Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
AIM:To investigate the clinical characteristics and treatment outcomes,including visual function and overall survival(OS)of patients with ocular adnexal diffuse large B-cell lymphoma(OA-DLBCL).METHODS:This retrospecti...AIM:To investigate the clinical characteristics and treatment outcomes,including visual function and overall survival(OS)of patients with ocular adnexal diffuse large B-cell lymphoma(OA-DLBCL).METHODS:This retrospective cohort study enrolled 29 patients diagnosed with OA-DLBCL based on histopathological biopsy between 2006 and 2023.Patients were stratified into two subgroups:primary OA-DLBCL(no prior history of lymphoma)and secondary OA-DLBCL(history of DLBCL at non-ocular adnexal sites).OS was defined as the time interval from OA-DLBCL diagnosis to death from any cause.Survival analysis was performed using the Kaplan–Meier method,and prognostic factors affecting OS were identified using multivariate Cox proportional hazards regression with a stepwise selection approach.RESULTS:The cohort included 24 patients with primary OA-DLBCL(13 males,11 females;mean age:61.36±18.29y)and 5 patients with secondary OA-DLBCL(2 males,3 females;mean age:50.94±18.17y).Among the primary OA-DLBCL subgroup,12 patients(50%)presented with advanced disease(Ann Arbor stage IIIE–IV),and 16 patients(66%)were classified as T4 disease according to the tumor-node-metastasis(TNM)staging system.The mean final visual acuity was 1.72±1.10 in the primary group and 0.90±1.18 in the secondary group.The 5-year OS rate for the entire cohort was 27.7%.Multivariate analysis identified five factors significantly associated with poor survival outcomes:epiphora[adjusted hazard ratio(aHR),36.95],atherosclerotic cardiovascular disease(aHR,10.08),human immunodeficiency virus(HIV)infection(aHR,12.47),M1 stage(aHR,6.99),and secondary OA-DLBCL(aHR,6.03;all P<0.05).The median OS was 1.68y for primary OA-DLBCL and 1.12y for secondary OA-DLBCL.CONCLUSION:A substantial proportion of patients with primary OA-DLBCL present with advanced-stage disease at diagnosis.Epiphora,atherosclerotic cardiovascular disease,HIV infection,M1 stage,and secondary OA-DLBCL are independent prognostic factors for poor survival outcomes.These findings emphasize the urgent need for optimized therapeutic strategies and early screening protocols to improve the management of OA-DLBCL,particularly in developing countries.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.De...The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.展开更多
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.展开更多
ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential sec...ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.展开更多
Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether ...Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.展开更多
The influence of ramps on the transient rolling contact characteristics and damage mechanisms of switch rails remains unclear,presenting substantial challenges to the safety of railway operations.To this end,this pape...The influence of ramps on the transient rolling contact characteristics and damage mechanisms of switch rails remains unclear,presenting substantial challenges to the safety of railway operations.To this end,this paper constructs a transient rolling contact finite element model of the wheel-rail in switch under different ramps using ANSYS/LSDYNA method,and analyzes the tribology and damage characteristics when the wheel passes through the switch at a uniform speed.Our research findings reveal that the vibration induced in the switch rail during the wheel load transfer process leads to a step-like increase in the contact force.Moreover,the interaction between the wheel and the rail primarily involves slip contact,which may significantly contribute to the formation of corrugations on the switch rail.Additionally,the presence of large ramps exacerbates switch rail wear and rolling contact fatigue,resulting in a notable 13.2%increase in switch rail damage under 40‰ramp conditions compared to flat(0‰ramp)conditions.Furthermore,the large ramps can alter the direction of crack propagation,ultimately causing surface spalling of the rail.Therefore,large ramps intensify the dynamic interactions during the wheel load transfer process,further aggravating the crack and spalling damage to the switch rails.展开更多
The ability to generate high pressures in a large-volume press(LVP)is crucial for the study of matter under extreme conditions.Here,we have achieved ultrahigh pressures of and 50 GPa,respectively,at room temperature a...The ability to generate high pressures in a large-volume press(LVP)is crucial for the study of matter under extreme conditions.Here,we have achieved ultrahigh pressures of and 50 GPa,respectively,at room temperature and a high temperature of 1900 K∼60within a millimeter-sized sample volume in a Kawai-type LVP(KLVP)using hard tungsten carbide(WC)and newly designed assem-blies.The introduction of electroconductive polycrystalline boron-doped diamond and dense alumina wrapped with Cu foils into a large conventional cell assembly enables the detection of resistance variations in the Fe_(2)O_(3) pressure standard upon compression.The efficiency of pressure generation in the newly developed cell assembly equipped with conventional ZK10F WC anvils is significantly higher than that of conventional assemblies with some ultrahard or tapered WC anvils.Our study has enabled the routine gener-ation of pressures exceeding 50 GPa within a millimeter-sized sample chamber that have been inaccessible with traditional KLVPs.This advance in high-pressure technology not only breaks a record for pressure generation in traditional KLVPs,but also opens up new avenues for exploration of the properties of the Earth’s deep interior and for the synthesis of novel materials at extreme high pressures.展开更多
In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach th...In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.展开更多
AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surfa...AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.展开更多
Large size titanium alloy parts are widely used in aerospace.However,they are difficult to manufacture using mechanical cutting technology because of severe tool wear.Electrochemical jet machining is a promising techn...Large size titanium alloy parts are widely used in aerospace.However,they are difficult to manufacture using mechanical cutting technology because of severe tool wear.Electrochemical jet machining is a promising technology to achieve high efficiency,because it has high machining flexibility and no machining tool wear.However,reports on the macro electrochemical jet machining of large size titanium alloy parts are very scarce,because it is difficult to achieve effective constraint of the flow field in macro electrochemical jet machining.In addition,titanium alloy is very sensitive to fluctuation of the flow field,and a turbulent flow field would lead to serious stray corrosion.This paper reports a series of investigations of the electrochemical jet machining of titanium alloy parts.Based on the flow analysis and experiments,the machining flow field was effectively constrained.TB6 titanium alloy part with a perimeter of one meter was machined.The machined surface was smooth with no obvious machining defects.The machining process was particularly stable with no obvious spark discharge.The research provides a reference for the application of electrochemical jet machining technology to achieve large allowance material removal in the machining of large titanium alloy parts.展开更多
BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patie...BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patient information needs.However,LLM use to deliver accurate and comprehensible IBD-related medical information has yet to be thoroughly investigated.AIM To assess the utility of three LLMs(ChatGPT-4.0,Claude-3-Opus,and Gemini-1.5-Pro)as a reference point for patients with IBD.METHODS In this comparative study,two gastroenterology experts generated 15 IBD-related questions that reflected common patient concerns.These questions were used to evaluate the performance of the three LLMs.The answers provided by each model were independently assessed by three IBD-related medical experts using a Likert scale focusing on accuracy,comprehensibility,and correlation.Simultaneously,three patients were invited to evaluate the comprehensibility of their answers.Finally,a readability assessment was performed.RESULTS Overall,each of the LLMs achieved satisfactory levels of accuracy,comprehensibility,and completeness when answering IBD-related questions,although their performance varies.All of the investigated models demonstrated strengths in providing basic disease information such as IBD definition as well as its common symptoms and diagnostic methods.Nevertheless,when dealing with more complex medical advice,such as medication side effects,dietary adjustments,and complication risks,the quality of answers was inconsistent between the LLMs.Notably,Claude-3-Opus generated answers with better readability than the other two models.CONCLUSION LLMs have the potential as educational tools for patients with IBD;however,there are discrepancies between the models.Further optimization and the development of specialized models are necessary to ensure the accuracy and safety of the information provided.展开更多
The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure cas...The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure casting gating system of a large thin-walled cabin casting.A high-quality dataset was established through orthogonal experiments combined with design criteria for the gating system.Spearman’s correlation analysis was used to select high-quality features.The gating system dimensions were predicted using a gated recurrent unit(GRU)recurrent neural network and an elastic network model.Using EasyCast and ProCAST casting software,a comparative analysis of the flow field,temperature field,and solidification field can be conducted to demonstrate the achievement of steady filling and top-down sequential solidification.Compared to the empirical formula method,this method eliminates trial-and-error iterations,reduces porosity,reduces casting defect volume from 11.23 cubic centimeters to 2.23 cubic centimeters,eliminates internal casting defects through the incorporation of an internally cooled iron,fulfilling the goal of intelligent gating system design.展开更多
BACKGROUND The incidence and mortality of colorectal cancer continue to rise.For early-stage colorectal cancer,endoscopic resection has become a preferred or important treatment option due to its significant advantage...BACKGROUND The incidence and mortality of colorectal cancer continue to rise.For early-stage colorectal cancer,endoscopic resection has become a preferred or important treatment option due to its significant advantages in operative time,extent of trauma,and medical costs.However,increasing lesion diameter significantly elevates the technical difficulty of endoscopic resection.Currently,robust evidence-based evidence regarding the upper size limit for safely and effectively resecting lesions endoscopically remains lacking.AIM To evaluate the efficacy and safety of endoscopic resection for colorectal lesions≥30 mm in diameter.METHODS This retrospective study reviewed data from 102 patients who underwent endoscopic resection for colorectal lesions measuring≥30 mm in diameter at General Hospital of Northern Theater Command between January 2023 and July 2024.RESULTS Among 102 patients who underwent endoscopic resection,99 received endoscopic submucosal dissection and 3 underwent endoscopic full-thickness resection.Four patients(3.9%)required conversion to surgical radical resection postoperatively.All patients exhibited favorable wound healing at the resection sites,and no long-term complications were observed during the 3-month postoperative colonoscopy follow-up.The primary perioperative complication was post-endoscopic submucosal dissection electrocoagulation syndrome(PEECS)(24/102,23.5%).Multivariate analysis identified lesion location in the transverse colon as an independent risk factor for PEECS occurrence(odds ratio=6.734,95%confidence interval:1.623-27.945,P=0.009).CONCLUSION Large colorectal lesion diameter does not constitute an absolute contraindication to endoscopic resection.Experienced endoscopic centers can achieve complete resection with a favorable efficacy and safety profile.Notably,lesion location in the transverse colon is identified as an independent risk factor for PEECS.展开更多
In order to adjust some properties of cement grout or concrete,some mineral admixtures are usually added in the preparation.Admixtures can reduce the cement consumption and save the cost,and also adjust the workabilit...In order to adjust some properties of cement grout or concrete,some mineral admixtures are usually added in the preparation.Admixtures can reduce the cement consumption and save the cost,and also adjust the workability of the material,improve the strength and durability of the cement stone,or reduce hydration heat of the composite cement.At present,the content of fly ash or slag is generally less than 50%among the composite cementitious materials that have been studied more,but there is little research on composite cementitious materials with large mineral admixture.In this paper,XRD,SEM,and adiabatic temperature rise tests were used to discuss hydration products and mechanism of composite cement grout with 90%content of fly ash and slag.The results show that the hydration of the composite cement grout is an alkali-activated hydration reaction,and the hydration products are mainly amorphous substances such as hydrated calcium silicate or hydrated calcium aluminate gel.The hydration reaction temperature rise is much lower than that of ordinary cement grout,and the time of the temperature peak is significantly delayed.展开更多
Background:The prognostic significance of the chemokine receptor CCR7 in diffuse large B-cell lymphoma(DLBCL)has been reported previously.However,the detailed mechanisms of CCR7 in DLBCL,particularly regarding its int...Background:The prognostic significance of the chemokine receptor CCR7 in diffuse large B-cell lymphoma(DLBCL)has been reported previously.However,the detailed mechanisms of CCR7 in DLBCL,particularly regarding its interaction with lenalidomide treatment,are not fully understood.Methods:Our study utilized bioinformatics approaches to identify hub genes in SU-DHL-2 cell lines treated with lenalidomide compared to control groups.Immunohistochemical data and clinical information from 122 patients with DLBCL were analyzed to assess the correlation of CCR7 and p-ERK1/2 expression with the prognosis of DLBCL.Furthermore,in vitro and in vivo experiments were conducted to clarify the role of CCR7 in the response of DLBCL to lenalidomide treatment.Results:Our bioinformatics analysis pinpointed CCR7 as a hub gene in the context of lenalidomide treatment in DLBCL.Notably,31.14%and 36.0%(44/122)of DLBCL cases showed positive expression for CCR7 and ERK1/2 respectively,establishing them as independent prognostic factors for adverse outcomes in DLBCL via multivariate Cox regression analysis.Additionally,our studies demonstrated that the external application of the protein CCL21 promoted proliferation,migration,invasion,and activation of the ERK1/2 pathway in SU-DHL-2 and OCI-LY3 cell lines with high levels of CCR7 expression.This effect was mitigated by CCR7 silencing through siRNA,application of ERK inhibitors,or lenalidomide treatment.In vivo experiments reinforced the efficacy of lenalidomide,significantly reducing tumor growth rate,tumor mass,serum total LDH levels,and expression of CCR7 and p-ERK1/2 in a SUDHL-2 xenograft model in nude mice(p<0.05).Conclusion:Our study clarifies the potential role of the CCL21/CCR7/ERK1/2 axis in the therapeutic effects of lenalidomide in DLBCL treatment.展开更多
Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have e...Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.展开更多
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金Supported by the Faculty of Medicine,Prince of Songkla University.Wainipitapong S has received grants from the Faculty of Medicine,Prince of Songkla University。
文摘AIM:To investigate the clinical characteristics and treatment outcomes,including visual function and overall survival(OS)of patients with ocular adnexal diffuse large B-cell lymphoma(OA-DLBCL).METHODS:This retrospective cohort study enrolled 29 patients diagnosed with OA-DLBCL based on histopathological biopsy between 2006 and 2023.Patients were stratified into two subgroups:primary OA-DLBCL(no prior history of lymphoma)and secondary OA-DLBCL(history of DLBCL at non-ocular adnexal sites).OS was defined as the time interval from OA-DLBCL diagnosis to death from any cause.Survival analysis was performed using the Kaplan–Meier method,and prognostic factors affecting OS were identified using multivariate Cox proportional hazards regression with a stepwise selection approach.RESULTS:The cohort included 24 patients with primary OA-DLBCL(13 males,11 females;mean age:61.36±18.29y)and 5 patients with secondary OA-DLBCL(2 males,3 females;mean age:50.94±18.17y).Among the primary OA-DLBCL subgroup,12 patients(50%)presented with advanced disease(Ann Arbor stage IIIE–IV),and 16 patients(66%)were classified as T4 disease according to the tumor-node-metastasis(TNM)staging system.The mean final visual acuity was 1.72±1.10 in the primary group and 0.90±1.18 in the secondary group.The 5-year OS rate for the entire cohort was 27.7%.Multivariate analysis identified five factors significantly associated with poor survival outcomes:epiphora[adjusted hazard ratio(aHR),36.95],atherosclerotic cardiovascular disease(aHR,10.08),human immunodeficiency virus(HIV)infection(aHR,12.47),M1 stage(aHR,6.99),and secondary OA-DLBCL(aHR,6.03;all P<0.05).The median OS was 1.68y for primary OA-DLBCL and 1.12y for secondary OA-DLBCL.CONCLUSION:A substantial proportion of patients with primary OA-DLBCL present with advanced-stage disease at diagnosis.Epiphora,atherosclerotic cardiovascular disease,HIV infection,M1 stage,and secondary OA-DLBCL are independent prognostic factors for poor survival outcomes.These findings emphasize the urgent need for optimized therapeutic strategies and early screening protocols to improve the management of OA-DLBCL,particularly in developing countries.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金supported by the National Key R&D Program of China under Grant No.2022YFB3103500the National Natural Science Foundation of China under Grants No.62402087 and No.62020106013+3 种基金the Sichuan Science and Technology Program under Grant No.2023ZYD0142the Chengdu Science and Technology Program under Grant No.2023-XT00-00002-GXthe Fundamental Research Funds for Chinese Central Universities under Grants No.ZYGX2020ZB027 and No.Y030232063003002the Postdoctoral Innovation Talents Support Program under Grant No.BX20230060.
文摘The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
文摘Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
文摘ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.
文摘Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.
基金Project(2023YFB2604304)supported by the National Key R&D Program of ChinaProjects(52122810,51978586,51778542,U23A20666,52472458)supported by the National Natural Science Foundation of China+1 种基金Project(K2022G034)supported by the Technology Research and Development Program of China National Railway Group Co.Ltd.Projects(2020JDJQ0033,2023NSFSC0884)supported by Sichuan Province Science and Technology Support Program,China。
文摘The influence of ramps on the transient rolling contact characteristics and damage mechanisms of switch rails remains unclear,presenting substantial challenges to the safety of railway operations.To this end,this paper constructs a transient rolling contact finite element model of the wheel-rail in switch under different ramps using ANSYS/LSDYNA method,and analyzes the tribology and damage characteristics when the wheel passes through the switch at a uniform speed.Our research findings reveal that the vibration induced in the switch rail during the wheel load transfer process leads to a step-like increase in the contact force.Moreover,the interaction between the wheel and the rail primarily involves slip contact,which may significantly contribute to the formation of corrugations on the switch rail.Additionally,the presence of large ramps exacerbates switch rail wear and rolling contact fatigue,resulting in a notable 13.2%increase in switch rail damage under 40‰ramp conditions compared to flat(0‰ramp)conditions.Furthermore,the large ramps can alter the direction of crack propagation,ultimately causing surface spalling of the rail.Therefore,large ramps intensify the dynamic interactions during the wheel load transfer process,further aggravating the crack and spalling damage to the switch rails.
基金supported by the National Key R&D Program of China(Grant No.2023YFA1406200)the National Natural Science Foundation of China(Grant Nos.42272041 and 52302043)+2 种基金the National Natural Science Foundation of China(Grant No.U23A20561)the Jilin University High-level Innovation Team Foundation(Grant No.2021TD–05)the Shanghai Synchrotron Radiation Facility(Grant Nos.2024-SSRF-PT-510031 and 505511).
文摘The ability to generate high pressures in a large-volume press(LVP)is crucial for the study of matter under extreme conditions.Here,we have achieved ultrahigh pressures of and 50 GPa,respectively,at room temperature and a high temperature of 1900 K∼60within a millimeter-sized sample volume in a Kawai-type LVP(KLVP)using hard tungsten carbide(WC)and newly designed assem-blies.The introduction of electroconductive polycrystalline boron-doped diamond and dense alumina wrapped with Cu foils into a large conventional cell assembly enables the detection of resistance variations in the Fe_(2)O_(3) pressure standard upon compression.The efficiency of pressure generation in the newly developed cell assembly equipped with conventional ZK10F WC anvils is significantly higher than that of conventional assemblies with some ultrahard or tapered WC anvils.Our study has enabled the routine gener-ation of pressures exceeding 50 GPa within a millimeter-sized sample chamber that have been inaccessible with traditional KLVPs.This advance in high-pressure technology not only breaks a record for pressure generation in traditional KLVPs,but also opens up new avenues for exploration of the properties of the Earth’s deep interior and for the synthesis of novel materials at extreme high pressures.
基金National Natural Science Foundation of china(No.42371446)Natural Science Foundatiorof Hubei Province(No.2024AFD412)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)(No.2024XLA17).
文摘In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.
基金Supported by National Natural Science Foundation of China(No.82160195,No.82460203)Degree and Postgraduate Education Teaching Reform Project of Jiangxi Province(No.JXYJG-2020-026).
文摘AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.
基金the National Natural Science Foundation of China(No.52205468)China Postdoctoral Science Foundation(No.2022M710061 and No.2023T160277)Natural Science Foundation of Jiangsu Province(No.BK20210755)。
文摘Large size titanium alloy parts are widely used in aerospace.However,they are difficult to manufacture using mechanical cutting technology because of severe tool wear.Electrochemical jet machining is a promising technology to achieve high efficiency,because it has high machining flexibility and no machining tool wear.However,reports on the macro electrochemical jet machining of large size titanium alloy parts are very scarce,because it is difficult to achieve effective constraint of the flow field in macro electrochemical jet machining.In addition,titanium alloy is very sensitive to fluctuation of the flow field,and a turbulent flow field would lead to serious stray corrosion.This paper reports a series of investigations of the electrochemical jet machining of titanium alloy parts.Based on the flow analysis and experiments,the machining flow field was effectively constrained.TB6 titanium alloy part with a perimeter of one meter was machined.The machined surface was smooth with no obvious machining defects.The machining process was particularly stable with no obvious spark discharge.The research provides a reference for the application of electrochemical jet machining technology to achieve large allowance material removal in the machining of large titanium alloy parts.
基金Supported by the China Health Promotion Foundation Young Doctors'Research Foundation for Inflammatory Bowel Disease,the Taishan Scholars Program of Shandong Province,China,No.tsqn202306343National Natural Science Foundation of China,No.82270578.
文摘BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patient information needs.However,LLM use to deliver accurate and comprehensible IBD-related medical information has yet to be thoroughly investigated.AIM To assess the utility of three LLMs(ChatGPT-4.0,Claude-3-Opus,and Gemini-1.5-Pro)as a reference point for patients with IBD.METHODS In this comparative study,two gastroenterology experts generated 15 IBD-related questions that reflected common patient concerns.These questions were used to evaluate the performance of the three LLMs.The answers provided by each model were independently assessed by three IBD-related medical experts using a Likert scale focusing on accuracy,comprehensibility,and correlation.Simultaneously,three patients were invited to evaluate the comprehensibility of their answers.Finally,a readability assessment was performed.RESULTS Overall,each of the LLMs achieved satisfactory levels of accuracy,comprehensibility,and completeness when answering IBD-related questions,although their performance varies.All of the investigated models demonstrated strengths in providing basic disease information such as IBD definition as well as its common symptoms and diagnostic methods.Nevertheless,when dealing with more complex medical advice,such as medication side effects,dietary adjustments,and complication risks,the quality of answers was inconsistent between the LLMs.Notably,Claude-3-Opus generated answers with better readability than the other two models.CONCLUSION LLMs have the potential as educational tools for patients with IBD;however,there are discrepancies between the models.Further optimization and the development of specialized models are necessary to ensure the accuracy and safety of the information provided.
基金supported by the National Natural Science Foundation of China(Nos.52074246,52275390,52375394)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)the Key R&D Program of Shanxi Province(No.202102050201011).
文摘The design of casting gating system directly determines the solidification sequence,defect severity,and overall quality of the casting.A novel machine learning strategy was developed to design the counter pressure casting gating system of a large thin-walled cabin casting.A high-quality dataset was established through orthogonal experiments combined with design criteria for the gating system.Spearman’s correlation analysis was used to select high-quality features.The gating system dimensions were predicted using a gated recurrent unit(GRU)recurrent neural network and an elastic network model.Using EasyCast and ProCAST casting software,a comparative analysis of the flow field,temperature field,and solidification field can be conducted to demonstrate the achievement of steady filling and top-down sequential solidification.Compared to the empirical formula method,this method eliminates trial-and-error iterations,reduces porosity,reduces casting defect volume from 11.23 cubic centimeters to 2.23 cubic centimeters,eliminates internal casting defects through the incorporation of an internally cooled iron,fulfilling the goal of intelligent gating system design.
基金Supported by the Shenyang Science and Technology of Liaoning Province,No.22-321-32-15.
文摘BACKGROUND The incidence and mortality of colorectal cancer continue to rise.For early-stage colorectal cancer,endoscopic resection has become a preferred or important treatment option due to its significant advantages in operative time,extent of trauma,and medical costs.However,increasing lesion diameter significantly elevates the technical difficulty of endoscopic resection.Currently,robust evidence-based evidence regarding the upper size limit for safely and effectively resecting lesions endoscopically remains lacking.AIM To evaluate the efficacy and safety of endoscopic resection for colorectal lesions≥30 mm in diameter.METHODS This retrospective study reviewed data from 102 patients who underwent endoscopic resection for colorectal lesions measuring≥30 mm in diameter at General Hospital of Northern Theater Command between January 2023 and July 2024.RESULTS Among 102 patients who underwent endoscopic resection,99 received endoscopic submucosal dissection and 3 underwent endoscopic full-thickness resection.Four patients(3.9%)required conversion to surgical radical resection postoperatively.All patients exhibited favorable wound healing at the resection sites,and no long-term complications were observed during the 3-month postoperative colonoscopy follow-up.The primary perioperative complication was post-endoscopic submucosal dissection electrocoagulation syndrome(PEECS)(24/102,23.5%).Multivariate analysis identified lesion location in the transverse colon as an independent risk factor for PEECS occurrence(odds ratio=6.734,95%confidence interval:1.623-27.945,P=0.009).CONCLUSION Large colorectal lesion diameter does not constitute an absolute contraindication to endoscopic resection.Experienced endoscopic centers can achieve complete resection with a favorable efficacy and safety profile.Notably,lesion location in the transverse colon is identified as an independent risk factor for PEECS.
文摘In order to adjust some properties of cement grout or concrete,some mineral admixtures are usually added in the preparation.Admixtures can reduce the cement consumption and save the cost,and also adjust the workability of the material,improve the strength and durability of the cement stone,or reduce hydration heat of the composite cement.At present,the content of fly ash or slag is generally less than 50%among the composite cementitious materials that have been studied more,but there is little research on composite cementitious materials with large mineral admixture.In this paper,XRD,SEM,and adiabatic temperature rise tests were used to discuss hydration products and mechanism of composite cement grout with 90%content of fly ash and slag.The results show that the hydration of the composite cement grout is an alkali-activated hydration reaction,and the hydration products are mainly amorphous substances such as hydrated calcium silicate or hydrated calcium aluminate gel.The hydration reaction temperature rise is much lower than that of ordinary cement grout,and the time of the temperature peak is significantly delayed.
基金supported by the Key Research and Development Program of Science and Technology Department of Guizhou Province(No.20204Y147).
文摘Background:The prognostic significance of the chemokine receptor CCR7 in diffuse large B-cell lymphoma(DLBCL)has been reported previously.However,the detailed mechanisms of CCR7 in DLBCL,particularly regarding its interaction with lenalidomide treatment,are not fully understood.Methods:Our study utilized bioinformatics approaches to identify hub genes in SU-DHL-2 cell lines treated with lenalidomide compared to control groups.Immunohistochemical data and clinical information from 122 patients with DLBCL were analyzed to assess the correlation of CCR7 and p-ERK1/2 expression with the prognosis of DLBCL.Furthermore,in vitro and in vivo experiments were conducted to clarify the role of CCR7 in the response of DLBCL to lenalidomide treatment.Results:Our bioinformatics analysis pinpointed CCR7 as a hub gene in the context of lenalidomide treatment in DLBCL.Notably,31.14%and 36.0%(44/122)of DLBCL cases showed positive expression for CCR7 and ERK1/2 respectively,establishing them as independent prognostic factors for adverse outcomes in DLBCL via multivariate Cox regression analysis.Additionally,our studies demonstrated that the external application of the protein CCL21 promoted proliferation,migration,invasion,and activation of the ERK1/2 pathway in SU-DHL-2 and OCI-LY3 cell lines with high levels of CCR7 expression.This effect was mitigated by CCR7 silencing through siRNA,application of ERK inhibitors,or lenalidomide treatment.In vivo experiments reinforced the efficacy of lenalidomide,significantly reducing tumor growth rate,tumor mass,serum total LDH levels,and expression of CCR7 and p-ERK1/2 in a SUDHL-2 xenograft model in nude mice(p<0.05).Conclusion:Our study clarifies the potential role of the CCL21/CCR7/ERK1/2 axis in the therapeutic effects of lenalidomide in DLBCL treatment.
文摘Significant advancements have been achieved in the field of Single Image Super-Resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art performance.Recent efforts have explored the incorporation of Transformers to augment network performance in SISR.However,the high computational cost of Transformers makes them less suitable for deployment on lightweight devices.Moreover,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel convolution.In this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel convolution.Specifically,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these features.In addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel dimensions.Experimental results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.
