis. Moreover, RFP effectively restricted porcine epidemic diarrhea virus (PEDV) and porcine enteric alphacoronavirus (PEAV) infection in Vero cells. In summary, these findings indicate the promising potential of RFP as a therapeutic agent for PRRSV, PEDV and PEAV infection in pig farms.

Abstract BackgroundThe traditional clinical trial data collection process requires a clinical research coordinator who is authorized by the investigators to read from the hospital’s electronic medical record. Using electronic source data opens a new path to extract patients’ data from electronic health records (EHRs) and transfer them directly to an electronic data capture (EDC) system; this method is often referred to as eSource. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness. At the same time, improved data collection efficiency reduces clinical trial costs. ObjectiveThis study aims to explore how to extract clinical trial–related data from hospital EHR systems, transform the data into a format required by the EDC system, and transfer it into sponsors’ environments, and to evaluate the transferred data sets to validate the availability, completeness, and accuracy of building an eSource dataflow. MethodsA prospective clinical trial study registered on the Drug Clinical Trial Registration and Information Disclosure Platform was selected, and the following data modules were extracted from the structured data of 4 case report forms: demographics, vital signs, local laboratory data, and concomitant medications. The extracted data was mapped and transformed, deidentified, and transferred to the sponsor’s environment. Data validation was performed based on availability, completeness, and accuracy. ResultsIn a secure and controlled data environment, clinical trial data was successfully transferred from a hospital EHR to the sponsor’s environment with 100% transcriptional accuracy, but the availability and completeness of the data could be improved. ConclusionsData availability was low due to some required fields in the EDC system not being available directly in the EHR. Some data is also still in an unstructured or paper-based format. The top-level design of the eSource technology and the construction of hospital electronic data standards should help lay a foundation for a full electronic data flow from EHRs to EDC systems in the future.

Abstract Background Patients diagnosed with advanced stage cancer face an elevated risk of suicide. We aimed to develop a suicidal ideation (SI) risk prediction model in patients with advanced cancer for early warning of their SI and facilitate suicide prevention in this population. Patients and Methods We consecutively enrolled patients with multiple types of advanced cancers from 10 cancer institutes in China from August 2019 to December 2020. Demographic characteristics, clinicopathological data, and clinical treatment history were extracted from medical records. Symptom burden, psychological status, and SI were assessed using the MD Anderson Symptom Inventory (MDASI), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire‐9 (PHQ‐9), respectively. A multivariable logistic regression model was employed to establish the model structure. Results In total, 2814 participants were included in the final analysis. Nine predictors including age, sex, number of household members, history of previous chemotherapy, history of previous surgery, MDASI score, HADS‐A score, HADS‐D score, and life satisfaction were retained in the final SI prediction model. The model achieved an area under the curve (AUC) of 0.85 (95% confidential interval: 0.82–0.87), with AUCs ranging from 0.75 to 0.95 across 10 hospitals and higher than 0.83 for all cancer types. Conclusion This study built an easy‐to‐use, good‐performance predictive model for SI. Implementation of this model could facilitate the incorporation of psychosocial support for suicide prevention into the standard care of patients with advanced cancer.

Distant hybridization is a well-established technique in genetic breeding for cultivation of superior traits. Previous studies have shown that, hybrids derived from the cross between Schizothorax wangchiachii (♀) and Percocypris pingi (♂) are significantly different from their parents in terms of growth performance, feeding behavior and disease resistance. Both cellular and plasma components of blood are integral to immune system, and erythrocytes serve as the sole cells for transporting oxygen which makes energy available for the growth, development, immune responses and all other physiological activities. In order to understand ecological adaptive strategies and also the underlying physiological and biochemical mechanisms of fish with different characteristics, the present study used offsprings and hybrids produced by artificial reproduction of the same batch of domesticated S. wangchiachii, P. pingi broodstock as the experimental fish, and analyses of biochemical indexes, differential leucocytes counts and histological examination of liver sections were performed. Results showed that the proportions of lymphocytes and thrombocytes in P. pingi were significantly higher than those in S. wangchiachii (P

Abstract Objectives Patients with advanced colorectal cancer (CRC) have multiple concurrent physical and psychological symptoms. This study aimed to explore the relationship between anxiety, depression, and symptom burden in advanced CRC. Methods A multicenter cross‐sectional study was conducted in 10 cancer centers from geographically and economically diverse sites in China. A total of 454 patients with advanced CRC completed the Hospital Anxiety and Depression Scale and the MD Anderson Symptom Inventory. Multiple regression analysis was applied to explore the relationship between anxiety, depression and symptom burden. Results About one‐third of the patients showed symptoms of anxiety or depression. Patients with anxiety or depression reported significantly higher symptom burden than those without (p

There are two sets of thermal reservoirs with different rock types in the Gucheng geothermal field of Hebei Province, namely the Ordovician fractured carbonate thermal reservoir and the Neogene Guantao sandstone thermal reservoir, each developed using independent well networks. The energy demand per unit area in the region is high, and the existing geothermal development methods cannot meet the regional energy demand. For this type of block, combined with the development characteristics of different types of thermal reservoirs, numerical simulation methods were used to study the geothermal development trends of Ordovician fractured carbonate reservoirs and Neogene Guantao Formation sandstone reservoirs. When simulating sandstone thermal storage, priority should be given to demonstrating the optimal recharge flow rate, while in simulating carbonate thermal storage, the focus should be on considering the influence of fracture development direction on development trends. Through numerical simulation of thermal storage development and combined with well network design, the optimal well spacing, production reinjection flow rate, and reinjection temperature for two sets of thermal reservoirs developed using the same well network were determined. It is predicted that the average heat flow in the study can increase by 47.8 %. This study presents reasonable development technical countermeasures to maximize the heating capacity of the geothermal development zone and provides an effective reference for the efficient development of similar geothermal resources in the Bohai Bay Basin.

Abstract Objective Calcific aortic valve disease (CAVD) is the leading cause of angina, heart failure, and death from aortic stenosis. However, the molecular mechanisms of its progression, especially the complex disease-related transcriptional regulatory mechanisms, remain to be further elucidated. Methods This study used porcine valvular interstitial cells (PVIC) as a model. We used osteogenic induced medium (OIM) to induce calcium deposition in PVICs to calcify them, followed by basic fibroblast growth factor (bFGF) treatment to inhibit calcium deposition. Transcriptome sequencing was used to study the mRNA expression profile of PVICs and its related transcriptional regulation. We used DaPars to further examine alternative polyadenylation (APA) between different treatment groups. Results We successfully induced calcium deposition of PVICs through OIM. Subsequently, mRNA-seq was used to identify differentially expressed mRNAs for three different treatments: control, OIM-induced and OIM-induced bFGF treatment. Global APA events were identified in the OIM and bFGF treatment groups by bioinformatics analysis. Finally, it was discovered and proven that catalase (CAT) is one of the potential targets of bFGF-induced APA regulation. Conclusion We described a global APA change in a calcium deposition model related to CAVD. We revealed that transcriptional regulation of the CAT gene may contribute to bFGF-induced calcium deposition inhibition.

Abstract Airway epithelial transcriptome analysis of asthma patients with different severity was used to disentangle the immune infiltration mechanisms affecting asthma exacerbation, which may be advantageous to asthma treatment. Here we introduce various bioinformatics methods and develop two models: an OVA/CFA-induced neutrophil asthma mouse model and an LPS-induced human bronchial epithelial cell damage model. Our objective is to investigate the molecular mechanisms, potential targets, and therapeutic strategies associated with asthma severity. Multiple bioinformatics methods identify meaningful differences in the degree of neutrophil infiltration in asthma patients with different severity. Then, PTPRC, TLR2, MMP9, FCGR3B, TYROBP, CXCR1, S100A12, FPR1, CCR1 and CXCR2 are identified as the hub genes. Furthermore, the mRNA expression of 10 hub genes is determined in vivo and in vitro models. Reperixin is identified as a pivotal drug targeting CXCR1, CXCR2 and MMP9. We further test the potential efficiency of Reperixin in 16HBE cells, and conclude that Reperixin can attenuate LPS-induced cellular damage and inhibit the expression of them. In this study, we successfully identify and validate several neutrophilic signatures and targets associated with asthma severity. Notably, Reperixin displays the ability to target CXCR1, CXCR2, and MMP9, suggesting its potential therapeutic value for managing deteriorating asthma.

Abstract Background Emerging evidences suggest that aberrant metabolites contributes to the immunosuppressive microenvironment that leads to cancer immune evasion. Among tumor immunosuppressive cells, myeloid-derived suppressor cells (MDSCs) are pathologically activated and extremely immunosuppressive, which are closely associated with poor clinical outcomes of cancer patients. However, the correlation between MDSCs mediated immunosuppression and particular cancer metabolism remained elusive. Methods Spontaneous lung adenocarcinoma and subcutaneous mouse tumor models, gas chromatography–mass spectrometry (GC–MS) and immunofluorescence assay of patient-derived lung adenocarcinoma tissues, and flow cytometry, RNA sequencing and Western blotting of immune cells, were utilized. Results Metabolite profiling revealed a significant accumulation of acetic acids in tumor tissues from both patients and mouse model, which contribute to immune suppression and cancer progression significantly through free fatty acid receptor 2 (FFAR2). Furthermore, FFAR2 is highly expressed in the myeloid-derived suppressor cells (MDSCs) from the tumor of lung adenocarcinoma (LUAD) patients which is greatly associated with poor prognosis. Surprisingly, whole or myeloid Ffar2 gene deletion markedly inhibited urethane-induced lung carcinogenesis and syngeneic tumor growth with reduced MDSCs and increased CD8+ T cell infiltration. Mechanistically, FFAR2 deficiency in MDSCs significantly reduced the expression of Arg1 through Gαq/Calcium/PPAR-γ axis, which eliminated T cell dysfunction through relieving L-Arginine consumption in tumor microenvironment. Therefore, replenishment of L-Arginine or inhibition to PPAR-γ restored acetic acids/FFAR2 mediated suppression to T cells significantly. Finally, FFAR2 inhibition overcame resistance to immune checkpoint blockade through enhancing the recruitment and cytotoxicity of tumor-infiltrating T cells. Conclusion Altogether, our results demonstrate that the acetic acids/FFAR2 axis enhances MDSCs mediated immunosuppression through Gαq/calcium/PPAR-γ/Arg1 signaling pathway, thus contributing to cancer progression. Therefore, FFAR2 may serve as a potential new target to eliminate pathologically activated MDSCs and reverse immunosuppressive tumor microenvironment, which has great potential in improving clinical outcomes of cancer immunotherapy.

Abstract Background Little is understood about the association between psychosomatic symptoms and advanced cancer among older Chinese patients. Methods This secondary analysis was part of a multicenter cross-sectional study based on an electronic patient-reported outcome platform. Patients with advanced cancer were included between August 2019 and December 2020 in China. Participants (over 60 years) completed the MD Anderson Symptom Inventory (MDASI) and Hospital Anxiety and Depression Scale (HADS) to measure symptom burden. Network analysis was also conducted to investigate the network structure, centrality indices (strength, closeness, and betweenness) and network stability. Results A total of 1022 patients with a mean age of 66 (60–88) years were included; 727 (71.1%) were males, and 295 (28.9%) were females. A total of 64.9% of older patients with advanced cancer had one or more symptoms, and up to 80% had anxiety and depression. The generated network indicated that the physical symptoms, anxiety and depression symptom communities were well connected with each other. Based on an evaluation of the centrality indices, ‘distress/feeling upset’ (MDASI 5) appears to be a structurally important node in all three networks, and ‘I lost interest in my own appearance’ (HADS-D4) had the lowest centrality indices. The network stability was relatively high (> 0.7). Conclusion The symptom burden remains high in older patients with advanced cancer in China. Psychosomatic symptoms are highly interactive and often present as comorbidities. This network can be used to provide targeted interventions to optimize symptom management in older patients with advanced cancer in China. Trial registration Chinese Clinical Trial Registry (ChiCTR1900024957), registered on 06/12/2020.

To analyze the episodic alteration of Middle Permian carbonate reservoirs by complex hydrothermal fluid in southwestern Sichuan Basin, petrology, geochemistry, fluid inclusion and U-Pb dating researches are conducted. The fractures and vugs of Middle Permian Qixia–Maokou formations are filled with multi-stage medium−coarse saddle dolomites and associated hydrothermal minerals, which indicates that the early limestone/dolomite episodic alteration was caused by the large-scale, high-temperature, deep magnesium-rich brine along flowing channels such as basement faults or associated fractures under the tectonic compression and napping during the Indosinian. The time of magnesium-rich hydrothermal activity was from the Middle Triassic to the Late Triassic. The siliceous and calcite fillings were triggered by hydrothermal alteration in the Middle and Late Yanshanian Movement and Himalayan Movement. Hydrothermal dolomitization is controlled by fault, hydrothermal property, flowing channel and surrounding rock lithology, which occur as equilibrium effect of porosity and permeability. The thick massive grainstone/dolomites were mainly altered by modification such as hydrothermal dolomitization/recrystallization, brecciation and fracture-vugs filling. Early thin–medium packstones were mainly altered by dissolution and infilling of fracturing, bedding dolomitization, dissolution and associated mineral fillings. The dissolved vugs and fractures are the main reservoir space under hydrothermal conditions, and the connection of dissolved vugs and network fractures is favorable for forming high-quality dolomite reservoir. Hydrothermal dolomite reservoirs are developed within a range of 1 km near faults, with a thickness of 30–60 m. Hydrothermal dolomite reservoirs with local connected pore/vugs and fractures have exploration potential.

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students’ ability objectively by questionnaire or other assessment methods. Research in the field of brain has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Three navigation tasks of increasing cognitive difficulty were designed and a total of 41 subjects participated in the experiment. For the classification and tracking of the subjects’ EEG signals, a convolutional neural network (CNN)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC) is proposed in this paper to extract and classify the features of the subjects’ EEG signals. Furthermore, Spiking neural networks (SNN) -based NeuCube is used to assess the learning effectiveness and demonstrate cognitive processes, acknowledging that NeuCube is an excellent method to display the spatiotemporal differences between spikes emitted by neurons. The results of the classification experiment show that the cognitive training traces of different students in solving three navigational problems can be effectively distinguished. More importantly, new information about navigation is revealed through the analysis of feature vector visualization and model dynamics. This work provides a foundation for future research on cognitive navigation and the training of students’ navigational skills.

Abstract Background Chinese indigenous pigs in Yunnan exhibit considerable phenotypic diversity, but their population structure and the biological interpretation of signatures of artificial selection require further investigation. To uncover population genetic diversity, migration events, and artificial selection signatures in Chinese domestic pigs, we sampled 111 Yunnan pigs from four breeds in Yunnan which is considered to be one of the centres of livestock domestication in China, and genotyped them using Illumina Porcine SNP60K BeadChip. We then leveraged multiple bioinformatics database tools to further investigate the signatures and associated complex traits. Results Population structure and migration analyses showed that Diannanxiaoer pigs had different genetic backgrounds from other Yunnan pigs, and Gaoligongshan may undergone the migration events from Baoshan and Saba pigs. Intriguingly, we identified a possible common target of sharing artificial selection on a 265.09 kb region on chromosome 5 in Yunnan indigenous pigs, and the genes on this region were associated with cardiovascular and immune systems. We also detected several candidate genes correlated with dietary adaptation, body size (e.g., PASCIN1, GRM4, ITPR2), and reproductive performance. In addition, the breed-sharing gene MMP16 was identified to be a human-mediated gene. Multiple lines of evidence at the mammalian genome, transcriptome, and phenome levels further supported the evidence for the causality between MMP16 variants and the metabolic diseases, brain development, and cartilage tissues in Chinese pigs. Our results suggested that the suppression of MMP16 would directly lead to inactivity and insensitivity of neuronal activity and skeletal development in Chinese indigenous pigs. Conclusion In this study, the population genetic analyses and identification of artificial selection signatures of Yunnan indigenous pigs help to build an understanding of the effect of human-mediated selection mechanisms on phenotypic traits in Chinese indigenous pigs. Further studies are needed to fully characterize the process of human-mediated genes and biological mechanisms.

AbstractPancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death. Therefore, we intend to explore novel strategies against PDAC. The exosomes-based biomimetic nanoparticle is an appealing candidate served as a drug carrier in cancer treatment, due to its inherit abilities. In the present study, we designed dasatinib-loaded hybrid exosomes by fusing human pancreatic cancer cells derived exosomes with dasatinib-loaded liposomes, followed by characterization for particle size (119.9 ± 6.10 nm) and zeta potential (−11.45 ± 2.24 mV). Major protein analysis from western blot techniques reveal the presence of exosome marker proteins CD9 and CD81. PEGylated hybrid exosomes showed pH-sensitive drug release in acidic condition, benefiting drug delivery to acidic cancer environment. Dasatinib-loaded hybrid exosomes exhibited significantly higher uptake rates and cytotoxicity to parent PDAC cells by two-sample t-test or by one-way ANOVA analysis of variance, as compared to free drug or liposomal formulations. The results from our computational analysis demonstrated that the drug-likeness, ADMET, and protein-ligand binding affinity of dasatinib are verified successfully. Cancer derived hybrid exosomes may serve as a potential therapeutic candidate for pancreatic cancer treatment.

This study highlights the significance of high-quality development of energy (EHQD) in achieving carbon neutrality. Researchers have explored how to successfully achieve carbon neutrality, but few studies have examined the impact of the EHQD on carbon neutrality. Accordingly, while considering the high-quality development of China’s economy, this study constructs a comprehensive evaluation index system for China’s EHQD using the technique for order preference by similarity to an ideal solution (TOPSIS) method based on entropy weight to measure the EHQD level of energy in 30 provinces in China from 2004 to 2019. Considering the possibility of endogeneities, the two-stage least squares (2SLS) regression is applied to empirically analyze the impact of the EHQD on carbon neutrality. Cointegration tests show a long-term cointegration relationship between the EHQD, real GDP per capita, urbanization, traffic pressure, industrial structure, and CO2 emissions (CE). The 2SLS method empirically shows that the EHQD has a significant negative impact on CE. The EHQD can significantly reduce CE, which is conducive for China to achieve the goal of carbon neutrality. The results of this study indicate that to reduce CE and enable China to successfully achieve its goal of carbon neutrality by 2060, policymakers should take effective measures to promote the EHQD.

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Inconsistent reports exist on the relationships between key agronomic traits and maize yield. We performed a multivariate analysis of yield and 10 agronomic traits in 59 hybrids to explore maize yields in mountainous areas. The yield per plant (YP) was significantly and positively correlated with kernel weight (KW), growth period (GP), and kernel row number (KRN). KW and KRN had positive effects on YP, whereas kernel rows per ear (KRE) had a negative effect. GP indirectly affected YP. GP, KW, KRN, and ear length (EL) showed the highest grey relational degree with YP. The first four principal components cumulatively accounted for 73.36% of variation. EL, KW, plant height (PH), ear height (EH), GP, KRN, and YP contributed positively to the variation, whereas KRE, shelling percentage (SP), bald-tip length (BTL), and ear girth (EG) contributed negatively. Based on trait similarity, the 59 maize hybrids were classified into two clusters, Clusters I and II. A total of 11 traits were grouped into four clusters, Clusters A–D. Cluster D included KW, GP, KRN, EL, EH, PH, and YP, and the 22 maize hybrids in Cluster I performed better in these traits. These results provide a theoretical basis for the breeding of high-yield maize varieties in mountainous areas.

In China, most blueberry varieties are characterized by tightly clustered fruits, which pose challenges for achieving precise and non-destructive automated harvesting. This complexity limits the design of robots for this task. Therefore, this paper proposes adding a jetting step during harvesting to separate fruit clusters and increase the operational space for mechanical claws. First, a combined approach of flow field analysis and pressure-sensitive experiments was employed to establish design criteria for the number, diameter, and inclination angle parameters of two types of nozzles: flat tip and round tip. Furthermore, fruit was introduced, and a fluid–structure coupling method was employed to calculate the deformation of fruit stems. Simultaneously, a mechanical analysis was conducted to quantify the relationship between jet characteristics and separation gaps. Simulation and pressure-sensitive experiments show that as the number of holes increases and their diameter decreases, the nozzle’s convergence becomes stronger. The greater the inclination angle of the circular nozzle holes, the more the gas diverges. The analysis of the output characteristics of the working section indicates that the 8-hole 40° round nozzle is the optimal solution. At an air compressor working pressure of 0.5 MPa, force analysis and simulation results both show that it can increase the picking space for the mechanical claw by about 5–7 mm without damaging the blueberries in the jet area. The final field experiments show that the mean distance for Type I (mature fruit) is 5.41 mm, for Type II (red fruit) is 6.42 mm, and for Type III (green fruit) is 5.43 mm. The short and curved stems of the green fruit are less effective, but the minimum distance of 4.71 mm is greater than the claw wall thickness, meeting the design requirements.

Abstract The dust direct radiative effect (DRE) depends strongly on the dust particle size distribution (PSD) and complex refractive index (CRI). Although recent studies constrained the dust PSD in the models, its CRI uncertainties are still large. As a result, whether dust warms or cools the climate system remains unclear. Here, we estimate the dust DRE by employing the regionally‐dependent dust CRI based on global measurements. We find that new dust CRI significantly enhances the scattering of dust in the shortwave while reduces its absorption in the longwave, which is opposite to that caused by increasing the coarse and giant dust fraction via constraining the PSD. Constraining both PSD and CRI ultimately leads to a net dust DRE of −0.68 W m−2, a cooling stronger than current model estimates.

African swine fever (ASF) is an acute, hemorrhagic, and severe infectious disease caused by the ASF virus (ASFV). ASFV has evolved multiple strategies to escape host antiviral immune responses. Here, we reported that ASFV pB318L, a trans-geranylgeranyl-diphosphate synthase, reduced the expression of type I interferon (IFN-I) and IFN-stimulated genes (ISGs). Mechanically, pB318L not only interacted with STING to reduce the translocation of STING from the endoplasmic reticulum to the Golgi apparatus but also interacted with IFN receptors to reduce the interaction of IFNAR1/TYK2 and IFNAR2/JAK1. Of note, ASFV with interruption of B318L gene (ASFV-intB318L) infected PAMs produces more IFN-I and ISGs than that in PAMs infected with its parental ASFV HLJ/18 at the late stage of infection. Consistently, the pathogenicity of ASFV-intB318L is attenuated in piglets compared with its parental virus. Taken together, our data reveal that B318L gene may partially affect ASFV pathogenicity by reducing the production of IFN-I and ISGs. This study provides a clue to design antiviral agents or live attenuated vaccines to prevent and control ASF.

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

Anti-tumor angiogenesis therapy, targeting the suppression of blood vessel growth in tumors, presents a potent approach in the battle against cancer. Traditional therapies have primarily concentrated on single-target techniques, with a specific emphasis on targeting the vascular endothelial growth factor, but have not reached ideal therapeutic efficacy. In response to this issue, our study introduced a novel nanoparticle system known as CS-siRNA/PEITC&L-cRGD NPs. These chitosan-based nanoparticles have been recognized for their excellent biocompatibility and ability to deliver genes. To enhance their targeted delivery capability, they were combined with a cyclic RGD peptide (cRGD). Targeted co-delivery of gene and chemotherapeutic agents was achieved through the use of a negatively charged lipid shell and cRGD, which possesses high affinity for integrin αvβ3 overexpressed in tumor cells and neovasculature. In this multifaceted approach, co-delivery of VEGF siRNA and phenethyl isothiocyanate (PEITC) was employed to target both tumor vascular endothelial cells and tumor cells simultaneously. The co-delivery of VEGF siRNA and PEITC could achieve precise silencing of VEGF, inhibit the accumulation of HIF-1α under hypoxic conditions, and induce apoptosis in tumor cells. In summary, we have successfully developed a nanoparticle delivery platform that utilizes a dual mechanism of action of anti-tumor angiogenesis and pro-tumor apoptosis, which provides a robust and potent strategy for the delivery of anti-cancer therapeutics.

ARPC1B encodes the protein known as actin-related protein 2/3 complex subunit 1 B (ARPC1B), which controls actin polymerization in the human body. Although ARPC1B has been linked to several human malignancies, its function in these cancers remains unclear. TCGA, GTEx, CCLE, Xena, CellMiner, TISIDB, and molecular signature databases were used to analyze ARPC1B expression in cancers. Visualization of data was primarily achieved using R language, version 4.0. Nineteen tumors exhibited high levels of ARPC1B expression, which were associated with different tumor stages and significantly affected the prognosis of various cancers. The level of ARPC1B expression substantially connected the narrative of ARPC1B expression with several TMB cancers and showed significant changes in MSI. Additionally, tolerance to numerous anticancer medications has been linked to high ARPC1B gene expression. Using Gene Set Variation Analysis/Gene Set Enrichment Analysisanalysis and concentrating on Rectum adenocarcinoma (READ), we thoroughly examined the molecular processes of the ARPC1B gene in pan-cancer. Using WGCNA, we examined the co-expression network of READ and ARPC1B. Meanwhile, ten specimens were selected for immunohistochemical examination, which showed high expression of ARPC1B in READ. Human pan-cancer samples show higher ARPC1B expression than healthy tissues. In many malignancies, particularly READ, ARPC1B overexpression is associated with immune cell infiltration and a poor prognosis. These results imply that the molecular biomarker ARPC1B may be used to assess the prognosis and immune infiltration of patients with READ.

Epithelial growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have significantly enhanced the treatment outcomes in non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. However, the occurrence of acquired resistance to EGFR-TKIs is an unavoidable outcome observed in these patients. Disruption of the PI3K/AKT/mTOR signaling pathway can contribute to the emergence of resistance to EGFR TKIs in lung cancer. The emergence of PIK3CA mutations following treatment with EGFR-TKIs can lead to resistance against EGFR-TKIs. This review provides an overview of the current perspectives regarding the involvement of PI3K/AKT/mTOR signaling in the development of lung cancer. Furthermore, we outline the state-of-the-art therapeutic strategies targeting the PI3K/AKT/mTOR signaling pathway in lung cancer. We highlight the role of PIK3CA mutation as an acquired resistance mechanism against EGFR-TKIs in EGFR-mutant NSCLC. Crucially, we explore therapeutic strategies targeting PIK3CA-mediated resistance to EGFR TKIs in lung cancer, aiming to optimize the effectiveness of treatment.