In June 2012,the UN conference on sustainable development "Rio+20" summarized the work of the world community in this direction for last 20 years and outlined the tasks for the future.The UN website contains...In June 2012,the UN conference on sustainable development "Rio+20" summarized the work of the world community in this direction for last 20 years and outlined the tasks for the future.The UN website contains enough information to estimate the importance of global problems and "green economy," taking into account a very complicated state of the global and Russian markets which are balancing on the verge of crisis.Unfortunately,the website does not contain the materials of the 6th civilization forum "Long-Term Strategy for Sustainable Development on the Basis of Partnership of Civilizations:Concepts,Strategy,Programs and Projects" within the bounds of "Rio+20" which has considered the problems of a dialogue and partnership in conditions of extensive globalization.These problems are covered in the Partnership of Civilizations Journal which is issued in the Russian,English and Arabian languages,including its Internet version.The International Informatization Academy (it has the general consultative status at the Economic and Social Council of the UN) and its 20-year activity in the sphere of informatization of the world and Russia on the way to the partnership of civilizations,are presented there[1].展开更多
Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other...Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.展开更多
Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people worldwide.This figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzhe...Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people worldwide.This figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzheimer’s Disease International).The apolipoproteinε4(APOE4)allele is the strongest genetic risk factor for late-onset AD(after age 65 years).Apolipoprotein E,a lipid transporter,exists in three variants:ε2,ε3,andε4.APOEε2(APOE2)is protective against AD,APOEε3(APOE3)is neutral,while APOE4 significantly increases the risk.Individuals with one copy of APOE4 have a 4-fold greater risk of developing AD,and those with two copies face an 8-fold risk compared to non-carriers.Even in cognitively normal individuals,APOE4 carriers exhibit brain metabolic and vascular deficits decades before amyloid-beta(Aβ)plaques and neurofibrillary tau tangles emerge-the hallmark pathologies of AD(Reiman et al.,2001,2005;Thambisetty et al.,2010).Notably,studies have demonstrated reduced glucose uptake,or hypometabolism,in brain regions vulnerable to AD in asymptomatic middle-aged APOE4 carriers,long before clinical symptoms arise(Reiman et al.,2001,2005).展开更多
In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of th...In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.展开更多
Patients with leukemia often suffer from the combined effects of cancer-related fatigue(CRF)and subthreshold depression,which mutually exacerbate each other in a vicious cycle.In this editorial,we comment on the artic...Patients with leukemia often suffer from the combined effects of cancer-related fatigue(CRF)and subthreshold depression,which mutually exacerbate each other in a vicious cycle.In this editorial,we comment on the article by Liu et al,published in the World Journal of Psychiatry.We further elucidate the profound impact of subthreshold depressive symptoms on the experience of CRF and complications in patients with leukemia.This editorial highlights the importance of early identification and treatment of subclinical depression,and advocates for a multidisciplinary and integrated treatment approach that includes social support,psychological interventions,and individualized treatment plans.Future research needs to explore the biological mechanisms underlying the interaction between the two to develop more effective prevention and treatment strategies.展开更多
In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by pred...In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.展开更多
In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential in...In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.展开更多
BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp ex...BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms;hence,negative AAp and complicated AAp are the primary concerns in research on AAp.In other terms,further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.AIM To use a Stochastic Gradient Boosting(SGB)-based machine learning(ML)algorithm to tell the difference between AAp patients who are complicated and those who are not,and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.METHODS This study analyzed an open access data set containing 140 people,including 41 healthy controls,65 individuals with uncomplicated AAp,and 34 individuals with complicated AAp.We analyzed some demographic data(age,sex)of the patients and the following biochemical blood parameters:White blood cell(WBC)count,neutrophils,lymphocytes,monocytes,platelet count,neutrophil-tolymphocyte ratio,lymphocyte-to-monocyte ratio,mean platelet volume,neutrophil-to-immature granulocyte ratio,ferritin,total bilirubin,immature granulocyte count,immature granulocyte percent,and neutrophil-to-immature granulocyte ratio.We tested the SGB model using n-fold cross-validation.It was implemented with an 80-20 training-test split.We used variable importance values to identify the variables that were most effective on the target.RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%,a micro aera under the curve(AUC)of 94.7%,a sensitivity of 94.7%,and a specificity of 100%.In distinguishing complicated AAp patients from uncomplicated ones,the model achieved an accuracy of 78.9%,a micro AUC of 79%,a sensitivity of 83.3%,and a specificity of 76.9%.The most useful biomarkers for confirming the AA diagnosis were WBC(100%),neutrophils(95.14%),and the lymphocyte-monocyte ratio(76.05%).On the other hand,the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin(100%),WBC(96.90%),and the neutrophil-immature granulocytes ratio(64.05%).CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients.Although the model's accuracy in the classification of complicated AAp is moderate,the high variable importance obtained is clinically significant.We need further prospective validation studies,but the integration of such ML algorithms into clinical practice may improve diagnostic processes.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various as...Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.展开更多
The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar ener...The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar energy,among the various renewable sources,is particularly appealing due to its abundant availability.However,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight hours.This study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat transfer.The CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of 10.These specific parameters were found to maximize the heat transfer and temperature reduction efficiency.This finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar panels.Alternative cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.展开更多
This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA f...This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
Lead chalcohalides(PbYX,X=Cl,Br,I;Y=S,Se)is an extension of the classic Pb chalcogenides(PbY).Constructing the heterogeneous integration with PbYX and PbY material systems makes it possible to achieve significantly im...Lead chalcohalides(PbYX,X=Cl,Br,I;Y=S,Se)is an extension of the classic Pb chalcogenides(PbY).Constructing the heterogeneous integration with PbYX and PbY material systems makes it possible to achieve significantly improved optoelectronic performance.In this work,we studied the effect of introducing halogen precursors on the structure of classical PbS nanocrystals(NCs)during the synthesis process and realized the preparation of PbS/Pb_(3)S_(2)X_(2) core/shell structure for the first time.The core/shell structure can effectively improve their optical properties.Furthermore,our approach enables the synthesis of Pb_(3)S_(2)Br_(2) that had not yet been reported.Our results not only provide valuable insights into the heterogeneous integration of PbYX and PbY materials to elevate material properties but also provide an effective method for further expanding the preparation of PbYX material systems.展开更多
The accuracy of thermal analysis measurements is critical to analyze material properties correctly,making the improvement of measurement precision and proper uncertainty analysis of test results absolutely essential.A...The accuracy of thermal analysis measurements is critical to analyze material properties correctly,making the improvement of measurement precision and proper uncertainty analysis of test results absolutely essential.As a primary thermal analysis instrument,the simultaneous thermal analyzer(STA)has unique advantages,which combines the functionalities of thermogravimetric(TG)analyzersand differential scanning calorimeters(DSC).However,the absence of standard quality control procedures has resulted in poor measurement reproducibility,low accuracy,and inadequate traceability of analytical results.This study utilized a multi-point temperature calibration method based on national certified reference materials to reduce instrument temperature indication errors.On this basis,we innovatively established a comprehensive quality control system encompassing laboratory environmental control,standard method selection,instrument performance verification,reference material traceability,and uncertainty analysis,thereby achieving standardized operational procedures for thermal analysis measurement.Taking the"determination of initial melting temperature of unknown substances"as a representative case study,a component resolution model for thermal analysis test uncertainty was developed.Through systematic analysis of both the reference material-introduced component and measurement repeatability component,complete traceability of test results was achieved.This approach ensures data validity and enhances the accuracy of test results.This provides crucial technical support and practical reference for the standardization of thermal analysis measurement procedure and assessment of result reliability.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature....Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.展开更多
Corticotomy is a clinical procedure to accelerate orthodontic tooth movement characterized by the regional acceleratory phenomenon(RAP).Despite its therapeutic effects,the surgical risk and unclear mechanism hamper th...Corticotomy is a clinical procedure to accelerate orthodontic tooth movement characterized by the regional acceleratory phenomenon(RAP).Despite its therapeutic effects,the surgical risk and unclear mechanism hamper the clinical application.Numerous evidences support macrophages as the key immune cells during bone remodeling.Our study discovered that the monocyte-derived macrophages primarily exhibited a pro-inflammatory phenotype that dominated bone remodeling in corticotomy by CX3CR1CreERT2;R26GFP lineage tracing system.Fluorescence staining,flow cytometry analysis,and western blot determined the significantly enhanced expression of binding immunoglobulin protein(BiP)and emphasized the activation of sensor activating transcription factor 6(ATF6)in macrophages.Then,we verified that macrophage specific ATF6 deletion(ATF6f/f;CX3CR1CreERT2 mice)decreased the proportion of pro-inflammatory macrophages and therefore blocked the acceleration effect of corticotomy.In contrast,macrophage ATF6 overexpression exaggerated the acceleration of orthodontic tooth movement.In vitro experiments also proved that higher proportion of pro-inflammatory macrophages was positively correlated with higher expression of ATF6.At the mechanism level,RNA-seq and CUT&Tag analysis demonstrated that ATF6 modulated the macrophage-orchestrated inflammation through interacting with Tnfαpromotor and augmenting its transcription.Additionally,molecular docking simulation and dual-luciferase reporter system indicated the possible binding sites outside of the traditional endoplasmic reticulum-stress response element(ERSE).Taken together,ATF6 may aggravate orthodontic bone remodeling by promoting Tnfαtranscription in macrophages,suggesting that ATF6 may represent a promising therapeutic target for non-invasive accelerated orthodontics.展开更多
Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and c...Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and content distortion caused by inadequate stylization.To address these problems,PhotoGAN,a new Generative AdversarialNetwork(GAN)model is proposed in this paper.A deeper feature extraction network has been designed to capture global information and local details better.Introducingmulti-scale attention modules helps the generator focus on important feature areas at different scales,further enhancing the effectiveness of feature extraction.Using a semantic discriminator helps the generator learn quickly and better understand image content,improving the consistency and visual quality of the generated images.Finally,qualitative and quantitative experiments were conducted on a self-built dataset.The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques.It not only performed excellently on objective metrics but also appeared more visually appealing,particularly excelling in handling complex scenes and details.展开更多
文摘In June 2012,the UN conference on sustainable development "Rio+20" summarized the work of the world community in this direction for last 20 years and outlined the tasks for the future.The UN website contains enough information to estimate the importance of global problems and "green economy," taking into account a very complicated state of the global and Russian markets which are balancing on the verge of crisis.Unfortunately,the website does not contain the materials of the 6th civilization forum "Long-Term Strategy for Sustainable Development on the Basis of Partnership of Civilizations:Concepts,Strategy,Programs and Projects" within the bounds of "Rio+20" which has considered the problems of a dialogue and partnership in conditions of extensive globalization.These problems are covered in the Partnership of Civilizations Journal which is issued in the Russian,English and Arabian languages,including its Internet version.The International Informatization Academy (it has the general consultative status at the Economic and Social Council of the UN) and its 20-year activity in the sphere of informatization of the world and Russia on the way to the partnership of civilizations,are presented there[1].
文摘Alzheimer’s disease(AD)is the most common form of dementia.In addition to the lack of effective treatments,there are limitations in diagnostic capabilities.The complexity of AD itself,together with a variety of other diseases often observed in a patient’s history in addition to their AD diagnosis,make deciphering the molecular mechanisms that underlie AD,even more important.Large datasets of single-cell RNA sequencing,single-nucleus RNA-sequencing(snRNA-seq),and spatial transcriptomics(ST)have become essential in guiding and supporting new investigations into the cellular and regional susceptibility of AD.However,with unique technology,software,and larger databases emerging;a lack of integration of these data can contribute to ineffective use of valuable knowledge.Importantly,there was no specialized database that concentrates on ST in AD that offers comprehensive differential analyses under various conditions,such as sex-specific,region-specific,and comparisons between AD and control groups until the new Single-cell and Spatial RNA-seq databasE for Alzheimer’s Disease(ssREAD)database(Wang et al.,2024)was introduced to meet the scientific community’s growing demand for comprehensive,integrated,and accessible data analysis.
基金supported by National Institute on Aging(NIH-NIA)R01AG054459(to ALL).
文摘Alzheimer’s disease(AD)is the most common form of dementia,affecting over 50 million people worldwide.This figure is projected to nearly double every 20 years,reaching 82 million by 2030 and 152 million by 2050(Alzheimer’s Disease International).The apolipoproteinε4(APOE4)allele is the strongest genetic risk factor for late-onset AD(after age 65 years).Apolipoprotein E,a lipid transporter,exists in three variants:ε2,ε3,andε4.APOEε2(APOE2)is protective against AD,APOEε3(APOE3)is neutral,while APOE4 significantly increases the risk.Individuals with one copy of APOE4 have a 4-fold greater risk of developing AD,and those with two copies face an 8-fold risk compared to non-carriers.Even in cognitively normal individuals,APOE4 carriers exhibit brain metabolic and vascular deficits decades before amyloid-beta(Aβ)plaques and neurofibrillary tau tangles emerge-the hallmark pathologies of AD(Reiman et al.,2001,2005;Thambisetty et al.,2010).Notably,studies have demonstrated reduced glucose uptake,or hypometabolism,in brain regions vulnerable to AD in asymptomatic middle-aged APOE4 carriers,long before clinical symptoms arise(Reiman et al.,2001,2005).
文摘In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.
文摘Patients with leukemia often suffer from the combined effects of cancer-related fatigue(CRF)and subthreshold depression,which mutually exacerbate each other in a vicious cycle.In this editorial,we comment on the article by Liu et al,published in the World Journal of Psychiatry.We further elucidate the profound impact of subthreshold depressive symptoms on the experience of CRF and complications in patients with leukemia.This editorial highlights the importance of early identification and treatment of subclinical depression,and advocates for a multidisciplinary and integrated treatment approach that includes social support,psychological interventions,and individualized treatment plans.Future research needs to explore the biological mechanisms underlying the interaction between the two to develop more effective prevention and treatment strategies.
基金supported by National Natural Science Foundation of China(Nos.62266054 and 62166050)Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021)+2 种基金Yunnan Fundamental Research Projects(No.202401AT070122)Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006)Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).
文摘In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number COFUND-DUT-OPEN4CEC-1,within PNCDI IV.
文摘In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.
文摘BACKGROUND Acute appendicitis(AAp)is a prevalent medical condition characterized by inflammation of the appendix that frequently necessitates urgent surgical procedures.Approximately two-thirds of patients with AAp exhibit characteristic signs and symptoms;hence,negative AAp and complicated AAp are the primary concerns in research on AAp.In other terms,further investigations and algorithms are required for at least one third of patients to predict the clinical condition and distinguish them from uncomplicated patients with AAp.AIM To use a Stochastic Gradient Boosting(SGB)-based machine learning(ML)algorithm to tell the difference between AAp patients who are complicated and those who are not,and to find some important biomarkers for both types of AAp by using modeling to get variable importance values.METHODS This study analyzed an open access data set containing 140 people,including 41 healthy controls,65 individuals with uncomplicated AAp,and 34 individuals with complicated AAp.We analyzed some demographic data(age,sex)of the patients and the following biochemical blood parameters:White blood cell(WBC)count,neutrophils,lymphocytes,monocytes,platelet count,neutrophil-tolymphocyte ratio,lymphocyte-to-monocyte ratio,mean platelet volume,neutrophil-to-immature granulocyte ratio,ferritin,total bilirubin,immature granulocyte count,immature granulocyte percent,and neutrophil-to-immature granulocyte ratio.We tested the SGB model using n-fold cross-validation.It was implemented with an 80-20 training-test split.We used variable importance values to identify the variables that were most effective on the target.RESULTS The SGB model demonstrated excellent performance in distinguishing AAp from control patients with an accuracy of 96.3%,a micro aera under the curve(AUC)of 94.7%,a sensitivity of 94.7%,and a specificity of 100%.In distinguishing complicated AAp patients from uncomplicated ones,the model achieved an accuracy of 78.9%,a micro AUC of 79%,a sensitivity of 83.3%,and a specificity of 76.9%.The most useful biomarkers for confirming the AA diagnosis were WBC(100%),neutrophils(95.14%),and the lymphocyte-monocyte ratio(76.05%).On the other hand,the most useful biomarkers for accurate diagnosis of complicated AAp were total bilirubin(100%),WBC(96.90%),and the neutrophil-immature granulocytes ratio(64.05%).CONCLUSION The SGB model achieved high accuracy rates in identifying AAp patients while it showed moderate performance in distinguishing complicated AAp patients from uncomplicated AAp patients.Although the model's accuracy in the classification of complicated AAp is moderate,the high variable importance obtained is clinically significant.We need further prospective validation studies,but the integration of such ML algorithms into clinical practice may improve diagnostic processes.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
基金supported by National Natural Science Foundation of China(32122066,32201855)STI2030—Major Projects(2023ZD04076).
文摘Phenotypic prediction is a promising strategy for accelerating plant breeding.Data from multiple sources(called multi-view data)can provide complementary information to characterize a biological object from various aspects.By integrating multi-view information into phenotypic prediction,a multi-view best linear unbiased prediction(MVBLUP)method is proposed in this paper.To measure the importance of multiple data views,the differential evolution algorithm with an early stopping mechanism is used,by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction.To further illustrate the characteristics of MVBLUP,we perform the empirical experiments on four multi-view datasets in different crops.Compared to the single-view method,the prediction accuracy of the MVBLUP method has improved by 0.038–0.201 on average.The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.
文摘The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar energy,among the various renewable sources,is particularly appealing due to its abundant availability.However,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight hours.This study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat transfer.The CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of 10.These specific parameters were found to maximize the heat transfer and temperature reduction efficiency.This finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar panels.Alternative cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.
基金funded by the Office of Gas and Electricity Markets(Ofgem)and supported by De Montfort University(DMU)and Nottingham Trent University(NTU),UK.
文摘This paper introduces the Integrated Security Embedded Resilience Architecture (ISERA) as an advanced resilience mechanism for Industrial Control Systems (ICS) and Operational Technology (OT) environments. The ISERA framework integrates security by design principles, micro-segmentation, and Island Mode Operation (IMO) to enhance cyber resilience and ensure continuous, secure operations. The methodology deploys a Forward-Thinking Architecture Strategy (FTAS) algorithm, which utilises an industrial Intrusion Detection System (IDS) implemented with Python’s Network Intrusion Detection System (NIDS) library. The FTAS algorithm successfully identified and responded to cyber-attacks, ensuring minimal system disruption. ISERA has been validated through comprehensive testing scenarios simulating Denial of Service (DoS) attacks and malware intrusions, at both the IT and OT layers where it successfully mitigates the impact of malicious activity. Results demonstrate ISERA’s efficacy in real-time threat detection, containment, and incident response, thus ensuring the integrity and reliability of critical infrastructure systems. ISERA’s decentralised approach contributes to global net zero goals by optimising resource use and minimising environmental impact. By adopting a decentralised control architecture and leveraging virtualisation, ISERA significantly enhances the cyber resilience and sustainability of critical infrastructure systems. This approach not only strengthens defences against evolving cyber threats but also optimises resource allocation, reducing the system’s carbon footprint. As a result, ISERA ensures the uninterrupted operation of essential services while contributing to broader net zero goals.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0110300)the National Natural Science Foundation of China(Grant Nos.52372215,92163114,and 52202274)+5 种基金the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20230504)the Special Fund for the"Dual Carbon"Science and Technology Innovation of Jiangsu province(Industrial Prospect and Key Technology Research program)(Grant Nos.BE2022023 and BE2022021)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.21KJA430004)Gusu Innovation and Entre preneurship Leading Talent Program(Grant No.ZXL2022451)the China Postdoctoral Science Foundation(Grant No.2023M732523)supported by Suzhou Key Laboratory of Functional Nano&Soft Materials,Collaborative Innovation Center of Suzhou Nano Science&Technology,the 111 Project.
文摘Lead chalcohalides(PbYX,X=Cl,Br,I;Y=S,Se)is an extension of the classic Pb chalcogenides(PbY).Constructing the heterogeneous integration with PbYX and PbY material systems makes it possible to achieve significantly improved optoelectronic performance.In this work,we studied the effect of introducing halogen precursors on the structure of classical PbS nanocrystals(NCs)during the synthesis process and realized the preparation of PbS/Pb_(3)S_(2)X_(2) core/shell structure for the first time.The core/shell structure can effectively improve their optical properties.Furthermore,our approach enables the synthesis of Pb_(3)S_(2)Br_(2) that had not yet been reported.Our results not only provide valuable insights into the heterogeneous integration of PbYX and PbY materials to elevate material properties but also provide an effective method for further expanding the preparation of PbYX material systems.
文摘The accuracy of thermal analysis measurements is critical to analyze material properties correctly,making the improvement of measurement precision and proper uncertainty analysis of test results absolutely essential.As a primary thermal analysis instrument,the simultaneous thermal analyzer(STA)has unique advantages,which combines the functionalities of thermogravimetric(TG)analyzersand differential scanning calorimeters(DSC).However,the absence of standard quality control procedures has resulted in poor measurement reproducibility,low accuracy,and inadequate traceability of analytical results.This study utilized a multi-point temperature calibration method based on national certified reference materials to reduce instrument temperature indication errors.On this basis,we innovatively established a comprehensive quality control system encompassing laboratory environmental control,standard method selection,instrument performance verification,reference material traceability,and uncertainty analysis,thereby achieving standardized operational procedures for thermal analysis measurement.Taking the"determination of initial melting temperature of unknown substances"as a representative case study,a component resolution model for thermal analysis test uncertainty was developed.Through systematic analysis of both the reference material-introduced component and measurement repeatability component,complete traceability of test results was achieved.This approach ensures data validity and enhances the accuracy of test results.This provides crucial technical support and practical reference for the standardization of thermal analysis measurement procedure and assessment of result reliability.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
基金supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2024C03106,X.F.)the National Natural Science Foundation of China(No.82474160,X.S.)+2 种基金the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(No.LBZ24H270001,X.P.)the Major Joint Projects Supported by the National Administration of TCM and Zhejiang Province(No.GZY-ZI-KJ-23037,X.P.)the Ningbo Top Medical and Health Research Program(No.2022030309,X.P.)。
文摘Network pharmacology has gained widespread application in drug discovery,particularly in traditional Chinese medicine(TCM)research,which is characterized by its“multi-component,multi-target,and multi-pathway”nature.Through the integration of network biology,TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms,establishing a novel research paradigm for TCM modernization.The rapid advancement of machine learning,particularly revolutionary deep learning methods,has substantially enhanced artificial intelligence(AI)technology,offering significant potential to advance TCM network pharmacology research.This paper describes the methodology of TCM network pharmacology,encompassing ingredient identification,network construction,network analysis,and experimental validation.Furthermore,it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods.Finally,it addresses challenges and future directions regarding cell-cell communication(CCC)-based network construction,analysis,and validation,providing valuable insights for TCM network pharmacology.
基金supported by the National Natural Science Foundation of China(82071143,82371000,82270361)Key Research and Development Program of Jiangsu Province(BE2022795)+2 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX22_1801)the Jiangsu Province Capability Improvement Project through the Science,Technology and Education-Jiangsu Provincial Research Hospital Cultivation Unit(YJXYYJSDW4)Jiangsu Provincial Medical Innovation Center(CXZX202227).
文摘Corticotomy is a clinical procedure to accelerate orthodontic tooth movement characterized by the regional acceleratory phenomenon(RAP).Despite its therapeutic effects,the surgical risk and unclear mechanism hamper the clinical application.Numerous evidences support macrophages as the key immune cells during bone remodeling.Our study discovered that the monocyte-derived macrophages primarily exhibited a pro-inflammatory phenotype that dominated bone remodeling in corticotomy by CX3CR1CreERT2;R26GFP lineage tracing system.Fluorescence staining,flow cytometry analysis,and western blot determined the significantly enhanced expression of binding immunoglobulin protein(BiP)and emphasized the activation of sensor activating transcription factor 6(ATF6)in macrophages.Then,we verified that macrophage specific ATF6 deletion(ATF6f/f;CX3CR1CreERT2 mice)decreased the proportion of pro-inflammatory macrophages and therefore blocked the acceleration effect of corticotomy.In contrast,macrophage ATF6 overexpression exaggerated the acceleration of orthodontic tooth movement.In vitro experiments also proved that higher proportion of pro-inflammatory macrophages was positively correlated with higher expression of ATF6.At the mechanism level,RNA-seq and CUT&Tag analysis demonstrated that ATF6 modulated the macrophage-orchestrated inflammation through interacting with Tnfαpromotor and augmenting its transcription.Additionally,molecular docking simulation and dual-luciferase reporter system indicated the possible binding sites outside of the traditional endoplasmic reticulum-stress response element(ERSE).Taken together,ATF6 may aggravate orthodontic bone remodeling by promoting Tnfαtranscription in macrophages,suggesting that ATF6 may represent a promising therapeutic target for non-invasive accelerated orthodontics.
基金funded by the Key R&D and Transformation Projects of Xizang(Tibet)Autonomous Region Science and Technology Program(funder:the Department of Science and Technology of the Xizang(Tibet)Autonomous Region),funding(grant)number:XZ202401ZY0004.
文摘Image style transfer is a research hotspot in the field of computer vision.For this job,many approaches have been put forth.These techniques do,however,still have some drawbacks,such as high computing complexity and content distortion caused by inadequate stylization.To address these problems,PhotoGAN,a new Generative AdversarialNetwork(GAN)model is proposed in this paper.A deeper feature extraction network has been designed to capture global information and local details better.Introducingmulti-scale attention modules helps the generator focus on important feature areas at different scales,further enhancing the effectiveness of feature extraction.Using a semantic discriminator helps the generator learn quickly and better understand image content,improving the consistency and visual quality of the generated images.Finally,qualitative and quantitative experiments were conducted on a self-built dataset.The experimental results indicate that PhotoGAN outperformed the current state-of-the-art techniques.It not only performed excellently on objective metrics but also appeared more visually appealing,particularly excelling in handling complex scenes and details.
