Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous m...This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.展开更多
Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of...Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of the product to users’ needs. Product innovation needs a cognitive design method based on needs of variant users for the product personalization. In this paper, an open concept is introduced to provide ways to meet user’s individual need in product lifespan. It is for industries to propose product concepts based on open sources, develop and support the product on the public capability. Using the open concept in the product architecture, called open?architecture product(OAP), can improve the product personalization leading to massive product innovation. To promote this promise of the OAP, effective methods are discussed for the OAP development. This paper introduces research on OAPs using adaptable design methods to meet product personalization. Adaptable design is based on the modular structure for product adaptability using function modules and adaptable interfaces. The proposed method provides solutions for planning modules and implementation of OAPs. Methods of OAP module planning, detail and interface design are described for transformation of product concepts into physical structures. A multiple?purpose electrical car is developed in a case study to show effectiveness of the proposed method.展开更多
With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstre...With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstream practices in molecular breeding of livestock. However, these techniques only use information from genomic variation but not multi-omics information, thus do not fully explain the molecular basis of phenotypic variations in complex traits. In addition, the accuracy of breeding value estimation based on these techniques is occasionally controversial in different populations or varieties. Given the rapid development of high-throughput sequencing techniques and functional genome and dramatic reductions in the overall cost of sequencing, it is possible to clarify the interactions between genes and formation of phenotypes using massive sets of omic-level data from studies of the transcriptome, proteome, epigenome, and metabolome. During livestock breeding, multi-omics information regarding breeding populations and individuals should be taken into account. The interactive regulatory networks governing gene regulation and phenotype formation in diverse livestock population, varieties and species should be analyzed. In addition, a multi-omics regulatory breeding model should be constructed. Precision, population-personalized breeding is expected to become a crucial practice in future livestock breeding. Precision breeding of individuals can be achieved by combining population genomic information at multi-omics levels together with genomic selection and genome editing techniques.展开更多
Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this pa...Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this paper, we propose an evolutionary game model of privacy protection between firms and consumers based on e-commerce personalization. Evolutionary stable strategies（ESSs） are obtained from the equilibrium points according to the model analysis, and then simulation experiments are launched to validate the decision-making results and the influencing mechanism of various factors. The results show that the model can eventually evolve toward a win-win situation by wisely varying its various factors, such as ratios of initial strategies, cost of privacy protection, commodity prices, and other related factors. Further, we find that reducing the possibility of the privacy breach under the premise of privacy protection can help promote the e-commerce personalization.展开更多
The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in ...The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.展开更多
The user interesting degree evaluation index is designed to fulfill the users＇ real needs, which includes the user＇ attention degree of commodity, hot commodity and preferential commodity. User interesting degree mo...The user interesting degree evaluation index is designed to fulfill the users＇ real needs, which includes the user＇ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model （UIDM） is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes （branches） are covered in this paper. The improved e-catalog is more satisfied to users＇ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterDriscs.展开更多
This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the cat...This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.展开更多
Inflammatory bowel diseases(IBDs)are chronic inflammatory disorders of the intestinal tract that have emerged as a growing problem in industrialized countries.Knowledge of IBD pathogenesis is still incomplete,and the ...Inflammatory bowel diseases(IBDs)are chronic inflammatory disorders of the intestinal tract that have emerged as a growing problem in industrialized countries.Knowledge of IBD pathogenesis is still incomplete,and the most widely-accepted interpretation considers genetic factors,environmental stimuli,uncontrolled immune responses and altered intestinal microbiota composition as determinants of IBD,leading to dysfunction of the intestinal epithelial functions.In vitro models commonly used to study the intestinal barrier do not fully reflect the proper intestinal architecture.An important innovation is represented by organoids,3D in vitro cell structures derived from stem cells that can self-organize into functional organ-specific structures.Organoids may be generated from induced pluripotent stem cells or adult intestinal stem cells of IBD patients and therefore retain their genetic and transcriptomic profile.These models are powerful pharmacological tools to better understand IBD pathogenesis,to study the mechanisms of action on the epithelial barrier of drugs already used in the treatment of IBD,and to evaluate novel target-directed molecules which could improve therapeutic strategies.The aim of this review is to illustrate the potential use of organoids for therapy personalization by focusing on the most significant advances in IBD research achieved through the use of adult stem cells-derived intestinal organoids.展开更多
Cross-silo federated learning(FL),which benefits from relatively abundant data and rich computing power,is drawing increasing focus due to the significant transformations that foundation models(FMs)are instigating in ...Cross-silo federated learning(FL),which benefits from relatively abundant data and rich computing power,is drawing increasing focus due to the significant transformations that foundation models(FMs)are instigating in the artificial intelligence field.The intensified data heterogeneity issue of this area,unlike that in cross-device FL,is caused mainly by substantial data volumes and distribution shifts across clients,which requires algorithms to comprehensively consider the personalization and generalization balance.In this paper,we aim to address the objective of generalized and personalized federated learning(GPFL)by enhancing the global model’s cross-domain generalization capabilities and simultaneously improving the personalization performance of local training clients.By investigating the fairness of performance distribution within the federation system,we explore a new connection between generalization gap and aggregation weights established in previous studies,culminating in the fairness-guided federated training for generalization and personalization(FFT-GP)approach.FFT-GP integrates a fairness-aware aggregation(FAA)approach to minimize the generalization gap variance among training clients and a meta-learning strategy that aligns local training with the global model’s feature distribution,thereby balancing generalization and personalization.Our extensive experimental results demonstrate FFT-GP’s superior efficacy compared to existing models,showcasing its potential to enhance FL systems across a variety of practical scenarios.展开更多
Objectives:To explore the efficacy and safety of virtual reality(VR)in relieving negative emotions in patients with breast cancer with different personalities.Methods:A randomized controlled trial was conducted.Betwee...Objectives:To explore the efficacy and safety of virtual reality(VR)in relieving negative emotions in patients with breast cancer with different personalities.Methods:A randomized controlled trial was conducted.Between April 2023 and October 2023,we enrolled patients with breast cancer treated in the Department of Breast Cancer and Oncology at Sun Yat-Sen Memorial Hospital,Sun Yat-Sen University,Guangdong Province.The patients were randomly divided into an intervention group(n=118)and a control group(n=119)using block randomization.The intervention group received the VR intervention 3-5 times over 5±2 weeks using natural landscapes with music or relaxation guidance,and the duration of each VR intervention was 15±3 min.The control group received routine nursing care,including disease education and psychological counseling.Patients were assessed using the Type D Scale,Positive and Negative Affect Scale,and Distress Thermometer,and adverse events during the intervention were recorded.Results:Overall,85 patients completed the study(44 in the intervention group and 41 in the control group).Patients with Type D personalities showed more negative emotions[25.0(21.5,27.5)vs.19.0(16.0,24.0),P=0.001]and distressed attitudes[4.0(2.0,5.0)vs.3.0(1.0,4.0),P=0.020]with fewer positive emotions(27.2±5.6 vs.31.0±5.9,P=0.014)than those with non-Type D personalities.Total population analysis revealed no significant differences between the groups.However,in the subgroup analysis,patients with Type D personalities in the intervention group showed greater relief from negative emotions than those in the control group[median difference,-5.0(-9.0,-2.5)vs.-2.0(-4.0,2.0),P=0.046].No significant differences were found between groups of patients with non-Type D personality traits.The proportion of adverse events was not significantly different between groups(P=0.110).Conclusions:Breast cancer patients with Type D personalities suffer more severe negative emotions and distress,and more attention should be paid to them.VR intervention significantly and safely reduced negative emotions in patients with Type D personalities.展开更多
Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by...Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by using portable diagnostic devices,avoiding sending samples to the medical laboratories.It has been extensively explored for diagnosing and monitoring patients’diseases and health conditions with the assistance of development in biochemistry and microfluidics.Microfluidic paper-based analytical devices(μPADs)have gained dramatic popularity in POCT because of their simplicity,user-friendly,fast and accurate result reading and low cost.SeveralμPADs have been successfully commercialized and received excellent feedback during the past several decades.This review briefly discusses the main types ofμPADs,preparation methods and their detection principles,followed by a few representative examples.The future perspectives of the development inμPADs are also provided.展开更多
Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with...Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.展开更多
Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes.This review systematically analyzes the curr...Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes.This review systematically analyzes the current understanding of the bidirectional relationship between blood-brain barrier disruption and neuroinflammation in traumatic brain injury,along with emerging combination therapeutic strategies.Literature review indicates that blood-brain barrier disruption and neuroinflammatory responses are key pathological features following traumatic brain injury.In the acute phase after traumatic brain injury,the pathological characteristics include primary blood-brain barrier disruption and the activation of inflammatory cascades.In the subacute phase,the pathological features are characterized by repair mechanisms and inflammatory modulation.In the chronic phase,the pathological features show persistent low-grade inflammation and incomplete recovery of the blood-brain barrier.Various physiological changes,such as structural alterations of the blood-brain barrier,inflammatory cascades,and extracellular matrix remodeling,interact with each other and are influenced by genetic,age,sex,and environmental factors.The dynamic balance between blood-brain barrier permeability and neuroinflammation is regulated by hormones,particularly sex hormones and stress-related hormones.Additionally,the role of gastrointestinal hormones is receiving increasing attention.Current treatment strategies for traumatic brain injury include various methods such as conventional drug combinations,multimodality neuromonitoring,hyperbaric oxygen therapy,and non-invasive brain stimulation.Artificial intelligence also shows potential in treatment decision-making and personalized therapy.Emerging sequential combination strategies and precision medicine approaches can help improve treatment outcomes;however,challenges remain,such as inadequate research on the mechanisms of the chronic phase traumatic brain injury and difficulties with technology integration.Future research on traumatic brain injury should focus on personalized treatment strategies,the standardization of techniques,costeffectiveness evaluations,and addressing the needs of patients with comorbidities.A multidisciplinary approach should be used to enhance treatment and improve patient outcomes.展开更多
Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understan...Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.展开更多
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma...Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.展开更多
This manuscript explores the recent study by Cui et al which assessed the interplay between inflammatory cytokines and brain-derived neurotrophic factor(BDNF)levels in first-episode schizophrenia patients.The study re...This manuscript explores the recent study by Cui et al which assessed the interplay between inflammatory cytokines and brain-derived neurotrophic factor(BDNF)levels in first-episode schizophrenia patients.The study revealed that higher levels of interleukin-6 and tumor necrosis factor-αcorrelated with reduced BDNF levels and poorer cognitive performance.Schizophrenia is a severe psy-chiatric disorder impacting approximately 1%of the global population,charac-terized by positive symptoms(hallucinations and delusions),negative symptoms(diminished motivation and cognitive impairments)and disorganized thoughts and behaviors.Emerging research highlights the role of BDNF as a potential biomarker for early diagnosis and therapeutic targeting.The findings from Cui et al’s study suggest that targeting neuroinflammation and enhancing BDNF levels may improve cognitive outcomes.Effective treatment approaches involve a com-bination of pharmacological and non-pharmacological interventions tailored to individual patient needs.Hence,monitoring cognitive and neuroinflammatory markers is essential for improving patient outcomes and quality of life.Conse-quently,this manuscript highlights the need for an integrated approach to schizo-phrenia management,considering both clinical symptoms and underlying neuro-biological changes.展开更多
BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking rec...BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking recovery.OBJECTIVE:To determine whether early suspension-protected training with a personal assistant machine for stroke patients enhances walking ability and prevents muscle spasms.METHODS:Thirty-two early-stage stroke patients from Shenzhen University General Hospital and the China Rehabilitation Research Center were randomly assigned to the experimental group(n=16)and the control group(n=16).Both groups underwent 4 weeks of gait training under the suspension protection system for 30 minutes daily,5 days a week.The experimental group used the personal assistant machine during training.Three-dimensional gait analysis(using the Cortex motion capture system),Brunnstrom staging,Fugl-Meyer Assessment for lower limb motor function,Fugl-Meyer balance function,and the modified Ashworth Scale were evaluated within 1 week before the intervention and after 4 weeks of intervention.RESULTS AND CONCLUSION:After the 4-week intervention,all outcome measures showed significant changes in each group.The experimental group had a small but significant increase in the modified Ashworth Scale score(P<0.05,d=|0.15|),while the control group had a large significant increase(P<0.05,d=|1.48|).The experimental group demonstrated greater improvements in walking speed(16.5 to 38.44 cm/s,P<0.05,d=|4.01|),step frequency(46.44 to 64.94 steps/min,P<0.05,d=|2.32|),stride length(15.50 to 29.81 cm,P<0.05,d=|3.44|),and peak hip and knee flexion(d=|1.82|to|2.17|).After treatment,the experimental group showed significantly greater improvements than the control group in walking speed(38.44 vs.26.63 cm/s,P<0.05,d=|2.75|),stride length,peak hip and knee flexion(d=|1.31|to|1.45|),step frequency(64.94 vs.59.38 steps/min,P<0.05,d=|0.85|),and a reduced support phase(bilateral:24.31%vs.28.38%,P<0.05,d=|0.88|;non-paretic:66.19%vs.70.13%,P<0.05,d=|0.94|).For early hemiplegia,personal assistant machine-assisted gait training under the suspension protection system helps establish a correct gait pattern,prevents muscle spasms,and improves motor function.展开更多
The ongoing coronavirus disease 2019(COVID-19)pandemic has necessitated rapid advancements in therapeutic strategies,with dexamethasone emerging as a key treatment for severe cases.This editorial discusses the systema...The ongoing coronavirus disease 2019(COVID-19)pandemic has necessitated rapid advancements in therapeutic strategies,with dexamethasone emerging as a key treatment for severe cases.This editorial discusses the systematic review conducted by Sethi et al,published in the World Journal of Virology.The review critically examines the efficacy and safety of varying dosages of dexamethasone in severe COVID-19 patients,providing a comprehensive meta-analysis that underscores the current clinical recommendations favoring a low-dose regimen.Despite these findings,the review highlights the potential benefits of tailored dosages for specific patient subgroups,suggesting a need for personalized treatment approaches.This editorial expands on the implications of these findings,advocating for the integration of evolving clinical data into treatment protocols and calling for further research into patient-specific responses to therapy.It emphasizes the importance of adaptability and precision in pandemic response,urging the medical community to consider both the robustness of existing evidence and the potential for innovative approaches to enhance patient outcomes in the face of global health challenges.展开更多
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
文摘This comprehensive study investigates the multifaceted impact of AI-powered personalization on strategic communications, delving deeply into its opportunities, challenges, and future directions. Employing a rigorous mixed-methods approach, we conduct an in-depth analysis of the effects of AI-driven personalization on audience engagement, brand perception, and conversion rates across various industries and communication channels. Our findings reveal that while AI-powered personalization significantly enhances communication effectiveness and offers unprecedented opportunities for audience connection, it also raises critical ethical considerations and implementation challenges. The study contributes substantially to the growing body of literature on AI in communications, offering both theoretical insights and practical guidelines for professionals navigating this rapidly evolving landscape. Furthermore, we propose a novel framework for ethical AI implementation in strategic communications and outline a robust agenda for future research in this dynamic field.
基金Supported by Discovery Grants(Grant No.RGPIN-2015-04173)of the Natural Sciences and Engineering Research Council(NSERC)of CanadaNational Natural Science Foundation of China(Grant Nos.51375287,51505269)
文摘Product innovation is creation of new concepts to plan and realize technological and functional details in the product to satisfy market and customer needs. One of the key drivers to product innovation is reactions of the product to users’ needs. Product innovation needs a cognitive design method based on needs of variant users for the product personalization. In this paper, an open concept is introduced to provide ways to meet user’s individual need in product lifespan. It is for industries to propose product concepts based on open sources, develop and support the product on the public capability. Using the open concept in the product architecture, called open?architecture product(OAP), can improve the product personalization leading to massive product innovation. To promote this promise of the OAP, effective methods are discussed for the OAP development. This paper introduces research on OAPs using adaptable design methods to meet product personalization. Adaptable design is based on the modular structure for product adaptability using function modules and adaptable interfaces. The proposed method provides solutions for planning modules and implementation of OAPs. Methods of OAP module planning, detail and interface design are described for transformation of product concepts into physical structures. A multiple?purpose electrical car is developed in a case study to show effectiveness of the proposed method.
基金supported by National High Technology Plan of China (2013 AA102502)the National Natural Science Foundation of China (313300453)the National Key Basic Research Program of China (2015CB943101)
文摘With the rapid development of molecular biology and related disciplines, animal breeding has moved from conventional breeding to molecular breeding. Marker-assisted selection and genomic selection have become mainstream practices in molecular breeding of livestock. However, these techniques only use information from genomic variation but not multi-omics information, thus do not fully explain the molecular basis of phenotypic variations in complex traits. In addition, the accuracy of breeding value estimation based on these techniques is occasionally controversial in different populations or varieties. Given the rapid development of high-throughput sequencing techniques and functional genome and dramatic reductions in the overall cost of sequencing, it is possible to clarify the interactions between genes and formation of phenotypes using massive sets of omic-level data from studies of the transcriptome, proteome, epigenome, and metabolome. During livestock breeding, multi-omics information regarding breeding populations and individuals should be taken into account. The interactive regulatory networks governing gene regulation and phenotype formation in diverse livestock population, varieties and species should be analyzed. In addition, a multi-omics regulatory breeding model should be constructed. Precision, population-personalized breeding is expected to become a crucial practice in future livestock breeding. Precision breeding of individuals can be achieved by combining population genomic information at multi-omics levels together with genomic selection and genome editing techniques.
基金Supported by the National Natural Science Foundation of China(71571082,71471073)the Fundamental Research Funds for the Central Universities(CCNU14Z02016,CCNU15A02046)
文摘Personalized products and services in e-commerce bring consumers many new experiences, but also trigger a series of information security issues. Considering the bounded rationality of the game participants, in this paper, we propose an evolutionary game model of privacy protection between firms and consumers based on e-commerce personalization. Evolutionary stable strategies（ESSs） are obtained from the equilibrium points according to the model analysis, and then simulation experiments are launched to validate the decision-making results and the influencing mechanism of various factors. The results show that the model can eventually evolve toward a win-win situation by wisely varying its various factors, such as ratios of initial strategies, cost of privacy protection, commodity prices, and other related factors. Further, we find that reducing the possibility of the privacy breach under the premise of privacy protection can help promote the e-commerce personalization.
文摘The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.
基金the National Natural Science Foundation of China(Nos.70972094,71072077 and 71172043)the National Key Technology Research and Development Program of China(No.2011BAH16B02)the Fundamental Research Funds for the Central Universities of China(No.2010-II-11)
文摘The user interesting degree evaluation index is designed to fulfill the users＇ real needs, which includes the user＇ attention degree of commodity, hot commodity and preferential commodity. User interesting degree model （UIDM） is constructed to justify the value of user interesting degree; the personalization approach is presented; operations of add and delete nodes （branches） are covered in this paper. The improved e-catalog is more satisfied to users＇ needs and wants than the former e-catalog which stands for enterprises, and the improved one can complete the recommendation of related products of enterDriscs.
文摘This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.
文摘Inflammatory bowel diseases(IBDs)are chronic inflammatory disorders of the intestinal tract that have emerged as a growing problem in industrialized countries.Knowledge of IBD pathogenesis is still incomplete,and the most widely-accepted interpretation considers genetic factors,environmental stimuli,uncontrolled immune responses and altered intestinal microbiota composition as determinants of IBD,leading to dysfunction of the intestinal epithelial functions.In vitro models commonly used to study the intestinal barrier do not fully reflect the proper intestinal architecture.An important innovation is represented by organoids,3D in vitro cell structures derived from stem cells that can self-organize into functional organ-specific structures.Organoids may be generated from induced pluripotent stem cells or adult intestinal stem cells of IBD patients and therefore retain their genetic and transcriptomic profile.These models are powerful pharmacological tools to better understand IBD pathogenesis,to study the mechanisms of action on the epithelial barrier of drugs already used in the treatment of IBD,and to evaluate novel target-directed molecules which could improve therapeutic strategies.The aim of this review is to illustrate the potential use of organoids for therapy personalization by focusing on the most significant advances in IBD research achieved through the use of adult stem cells-derived intestinal organoids.
基金Project supported by the National Key R&D Program of China(No.2022ZD0160702)the STCSM(Nos.22511106101,18DZ2270700,and 21DZ1100-100)+1 种基金the 111 Plan(No.BP0719010)the State Key Laboratory of UHD Video and Audio Production and Presentation。
文摘Cross-silo federated learning(FL),which benefits from relatively abundant data and rich computing power,is drawing increasing focus due to the significant transformations that foundation models(FMs)are instigating in the artificial intelligence field.The intensified data heterogeneity issue of this area,unlike that in cross-device FL,is caused mainly by substantial data volumes and distribution shifts across clients,which requires algorithms to comprehensively consider the personalization and generalization balance.In this paper,we aim to address the objective of generalized and personalized federated learning(GPFL)by enhancing the global model’s cross-domain generalization capabilities and simultaneously improving the personalization performance of local training clients.By investigating the fairness of performance distribution within the federation system,we explore a new connection between generalization gap and aggregation weights established in previous studies,culminating in the fairness-guided federated training for generalization and personalization(FFT-GP)approach.FFT-GP integrates a fairness-aware aggregation(FAA)approach to minimize the generalization gap variance among training clients and a meta-learning strategy that aligns local training with the global model’s feature distribution,thereby balancing generalization and personalization.Our extensive experimental results demonstrate FFT-GP’s superior efficacy compared to existing models,showcasing its potential to enhance FL systems across a variety of practical scenarios.
基金supported by a project of the National Natural Science Foundation of China:Research on the integration of artificial intelligence and virtual reality technology to promote psychological rehabilitation of breast cancer patients with different personalities(project approval no.82073408).
文摘Objectives:To explore the efficacy and safety of virtual reality(VR)in relieving negative emotions in patients with breast cancer with different personalities.Methods:A randomized controlled trial was conducted.Between April 2023 and October 2023,we enrolled patients with breast cancer treated in the Department of Breast Cancer and Oncology at Sun Yat-Sen Memorial Hospital,Sun Yat-Sen University,Guangdong Province.The patients were randomly divided into an intervention group(n=118)and a control group(n=119)using block randomization.The intervention group received the VR intervention 3-5 times over 5±2 weeks using natural landscapes with music or relaxation guidance,and the duration of each VR intervention was 15±3 min.The control group received routine nursing care,including disease education and psychological counseling.Patients were assessed using the Type D Scale,Positive and Negative Affect Scale,and Distress Thermometer,and adverse events during the intervention were recorded.Results:Overall,85 patients completed the study(44 in the intervention group and 41 in the control group).Patients with Type D personalities showed more negative emotions[25.0(21.5,27.5)vs.19.0(16.0,24.0),P=0.001]and distressed attitudes[4.0(2.0,5.0)vs.3.0(1.0,4.0),P=0.020]with fewer positive emotions(27.2±5.6 vs.31.0±5.9,P=0.014)than those with non-Type D personalities.Total population analysis revealed no significant differences between the groups.However,in the subgroup analysis,patients with Type D personalities in the intervention group showed greater relief from negative emotions than those in the control group[median difference,-5.0(-9.0,-2.5)vs.-2.0(-4.0,2.0),P=0.046].No significant differences were found between groups of patients with non-Type D personality traits.The proportion of adverse events was not significantly different between groups(P=0.110).Conclusions:Breast cancer patients with Type D personalities suffer more severe negative emotions and distress,and more attention should be paid to them.VR intervention significantly and safely reduced negative emotions in patients with Type D personalities.
文摘Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by using portable diagnostic devices,avoiding sending samples to the medical laboratories.It has been extensively explored for diagnosing and monitoring patients’diseases and health conditions with the assistance of development in biochemistry and microfluidics.Microfluidic paper-based analytical devices(μPADs)have gained dramatic popularity in POCT because of their simplicity,user-friendly,fast and accurate result reading and low cost.SeveralμPADs have been successfully commercialized and received excellent feedback during the past several decades.This review briefly discusses the main types ofμPADs,preparation methods and their detection principles,followed by a few representative examples.The future perspectives of the development inμPADs are also provided.
文摘Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.
基金supported by Open Scientific Research Program of Military Logistics,No.BLB20J009(to YZhao).
文摘Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression and treatment outcomes.This review systematically analyzes the current understanding of the bidirectional relationship between blood-brain barrier disruption and neuroinflammation in traumatic brain injury,along with emerging combination therapeutic strategies.Literature review indicates that blood-brain barrier disruption and neuroinflammatory responses are key pathological features following traumatic brain injury.In the acute phase after traumatic brain injury,the pathological characteristics include primary blood-brain barrier disruption and the activation of inflammatory cascades.In the subacute phase,the pathological features are characterized by repair mechanisms and inflammatory modulation.In the chronic phase,the pathological features show persistent low-grade inflammation and incomplete recovery of the blood-brain barrier.Various physiological changes,such as structural alterations of the blood-brain barrier,inflammatory cascades,and extracellular matrix remodeling,interact with each other and are influenced by genetic,age,sex,and environmental factors.The dynamic balance between blood-brain barrier permeability and neuroinflammation is regulated by hormones,particularly sex hormones and stress-related hormones.Additionally,the role of gastrointestinal hormones is receiving increasing attention.Current treatment strategies for traumatic brain injury include various methods such as conventional drug combinations,multimodality neuromonitoring,hyperbaric oxygen therapy,and non-invasive brain stimulation.Artificial intelligence also shows potential in treatment decision-making and personalized therapy.Emerging sequential combination strategies and precision medicine approaches can help improve treatment outcomes;however,challenges remain,such as inadequate research on the mechanisms of the chronic phase traumatic brain injury and difficulties with technology integration.Future research on traumatic brain injury should focus on personalized treatment strategies,the standardization of techniques,costeffectiveness evaluations,and addressing the needs of patients with comorbidities.A multidisciplinary approach should be used to enhance treatment and improve patient outcomes.
基金funded by NIH-NIA R01AG061708 (to PHO)Patrick Grange Memorial Foundation (to PHO)+1 种基金A Long Swim (to PHO)CureSPG4 Foundation (to PHO)。
文摘Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein–protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as “causative” for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration–approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.
文摘Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.
基金Supported by Basic Science Research Program Through the National Research Foundation of Korea(NRF)Funded By the Ministry of Education,No.NRF-RS-2023-00237287.
文摘This manuscript explores the recent study by Cui et al which assessed the interplay between inflammatory cytokines and brain-derived neurotrophic factor(BDNF)levels in first-episode schizophrenia patients.The study revealed that higher levels of interleukin-6 and tumor necrosis factor-αcorrelated with reduced BDNF levels and poorer cognitive performance.Schizophrenia is a severe psy-chiatric disorder impacting approximately 1%of the global population,charac-terized by positive symptoms(hallucinations and delusions),negative symptoms(diminished motivation and cognitive impairments)and disorganized thoughts and behaviors.Emerging research highlights the role of BDNF as a potential biomarker for early diagnosis and therapeutic targeting.The findings from Cui et al’s study suggest that targeting neuroinflammation and enhancing BDNF levels may improve cognitive outcomes.Effective treatment approaches involve a com-bination of pharmacological and non-pharmacological interventions tailored to individual patient needs.Hence,monitoring cognitive and neuroinflammatory markers is essential for improving patient outcomes and quality of life.Conse-quently,this manuscript highlights the need for an integrated approach to schizo-phrenia management,considering both clinical symptoms and underlying neuro-biological changes.
文摘BACKGROUND:Hemiplegia,a prevalent stroke-related condition,is often studied for motor dysfunction;however,spasticity remains under-researched.Abnormal muscle tone significantly hinders hemiplegic patients’walking recovery.OBJECTIVE:To determine whether early suspension-protected training with a personal assistant machine for stroke patients enhances walking ability and prevents muscle spasms.METHODS:Thirty-two early-stage stroke patients from Shenzhen University General Hospital and the China Rehabilitation Research Center were randomly assigned to the experimental group(n=16)and the control group(n=16).Both groups underwent 4 weeks of gait training under the suspension protection system for 30 minutes daily,5 days a week.The experimental group used the personal assistant machine during training.Three-dimensional gait analysis(using the Cortex motion capture system),Brunnstrom staging,Fugl-Meyer Assessment for lower limb motor function,Fugl-Meyer balance function,and the modified Ashworth Scale were evaluated within 1 week before the intervention and after 4 weeks of intervention.RESULTS AND CONCLUSION:After the 4-week intervention,all outcome measures showed significant changes in each group.The experimental group had a small but significant increase in the modified Ashworth Scale score(P<0.05,d=|0.15|),while the control group had a large significant increase(P<0.05,d=|1.48|).The experimental group demonstrated greater improvements in walking speed(16.5 to 38.44 cm/s,P<0.05,d=|4.01|),step frequency(46.44 to 64.94 steps/min,P<0.05,d=|2.32|),stride length(15.50 to 29.81 cm,P<0.05,d=|3.44|),and peak hip and knee flexion(d=|1.82|to|2.17|).After treatment,the experimental group showed significantly greater improvements than the control group in walking speed(38.44 vs.26.63 cm/s,P<0.05,d=|2.75|),stride length,peak hip and knee flexion(d=|1.31|to|1.45|),step frequency(64.94 vs.59.38 steps/min,P<0.05,d=|0.85|),and a reduced support phase(bilateral:24.31%vs.28.38%,P<0.05,d=|0.88|;non-paretic:66.19%vs.70.13%,P<0.05,d=|0.94|).For early hemiplegia,personal assistant machine-assisted gait training under the suspension protection system helps establish a correct gait pattern,prevents muscle spasms,and improves motor function.
文摘The ongoing coronavirus disease 2019(COVID-19)pandemic has necessitated rapid advancements in therapeutic strategies,with dexamethasone emerging as a key treatment for severe cases.This editorial discusses the systematic review conducted by Sethi et al,published in the World Journal of Virology.The review critically examines the efficacy and safety of varying dosages of dexamethasone in severe COVID-19 patients,providing a comprehensive meta-analysis that underscores the current clinical recommendations favoring a low-dose regimen.Despite these findings,the review highlights the potential benefits of tailored dosages for specific patient subgroups,suggesting a need for personalized treatment approaches.This editorial expands on the implications of these findings,advocating for the integration of evolving clinical data into treatment protocols and calling for further research into patient-specific responses to therapy.It emphasizes the importance of adaptability and precision in pandemic response,urging the medical community to consider both the robustness of existing evidence and the potential for innovative approaches to enhance patient outcomes in the face of global health challenges.
