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The exponential distribution has always been prominent in various disciplines because of its wide range of applications. In this work, a generalization of the classical exponential distribution under a neutrosophic environment is scarcely presented. The mathematical properties of the neutrosophic exponential model are described in detail. The estimation of a neutrosophic parameter by the method of maximum likelihood is discussed and illustrated with examples. The suggested neutrosophic exponential distribution (NED) model involves the interval time it takes for certain particular events to occur. Thus, the proposed model may be the most widely used statistical distribution for the reliability problems. For conceptual understanding, a wide range of applications of the NED in reliability engineering is given, which indicates the circumstances under which the distribution is suitable. Furthermore, a simulation study has been conducted to assess the performance of the estimated neutrosophic parameter. Simulated results show that imprecise data with a larger sample size efficiently estimate the unknown neutrosophic parameter. Finally, a complex dataset on remission periods of cancer patients has been analyzed to identify the importance of the proposed model for real-world case studies.

The efficacy of checkpoint immunotherapy to non-small cell lung cancer (NSCLC) largely depends on the tumor microenvironment (TME). Here, we demonstrate that CCL7 facilitates anti-PD-1 therapy for the Kras LSL−G12D/+ Tp53 fl/fl (KP) and the Kras LSL−G12D/+ Lkb1 fl/fl (KL) NSCLC mouse models by recruiting conventional DC 1 (cDC1) into the TME to promote T cell expansion. CCL7 exhibits high expression in NSCLC tumor tissues and is positively correlated with the infiltration of cDC1 in the TME and the overall survival of NSCLC patients. CCL7 deficiency impairs the infiltration of cDC1 in the TME and the subsequent expansion of CD8 + and CD4 + T cells in bronchial draining lymph nodes and TME, thereby promoting tumor development in the KP mouse model. Administration of CCL7 into lungs alone or in combination with anti-PD-1 significantly inhibits tumor development and prolongs the survival of KP and KL mice. These findings suggest that CCL7 potentially serves as a biomarker and adjuvant for checkpoint immunotherapy of NSCLC. Only a limited proportion of patients with non-small cell lung cancer respond to anti-PD-1/PD-L1 immunotherapy. Here, the authors show that in autochthonous models of KRAS-mutated lung cancer, CCL7 promotes cDC1 infiltration into the lungs, sustaining antitumor immune responses and potentiating anti-PD1 treatment efficacy.

ObjectiveDyslipidemia is one of the major public health problems in China. It is characterized by multisystem dysregulation and inflammation, and oxidant/antioxidant balance has been suggested as an important factor for its initiation and progression. The objective of this study was to determine the relationship between prevalence of dyslipidemia and measured changes in the levels of proinflammatory cytokines (IL-6, TNF-a, and MCP-1), thiobarbituric acid-reactant substances (TBARS), and serum total antioxidant capacity (TAC) in serum samples.Study designA cross-sectional survey with a purposive sampling of 2,631 enrolled participants (age 18–85 years) was performed using the adult population of long-term residents of the municipality of east coast China in Fujian province between the years 2017 and 2019. Information on general health status, dyslipidemia prevalence, and selected mediators of inflammation was collected through a two-stage probability sampling design according to socioeconomic level, sex, and age.MethodsThe lipid profile was conducted by measuring the levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) with an autoanalyzer. Dyslipidemia was defined according to National Cholesterol Education Program Adult Treatment Panel III diagnostic criteria, and patients with it were identified by means of a computerized database. Serum parameters including IL-6/TNF-a/MCP-1, TBARS, and TAC were measured in three consecutive years. Familial history, education level, risk factors, etc. were determined. The association between dyslipidemia and serum parameters was explored using multivariable logistic regression models. Sociodemographic, age, and risk factors were also investigated among all participants.ResultsThe mean prevalence of various dyslipidemia in the population at baseline (2017) was as follows: dyslipidemias, 28.50%; hypercholesterolemia, 26.33%; high LDL-C, 26.10%; low HDL-C, 24.44%; and hypertriglyceridemia, 27.77%. A significant effect of aging was found among all male and female participants. The mean levels of serum Il-6/TNF-a/MCP-1 were significantly higher in all the types of dyslipidemia among male participants. Female participants with all types of dyslipidemia but low HDL-C showed an elevation of IL-6 and MCP-1 levels, and those with dyslipidemias and hypercholesterolemia presented higher levels of TNF-a compared to the normal participants. The oxidative stress marker TBARS increased among all types of dyslipidemia except hypertriglyceridemia. All participants with different types of dyslipidemia had a lower total antioxidant capacity. Correlation analysis showed that cytokines and TBARS were positively associated with age, obesity, and diabetes mellitus, but not sex, sedentary leisure lifestyle, hypertension, and CVD/CHD history. The activity of TAC was negatively associated with the above parameters.ConclusionsThe correlation between the prevalence of dyslipidemia and the modification of inflammation status was statistically significant. The levels of proinflammatory cytokines, oxidative stress, and antioxidant capacity in serum may reflect the severity of the lipid abnormalities. These promising results further warrant a thorough medical screening in enhanced anti-inflammatory and reduced oxidative stress to better diagnose and comprehensively treat dyslipidemia at an early stage.

Root pull-out resistance is an important index to measure the soil-fixing ability of roots. The study aims to investigate the root pull-out resistance and root surface microstructural characteristics of plants adapted to the Water-Level Fluctuation Zone (WLFZ) and provide a reference for the study of vegetation soil reinforcement capacity in the WLFZ of the Three Parallel Rivers area. The study subjects are the four-year-old Arundo donax ‘Versicolor’ , Cyperus involucratus , and Acorus calamus . The study employs the single root pull-out resistance experiments to determine their resistance. Additionally, SEM and paraffin sectioning methods were utilized to measure the microstructure of the root surface and to explore the differences in microstructure and their impact on the friction between the root and soil. The findings revealed (1) The failure modes of the single root pull-out experiments included both pull-out and breakage, with 70.83%, 81.48%, and 57.69% of the roots being broken for A. donax ‘Versicolor’ , C. involucratus , and A. calamus , respectively. (2)There were significant differences in the average maximum pull-out resistance and average frictional strength among the three plants (P < 0.05), with the average maximum pull-out resistance being A. donax ‘Versicolor’ (27.88 N) > C. involucratus (20.53 N) > A. calamus (13.75 N), and the average frictional strength was A. donax ‘Versicolor’ (43.48 Pa) > C. involucratus (31.77 Pa) > A. calamus (19.05 Pa). The root surface roughness also showed significant differences among the three plants (P < 0.05), with the surface roughness of A. donax ‘Versicolor’ (20.13%) > C. involucratus (16.12%) > A. calamus (9.23%). (3) The root system of A. donax ‘Versicolor’ was relatively rough, with dense depressions and protrusions. In contrast, the root system of A. calamus was relatively smooth with no significant depressions or protrusions, and C. involucratus was intermediate between the two. The results suggested that the maximum pull-out force of single roots for the three plants followed the order of A. donax ‘Versicolor’  >  C. involucratus > A. calamus . Moreover, the microstructure of the root surface had a significant impact on the maximum pull-out force of the roots, The rougher the root surface. The greater the single root drawing force.

China’s rich intangible cultural heritage (ICH) offers a unique context for examining the economic implications of cultural preservation. This study exploits the designation of National Representative ICH Items as a quasi-natural experiment and employs a difference-in-differences (DID) approach based on county-level data to assess the impact of ICH conservation on the urban–rural income gap. The results show that counties approved for the national ICH list experience, on average, a 2.4% smaller urban–rural income ratio than non-approved counties, suggesting that ICH protection contributes to narrowing income disparities. Mechanism analyses reveal that the promotion of new enterprise formation and the expansion of the service sector are key transmission channels. Heterogeneity analysis reveals that four categories of ICH items, including traditional dance, traditional music, folk art, and folk literature, and newly added ICH items exhibit more pronounced effects in reducing the urban–rural income gap. Complementary micro-level evidence demonstrates that ICH protection significantly raises rural residents’ wage, business, transfer, and property incomes, with stronger effects relative to their urban counterparts. These results highlight the potential of cultural heritage conservation as a pathway to inclusive and sustainable rural development.

Background Celery is a widely cultivated vegetable abundant in ascorbate (AsA), a natural plant antioxidant capable of scavenging free radicals generated by abiotic stress in plants. Ascorbate peroxidase (APX) is a plant antioxidant enzyme that is important in the synthesis of AsA and scavenging of excess hydrogen peroxide. However, the characteristics and functions of APX in celery remain unclear to date. Results In this study, a gene encoding APX was cloned from celery and named AgAPX1 . The transcription level of the AgAPX1 gene was significantly upregulated under drought stress. AgAPX1 was expressed in Escherichia coli BL21 (DE3) and purified. The predicted molecular mass of rAgAPX1 was 33.16 kDa, which was verified by SDS-PAGE assay. The optimum pH and temperature for rAgAPX1 were 7.0 and 55 °C, respectively. Transgenic Arabidopsis hosting the AgAPX1 gene showed elevated AsA content, antioxidant capacity and drought resistance. Less decrease in net photosynthetic rate, chlorophyll content, and relative water content contributed to the high survival rate of transgenic Arabidopsis lines after drought. Conclusions The characteristics of APX in celery were different from that in other species. The enhanced drought resistance of overexpressing AgAPX1 in Arabidopsis may be achieved by increasing the accumulation of AsA, enhancing the activities of various antioxidant enzymes, and promoting stomatal closure. Our work provides new evidence to understand APX and its response mechanisms to drought stress in celery.

The challenge to improve the clinical efficacy and enlarge the population that benefits from immune checkpoint inhibitors (ICIs) for non‐small‐cell lung cancer (NSCLC) is significant. Based on whole‐exosome sequencing analysis of biopsies from NSCLC patients before anti‐programmed cell death protein‐2 (PD‐1) treatment, we identified NLRP4 mutations in the responders with a longer progression‐free survival (PFS). Knockdown of NLRP4 in mouse Lewis lung cancer cell line enhanced interferon (IFN)‐α/β production through the cGAS‐STING‐IRF3/IRF7 axis and promoted the accumulation of intratumoral CD8+ T cells, leading to tumor growth retardation in vivo and a synergistic effect with anti‐PD‐ligand 1 therapy. This was consistent with clinical observations that more tumor‐infiltrating CD8+ T cells and elevated peripheral IFN‐α before receiving nivolumab treatment were associated with a longer PFS in NSCLC patients. Our study highlights the roles of tumor‐intrinsic NLRP4 in remodeling the immune contextures in the tumor microenvironment, making regional type I IFN beneficial for ICI treatment. Our study addressed novel mechanisms for overcoming failures of immune checkpoint inhibitor (ICI) therapy for non‐small‐cell lung cancer (NSCLC) through endogenous regulation of NLRP4 on type I interferon. This is realized mostly through reshaping the tumor microenvironment to a “hot” tumor status, which is beneficial to the treatment of anti‐programmed cell death protein‐1. Furthermore, we provided new evidence on type I interferon (regulated by NLRP4 in our study) as a potential immune adjuvant to improve the efficacy of NSCLC patients ineffective to ICI treatment.

Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification ( C -SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C -SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C -SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples, e.g., in-context learning. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks. In this paper, we aim at approaches adapting the foundation models for medical image classification and present a novel dataset and benchmark for the evaluation, i.e., examining the overall performance of accommodating the large-scale foundation models downstream on a set of diverse real-world clinical tasks. We collect five sets of medical imaging data from multiple institutes targeting a variety of real-world clinical tasks (22,349 images in total), i.e., thoracic diseases screening in X-rays, pathological lesion tissue screening, lesion detection in endoscopy images, neonatal jaundice evaluation, and diabetic retinopathy grading. Results of multiple baseline methods are demonstrated using the proposed dataset from both accuracy and cost-effective perspectives.

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.