Laryngeal cancer represents a common malignancy that originates from the larynx, with unfavorable prognosis. Herein, this study systematically analyzed the immune signatures of laryngeal cancer and ...to evaluate their roles on tumor progression.
Differentially expressed immune-related genes (IRGs) were screened between laryngeal cancer and normal tissues from TCGA dataset. Then, two prognosis-related IRGs AQP9 and ZAP70 were analyzed by a series of survival analysis. Based on them, molecular subtypes were constructed by unsupervised cluster analysis. Differences in survival outcomes, HLA expression and immune cell infiltrations were assessed between subtypes. Expression of AQP9 and ZAP70 was validated in laryngeal cancer tissues and cells by RT-qPCR and immunohistochemistry. After silencing and overexpressing AQP9 and ZAP70, CCK-8, EdU, wound healing and transwell assays were performed in TU212 and LCC cells.
Totally, 315 IRGs were abnormally expressed in laryngeal cancer. Among them, AQP9 and ZAP70 were distinctly correlated to patients' prognosis. Two subtypes were developed with distinct survival outcomes, HLA expression and immune microenvironment. Low expression of AQP9 and ZAP70 was confirmed in laryngeal cancer. AQP9 and ZAP70 up-regulation distinctly suppressed proliferation, migration, and invasion of laryngeal cancer cells. The opposite results were investigated when their knockdown.
Our findings revealed the roles of AQP9 and ZAP70 in progression of laryngeal cancer, and suggested that AQP9 and ZAP70 could potentially act as candidate immunotherapeutic targets for laryngeal cancer.
Off-site investment relations among enterprises often bring the flow of capital, logistics, talent, information, and technology across cities, representing a new avenue for studying the urban ...network. Therefore, an increasing number of studies are investigating urban networks from the perspective of firm relations. Most of these studies mainly use data from intra-firm branches or mega firms' investment to quantify the linkages among cities. However, these studies have neglected the linkages among small-sized enterprises that are often located in small and medium-sized cities, resulting in the lack of authenticity in establishing urban networks. Recently, a small amount of research has begun to use wholesale enterprises' off-site investment data to construct urban networks. However, such research has neglected the indirect linkages and transit effects on enterprise investment routes. Therefore, this study uses investment data of all-industry enterprises from the Industrial and Commercial Enterprise Registratio
With the imminent threat of the energy crises, innovation in energy technologies is happening world-wide. The aim is to reduce our reliance on fossil fuels. Electric vehicles with fuel-cells that use ...hydrogen as an energy carrier are touted to be one of the most important potential replacements of the gasoline vehicle in both future transportation scenarios and emerging smart energy grids. However, hydrogen storage is a major technical barrier that lies between where we are now and the mass application of hydrogen energy. Further exploration of onboard hydrogen storage systems (OHSS) is urgently needed and, in this regard, a comprehensive technology opportunity analysis will help. Hence, with this research, we drew on scientific papers and patents related to OHSS and developed a novel methodology for investigating the past, present, and future development trends in OHSS. Specifically, we constructed a heterogeneous knowledge network using a unique multi-component structure with three core components: hydrogen carriers, hydrogen storage materials, and fuel cells. From this network, we extracted both the developed and underdeveloped technological solutions in the field and applied a well-designed evaluation system and prediction model to score the future development potential of these technological solutions. What emerged was the most promising directions of research in the short, medium, and long term. The results show that our methodology can effectively identify technology opportunities in OHSS, along with providing valuable decision support to researchers and enterprise managers associated with the development and application of OHSS.
Aspirin is widely used to treat various clinical symptoms. Evidence suggests that aspirin has antiviral properties, but little is known about its specific effect against rotavirus. MA104, Caco-2, and ...CV-1 cells were infected with rotavirus, and aspirin was added after 12 h. Viral mRNA and titer levels were measured by qRT-PCR and immunofluorescence assays. For in vivo validation, forty specific-pathogen-free SD rats were randomly divided into oral aspirin (ASP) groups and control (NC) groups. 16 S rRNA gene sequencing was performed to identify gut microbiota. After 6 months of continuous ASP/NC administration, the rats were infected with rotavirus. Fecal samples were collected over a 30-day time course, and viral levels were quantified. Proinflammatory cytokines/chemokine levels were measured by ELISA. Aspirin inhibited rotavirus infection in cell lines and in rats. The effects of aspirin on viral replication were associated with the alteration of gut microbiota composition by aspirin, including increased abundance of Firmicutes and decreased abundance of Bacteroidetes after aspirin treatment. Mechanistically, aspirin reduced IL-2 and IL-10 levels, and increased IRF-1 and COX-2 levels. Aspirin blocked rotavirus replication in vitro and in vivo, which might be related to effects on IRF-1, COX-2, chemokines, and gut microbial composition. These results indicate that long-term oral aspirin administration reduces rotavirus infection. Intestinal virus infection may be suppressed in elderly patients who take aspirin for a long time. The change of their Gut microbiota may lead to functional disorder of the intestinal tract, which may provide some reference for clinical adjuvant probiotics treatment.
Positive data are very common in many scientific fields and applications; for these data, it is known that estimation and inference based on relative error criterion are superior to that of absolute ...error criterion. In prediction problems, conformal prediction provides a useful framework to construct flexible prediction intervals based on hypothesis testing, which has been actively studied in the past decade. In view of the advantages of the relative error criterion for regression problems with positive responses, in this paper, we combine the relative error criterion (REC) with conformal prediction to develop a novel REC-based predictive inference method to construct prediction intervals for the positive response. The proposed method satisfies the finite sample global coverage guarantee and to some extent achieves the local validity. We conduct extensive simulation studies and two real data analysis to demonstrate the competitiveness of the new proposed method.
Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. Usually, GTS is composed of offline predetermination and real-time scenario ...match. However, it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system. To improve efficiency of predetermination, this paper proposes a framework of knowledge fusion-based deep reinforcement learning (KF-DRL) for intelligent predetermination of GTS. First, the Markov Decision Process (MDP) for GTS problem is formulated based on transient instability events. Then, linear action space is developed to reduce dimensionality of action space for multiple controllable generators. Especially, KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process. This can enhance the efficiency and learning process. Moreover, the graph convolutional network (GCN) is introduced to the policy network for enhanced learning ability. Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.
With the development of science and technology, massive datasets stored in multiple machines are increasingly prevalent. It is known that traditional statistical methods may be infeasible for ...analyzing large datasets owing to excessive computing time, memory limitations, communication costs, and privacy concerns. This article develops divide‐and‐conquer empirical likelihood (DEL) and divide‐and‐conquer exponentially tilted empirical likelihood (DETEL) methods for the distributed computing setting. We investigate the theoretical properties of the DEL and DETEL estimators. In particular, we derive upper bounds for the mean squared errors of the DEL and DETEL estimators, and, under some mild conditions, we prove the consistency and the asymptotic normality of the proposed estimators. Simulation studies and a real data analysis are carried out to demonstrate the finite‐sample performance of the proposed methods.
Résumé
Avec le développement de la science et de la technologie, de plus en plus d'ensembles de données massives sont stockés dans plusieurs machines. Les méthodes statistiques traditionnelles ne sont pas en mesure d'analyser de tels ensembles massifs, et ce en raison du temps de calcul excessif, des limites de mémoire, des coûts de communication et des préoccupations relatives à la protection de la vie privée. Les auteurs de cet article développent dans un cadre de calcul distribué les méthodes de la vraisemblance empirique diviser‐pour‐régner (DEL) et la vraisemblance empirique diviser‐pour‐régner exponentiellement inclinée (DETEL). Ensuite, ils explorent quelques propriétés théoriques des estimateurs DEL et DETEL, dont les limites supérieures de leurs erreurs quadratiques moyennes ainsi que leur convergence et normalité asymptotique sous de faibles conditions de régularité. La mise en œuvre pratique des méthodes proposées est illustrée à travers une analyse de données réelles, en plus de simulations numériques qui démontrent leurs performances à taille finie.
Multiplicative regression models are useful for analyzing data with positive responses, such as wages, stock prices and lifetimes, that are particularly common in economic, financial, epidemiological ...and social studies. Recently, the least absolute relative error (LARE) estimation was proposed to be a useful alternative to the conventional least squares (LS) or least absolute deviation (LAD). However, one may resort to the time-consuming resampling methods for the inference of the LARE estimation. This paper proposes an empirical likelihood approach towards constructing confidence intervals/regions of the regression parameters for the multiplicative models. The major advantage of the proposal is its ability of internal studentizing to avoid density estimation. And it is computationally fast. Simulation studies investigate the effectiveness of the proposed method. An analysis of the body fat data is presented to illustrate the new method.
This paper considers the imbalanced binary classification problem by focusing on the application of the short-term rainfall forecasting in arid and semi-arid regions. Specifically, we present a novel ...boosting-type method by utilizing the generalized extreme value (GEV) distribution as the link function and applying a gradient tree boosting algorithm to capture complex interactions among covariates. The proposed method has several appealing advantages such as, it can identify rare rainfall events as well as quantifying the uncertainties; it is data-driven that without any assumption on the relationship between the covariates and the rainfall event; the fitted model is highly interpretable, making it a useful tool for studying the rainfall mechanisms in arid and semi-arid regions. Experiments on two real-world datasets show that our approach outperforms its competing methods.
•Propose the generalized extreme value distribution as the skewed link function.•Employ the gradient tree boosting to capture complex interactions among covariates.•Can identify the rare rainfall events as well as quantifying the uncertainties.•No assumption on the relationship between the covariates and the rainfall event.•The model for fitting the imbalanced data is highly interpretable.
Clustering is an important task in statistics and many other scientific fields. In this note, we propose an improved K-means clustering approach called 'enhanced shrinkage K-means' based on the ...James-Stein estimator and learning vector quantization (LVQ) algorithm. The basic idea of this new algorithm is taking into account of the strength of both unsupervised clustering and supervised classification methods, in which we shrink the clustering centers toward the prototype vector via James-Stein estimator. We carry out extensive simulation studies and real data analysis to evaluate the performance of this new approach, and obtain encouraging results.
