Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal ce...Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal cells(MSCs)exhibited greater therapeutic efficacy than CD146-MSCs.These cells enhance epithelial repair through nuclear factor kappa B/cyclooxygenase-2-associated paracrine signaling and secretion of pro-angiogenic factors.We concur that MSCs hold significant promise for ARDS treatment;however,the heterogeneity of cell products is a translational barrier.Phenotype-aware strategies,such as CD146 enrichment,standardized potency assays,and extracellular vesicle profiling,are essential for improving the consistency of these studies.Further-more,advanced preclinical models,such as lung-on-a-chip systems,may provide more predictive insights into the therapeutic mechanisms.This article underscores the importance of CD146+MSCs in ARDS,emphasizes the need for precision in defining cell products,and discusses how integrating subset selection into translational pipelines could enhance the clinical impact of MSC-based therapies.展开更多
针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现...针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。展开更多
In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper...In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).展开更多
Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models.Organoids pres...Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models.Organoids preserve the complex tissue architecture and cellular diversity of human cancers,enabling more accurate predictions of tumor growth,metastasis,and drug responses.Integration with microfluidic platforms,such as organ-on-a-chip systems,further enhances the ability to model tumor-environment interactions in real-time.Organoids facilitate in-depth exploration of tumor heterogeneity,molecular mechanisms,and the development of personalized treatment strategies when coupled with multi-omics technologies.Organoids provide a platform for investigating tumor-immune cell interactions,which aid in the design and testing of immune-based therapies and vaccines.Taken together,these features position organoids as a transformative tool in advancing cancer research and precision medicine.展开更多
Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,rad...Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.展开更多
Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for sp...Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.展开更多
Zenith Tropospheric Delay(ZTD)is an important factor that restricts the high-precision positioning of global navigation satellite system(GNSS),and it is of great significance in establishing a real-time and highprecis...Zenith Tropospheric Delay(ZTD)is an important factor that restricts the high-precision positioning of global navigation satellite system(GNSS),and it is of great significance in establishing a real-time and highprecision ZTD model.However,existing ZTD models only consider the impact of linear terms on ZTD estimation,whereas the nonlinear factors have rarely been investigated before and thus become the focus of this study.A real-time and high-precision ZTD model for large height difference area is proposed by considering the linear and nonlinear characteristics of ZTD spatiotemporal variations and is called the realtime linear and nonlinearity ZTD(RLNZ)model.This model uses the ZTD estimated from the Global Pressure and Temperature 3(GPT3)model as the initial value.The linear impacts of periodic term and height on the estimation of ZTD difference between GNSS and GPT3 model are first considered.In addition,nonlinear factors such as geographical location and time are further used to fit the remaining nonlinear ZTD residuals using the general regression neural network method.Finally,the RLNZ-derived ZTD is obtained at an arbitrary location.The western United States,with height difference ranging from-500 to 4000 m,is selected,and the hourly ZTD of 484 GNSS stations provided by the Nevada Geodetic Laboratory(NGL)and the data of 9 radiosonde(RS)stations in the year 2021 are used.Experiment results show that a better performance of ZTD estimation can be retrieved from the proposed RLNZ model when compared with the GPT3 model.Statistical results show the averaged root mean square(RMS),Bias,and mean absolute error(MAE)of ZTD from GPT3 and RLNZ models are 33.7/0.8/25.7 mm and 22.6/0.1/17.4 mm,respectively.The average improvement rate of the RLNZ model is 33% when compared to the GPT3 model.Finally,the application of the proposed RLNZ model in simulated real-time Precise Point Positioning(PPP)indicates that the accuracy of PPP in N,E and U components is improved by 8%,2%,and 6% when compared with that from the GPT3-based PPP.Meanwhile,the convergence time in N and U components is improved by 23% and 7%,respectively.Such results verify the superiority of the proposed RLNZ model in retrieving realtime ZTD maps for GNSS positioning and navigation applications.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
The management of breast cancer,one of the most common and heterogeneous malignancies,has transformed with the advent of precision medicine.This review explores current developments in genetic profiling,molecular diag...The management of breast cancer,one of the most common and heterogeneous malignancies,has transformed with the advent of precision medicine.This review explores current developments in genetic profiling,molecular diagnostics,and targeted therapies that have revolutionized breast cancer treatment.Key innovations,such as cyclin-dependent kinases 4/6(CDK4/6)inhibitors,antibodydrug conjugates(ADCs),and immune checkpoint inhibitors(ICIs),have improved outcomes for hormone receptor-positive(HR+),HER2-positive(HER2+),and triple-negative breast cancer(TNBC)subtypes remarkably.Additionally,emerging treatments,such as PI3K inhibitors,poly(ADP-ribose)polymerase(PARP)inhibitors,and m RNA-based therapies,offer new avenues for targeting specific genetic mutations and improving treatment response,particularly in difficult-to-treat breast cancer subtypes.The integration of liquid biopsy technologies provides a non-invasive approach for real-time monitoring of tumor evolution and treatment response,thus enabling dynamic adjustments to therapy.Molecular imaging and artificial intelligence(AI)are increasingly crucial in enhancing diagnostic precision,personalizing treatment plans,and predicting therapeutic outcomes.As precision medicine continues to evolve,it has the potential to significantly improve survival rates,decrease recurrence,and enhance quality of life for patients with breast cancer.By combining cutting-edge diagnostics,personalized therapies,and emerging treatments,precision medicine can transform breast cancer care by offering more effective,individualized,and less invasive treatment options.展开更多
The increasing use of UAV-based LiDAR systems for high-resolution mapping highlights the need for reliable,field-validated accuracy assessment methods.This study presents a practical technique for evaluating geometric...The increasing use of UAV-based LiDAR systems for high-resolution mapping highlights the need for reliable,field-validated accuracy assessment methods.This study presents a practical technique for evaluating geometric and radiometric performance using georeferenced,high-reflectivity foil targets.The method enables precise extraction of target centers and correction of systematic georeferencing errors through 3D transformation.The approach was applied at the Tora Cement Factory in Cairo,Egypt—an industrial site with complex topography—using a DJI Matrice 300 RTK UAV equipped with the Zenmuse L1 LiDAR sensor and Zenmuse P1 photogrammetric camera.Three test flights were performed at altitudes of 50 m(nadir and oblique)and 70 m(oblique),with a high-resolution Structure-from-Motion(SfM)point cloud generated for reference.After transformation,the global RMSE of the LiDAR dataset was reduced to approximately 2.8∼3.2 cm,improving upon the raw uncorrected accuracy of up to 4.6 cm.Surface-wise comparisons showed RMSEs of 3.1 cm on flat areas,3.8 cm on rugged terrain,and 4.5 cm on vertical structures.Additionally,the RGB data embedded in the LiDAR point cloud exhibited a systematic spatial offset between 18 and 43 cm,with an average internal standard deviation near 5 cm,indicating a potential limitation for radiometric applications.The proposed method offers a cost-effective,accurate,and repeatable solution for UAV LiDAR validation and supports operational deployment,quality assurance,and system calibration in real-world scenarios.展开更多
In the era of precision medicine,the breast cancer surgical treatment field is gradually moving toward a de-escalation model.Through precise preoperative assessments and multidisciplinary decision-making,surgical trau...In the era of precision medicine,the breast cancer surgical treatment field is gradually moving toward a de-escalation model.Through precise preoperative assessments and multidisciplinary decision-making,surgical trauma can be decreased,and patients’quality of life can be improved by ensuring safety.Herein,we explore the axillary de-escalation surgery model for breast cancer.展开更多
Precision Psychiatry in Mood Disorders refers to the application of precision medicine principles in the field of mood disorders,such as depression and bipolar disorder.It involves the use of advanced technologies,bio...Precision Psychiatry in Mood Disorders refers to the application of precision medicine principles in the field of mood disorders,such as depression and bipolar disorder.It involves the use of advanced technologies,biomarkers,and personalized treatment approaches to improve the accuracy of diagnosis,prognosis,and treatment selection for individuals with mood disorders.展开更多
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金the Scientific and Technological Research Council of Türkiye(TÜBİTAK)Under the International Postdoctoral Research Fellowship Program(2219),No.1059B192400980the National Postdoctoral Research Fellowship Program(2218),No.122C158.
文摘Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal cells(MSCs)exhibited greater therapeutic efficacy than CD146-MSCs.These cells enhance epithelial repair through nuclear factor kappa B/cyclooxygenase-2-associated paracrine signaling and secretion of pro-angiogenic factors.We concur that MSCs hold significant promise for ARDS treatment;however,the heterogeneity of cell products is a translational barrier.Phenotype-aware strategies,such as CD146 enrichment,standardized potency assays,and extracellular vesicle profiling,are essential for improving the consistency of these studies.Further-more,advanced preclinical models,such as lung-on-a-chip systems,may provide more predictive insights into the therapeutic mechanisms.This article underscores the importance of CD146+MSCs in ARDS,emphasizes the need for precision in defining cell products,and discusses how integrating subset selection into translational pipelines could enhance the clinical impact of MSC-based therapies.
文摘针对PTP(precise time protocol)协议在应用层获取软件时间戳导致时钟同步精度下降的问题,提出一种基于MAC(media access control)层获取硬件时间戳的PTP同步优化方案。设计了以STM32F407微处理器为核心的PTP时钟应用平台,在MAC层实现了硬件时间戳获取,避免了由于协议栈软件处理延时产生的不确定性;针对PTP时钟晶振老化导致的时间同步偏差及网络延迟抖动问题,采用迭代方法优化了本地时钟频率调节算法,提高了频率校正精度。经实际测试,主从时钟偏差的RMS(root mean square)优于20 ns,提升了时钟同步精度。
文摘In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).
基金supported by the Chinese Academy of Medical Sciences(Grant No.2021RU002)Beijing Natural Science Foundation(Grant No.Z240013)+2 种基金National Natural Science Foundation of China(Grant Nos.82450111,82388102,82373416,and 92259303)Beijing Research Ward Excellence Program(Grant Nos.BRWEP2024W034080200 and BRWEP2024W034080204)Peking University People’s Hospital Research and Development Funds(Grant No.RZG2024-02).
文摘Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models.Organoids preserve the complex tissue architecture and cellular diversity of human cancers,enabling more accurate predictions of tumor growth,metastasis,and drug responses.Integration with microfluidic platforms,such as organ-on-a-chip systems,further enhances the ability to model tumor-environment interactions in real-time.Organoids facilitate in-depth exploration of tumor heterogeneity,molecular mechanisms,and the development of personalized treatment strategies when coupled with multi-omics technologies.Organoids provide a platform for investigating tumor-immune cell interactions,which aid in the design and testing of immune-based therapies and vaccines.Taken together,these features position organoids as a transformative tool in advancing cancer research and precision medicine.
基金Supported by the Natural Science Foundation of Jilin Province,No.YDZJ202401182ZYTSJilin Provincial Key Laboratory of Precision Infectious Diseases,No.20200601011JCJilin Provincial Engineering Laboratory of Precision Prevention and Control for Common Diseases,Jilin Province Development and Reform Commission,No.2022C036.
文摘Artificial intelligence(AI)is driving a paradigm shift in gastroenterology and hepa-tology by delivering cutting-edge tools for disease screening,diagnosis,treatment,and prognostic management.Through deep learning,radiomics,and multimodal data integration,AI has achieved diagnostic parity with expert cli-nicians in endoscopic image analysis(e.g.,early gastric cancer detection,colorectal polyp identification)and non-invasive assessment of liver pathologies(e.g.,fibrosis staging,fatty liver typing)while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and opti-mizing inflammatory bowel disease treatment responses.Despite these advance-ments challenges persist including limited model generalization due to frag-mented datasets,algorithmic limitations in rare conditions(e.g.,pediatric liver diseases)caused by insufficient training data,and unresolved ethical issues related to bias,accountability,and patient privacy.Mitigation strategies involve constructing standardized multicenter databases,validating AI tools through prospective trials,leveraging federated learning to address data scarcity,and de-veloping interpretable systems(e.g.,attention heatmap visualization)to enhance clinical trust.Integrating generative AI,digital twin technologies,and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI’s potential to redefine precision care for digestive disorders,improve global health outcomes,and reshape healthcare equity.
文摘Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.
基金supported by the National Natural Science Foundation of China(42274039)Shaanxi Provincial Innovation Capacity Support Plan Project(2023KJXX-050)+2 种基金The Open Grants of the State Key Laboratory of Severe Weather(2023LASW-B18)Scientific and technological research projects for major issues in military medicine and aviation medicine(2022ZZXM012)Local special scientific research plan project of Shaanxi Provincial Department of Education(22JE012)。
文摘Zenith Tropospheric Delay(ZTD)is an important factor that restricts the high-precision positioning of global navigation satellite system(GNSS),and it is of great significance in establishing a real-time and highprecision ZTD model.However,existing ZTD models only consider the impact of linear terms on ZTD estimation,whereas the nonlinear factors have rarely been investigated before and thus become the focus of this study.A real-time and high-precision ZTD model for large height difference area is proposed by considering the linear and nonlinear characteristics of ZTD spatiotemporal variations and is called the realtime linear and nonlinearity ZTD(RLNZ)model.This model uses the ZTD estimated from the Global Pressure and Temperature 3(GPT3)model as the initial value.The linear impacts of periodic term and height on the estimation of ZTD difference between GNSS and GPT3 model are first considered.In addition,nonlinear factors such as geographical location and time are further used to fit the remaining nonlinear ZTD residuals using the general regression neural network method.Finally,the RLNZ-derived ZTD is obtained at an arbitrary location.The western United States,with height difference ranging from-500 to 4000 m,is selected,and the hourly ZTD of 484 GNSS stations provided by the Nevada Geodetic Laboratory(NGL)and the data of 9 radiosonde(RS)stations in the year 2021 are used.Experiment results show that a better performance of ZTD estimation can be retrieved from the proposed RLNZ model when compared with the GPT3 model.Statistical results show the averaged root mean square(RMS),Bias,and mean absolute error(MAE)of ZTD from GPT3 and RLNZ models are 33.7/0.8/25.7 mm and 22.6/0.1/17.4 mm,respectively.The average improvement rate of the RLNZ model is 33% when compared to the GPT3 model.Finally,the application of the proposed RLNZ model in simulated real-time Precise Point Positioning(PPP)indicates that the accuracy of PPP in N,E and U components is improved by 8%,2%,and 6% when compared with that from the GPT3-based PPP.Meanwhile,the convergence time in N and U components is improved by 23% and 7%,respectively.Such results verify the superiority of the proposed RLNZ model in retrieving realtime ZTD maps for GNSS positioning and navigation applications.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.82103614 and 32171363)Natural Science Foundation of Fujian Province of China(Grant No.2021J05007)+4 种基金funding from the start-up fund for Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast CancerXiamen’s Key Laboratory of Precision Medicine for Endocrine-Related Cancersstart-up and supporting funds from the Third Affiliated Hospital of Kunming Medical University,Yunnan Cancer Hospital for Guo-Jun Zhang and Jing-Wen BaiKey Research and development program for social development of Yunnan Science and Technology Department(Grant No.202403AC100014-2)horizontal project funding from the Third Affiliated Hospital of Kunming Medical University(Grant Nos.20233160A0866 and 20243160A0511)。
文摘The management of breast cancer,one of the most common and heterogeneous malignancies,has transformed with the advent of precision medicine.This review explores current developments in genetic profiling,molecular diagnostics,and targeted therapies that have revolutionized breast cancer treatment.Key innovations,such as cyclin-dependent kinases 4/6(CDK4/6)inhibitors,antibodydrug conjugates(ADCs),and immune checkpoint inhibitors(ICIs),have improved outcomes for hormone receptor-positive(HR+),HER2-positive(HER2+),and triple-negative breast cancer(TNBC)subtypes remarkably.Additionally,emerging treatments,such as PI3K inhibitors,poly(ADP-ribose)polymerase(PARP)inhibitors,and m RNA-based therapies,offer new avenues for targeting specific genetic mutations and improving treatment response,particularly in difficult-to-treat breast cancer subtypes.The integration of liquid biopsy technologies provides a non-invasive approach for real-time monitoring of tumor evolution and treatment response,thus enabling dynamic adjustments to therapy.Molecular imaging and artificial intelligence(AI)are increasingly crucial in enhancing diagnostic precision,personalizing treatment plans,and predicting therapeutic outcomes.As precision medicine continues to evolve,it has the potential to significantly improve survival rates,decrease recurrence,and enhance quality of life for patients with breast cancer.By combining cutting-edge diagnostics,personalized therapies,and emerging treatments,precision medicine can transform breast cancer care by offering more effective,individualized,and less invasive treatment options.
文摘The increasing use of UAV-based LiDAR systems for high-resolution mapping highlights the need for reliable,field-validated accuracy assessment methods.This study presents a practical technique for evaluating geometric and radiometric performance using georeferenced,high-reflectivity foil targets.The method enables precise extraction of target centers and correction of systematic georeferencing errors through 3D transformation.The approach was applied at the Tora Cement Factory in Cairo,Egypt—an industrial site with complex topography—using a DJI Matrice 300 RTK UAV equipped with the Zenmuse L1 LiDAR sensor and Zenmuse P1 photogrammetric camera.Three test flights were performed at altitudes of 50 m(nadir and oblique)and 70 m(oblique),with a high-resolution Structure-from-Motion(SfM)point cloud generated for reference.After transformation,the global RMSE of the LiDAR dataset was reduced to approximately 2.8∼3.2 cm,improving upon the raw uncorrected accuracy of up to 4.6 cm.Surface-wise comparisons showed RMSEs of 3.1 cm on flat areas,3.8 cm on rugged terrain,and 4.5 cm on vertical structures.Additionally,the RGB data embedded in the LiDAR point cloud exhibited a systematic spatial offset between 18 and 43 cm,with an average internal standard deviation near 5 cm,indicating a potential limitation for radiometric applications.The proposed method offers a cost-effective,accurate,and repeatable solution for UAV LiDAR validation and supports operational deployment,quality assurance,and system calibration in real-world scenarios.
基金supported by grants from the Natural Science Foundation of Shandong Province(Grant No.ZR2024QH058).
文摘In the era of precision medicine,the breast cancer surgical treatment field is gradually moving toward a de-escalation model.Through precise preoperative assessments and multidisciplinary decision-making,surgical trauma can be decreased,and patients’quality of life can be improved by ensuring safety.Herein,we explore the axillary de-escalation surgery model for breast cancer.
文摘Precision Psychiatry in Mood Disorders refers to the application of precision medicine principles in the field of mood disorders,such as depression and bipolar disorder.It involves the use of advanced technologies,biomarkers,and personalized treatment approaches to improve the accuracy of diagnosis,prognosis,and treatment selection for individuals with mood disorders.
