Objective To study the causal relationship between R&D investment and enterprise performance of domestic pharmaceutical enterprises.Methods Panel data model was adopted for empirical analysis.Results and Conclusio...Objective To study the causal relationship between R&D investment and enterprise performance of domestic pharmaceutical enterprises.Methods Panel data model was adopted for empirical analysis.Results and Conclusion Increasing the R&D investment intensity of pharmaceutical enterprises in the Yangtze River Delta and Zhejiang by 1%will increase their profit margins by 0.79%and 0.46%.On the contrary,if the profit margin increases by 1%,the R&D investment intensity will increase by 0.25%and 0.19%.If the profit margin of pharmaceutical enterprises in Beijing,Tianjin,Hebei,Chengdu,Chongqing and other regions increases by 1%,the R&D investment intensity will increase by 0.14%,0.07%and 0.1%,respectively,which are lower than those in the Yangtze River Delta and Zhejiang.The relationship between R&D investment and enterprise performance of pharmaceutical enterprises in the Yangtze River Delta and Zhejiang Province is Granger causality,showing a two-way positive effect.Profits and R&D investment of pharmaceutical enterprises in Beijing,Tianjin,Hebei,Chengdu,Chongqing and other regions are also Granger causality.But in the Pearl River Delta,profits and R&D investment have not passed the stability test,it is impossible to determine the causality between them.展开更多
This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022.The study's concentrated samples ...This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022.The study's concentrated samples are Russia,the United States,the Netherlands,Brazil,Germany,China,France,Spain,Italy,Canada,Belgium,Indonesia,and India.The research uncovers notable variations in ecological balance by utilizing a comprehensive set of advanced panel unit root tests(Panel CIPS,CADF,Panel-LM,Panel-KPSS,and Bahmani-Oskooee et al.’s Panel KPSS Unit Root Test).The findings highlight significant improvements in Canada,contrasting with declines in the Netherlands,France,Germany,and the United States.The results indicate convergence in ecological balance among these countries,suggesting that agricultural practices are progressively aligning with sustainability objectives.The considered countries can determine and enact joint and collective policy actions addressing cropland sustainability.However,the univariate outcome also shows that the cropland ecological balance of Germany,China,France,Indonesia,and India does contain a unit root and stationary which means the presence of the constant-mean.The univariate actions from the mentioned governments will not promote persistent impact.Therefore,joint actions determined by the countries considered are recommended for the mentioned countries.However,the rest of the countries also enact local policies.The insights gained are critical for informing global sustainability strategies and aiding policymakers in developing effective measures to enhance agricultural practices and mitigate environmental impacts.This research provides a data-driven foundation for optimizing agricultural sustainability and supports international efforts to achieve long-term ecological stability.展开更多
Objective:This study aimed to examine the influence of behavioral lifestyle factors on recent episodic memory retention capacity among young-old adults(aged 60-69 years)in China.The findings provide scientific evidenc...Objective:This study aimed to examine the influence of behavioral lifestyle factors on recent episodic memory retention capacity among young-old adults(aged 60-69 years)in China.The findings provide scientific evidence to inform proactive strategies to mitigate cognitive decline risk within China’s rapidly aging population.Methods:Utilizing data from the 2022 wave of the China Family Panel Studies(CFPS),a total of 2,772 adults aged 60-69 were included in the analytical sample.Recent episodic memory retention capacity(scored 0-5 points,based on self-reported assessment)served as the dependent variable.Six categories of behavioral lifestyle indicators(including exercise frequency,sleep quality,dietary patterns,etc.)were analyzed as independent variables.Associations were assessed using multivariate ordinal logistic regression models,controlling for relevant covariates.Results:Self-reported potential impairment in recent episodic memory was identified by 47.19%of respondents.Multivariate analysis revealed significant associations between behavioral lifestyle factors and memory retention capacity.Regular exercise(OR=1.297,95%CI:1.118-1.504),meat consumption(OR=1.765,95%CI:1.393-2.237),regular reading habits(OR=1.599,95%CI:1.283-1.992),and internet use(OR=1.413,95%CI:1.217-1.641)emerged as significant protective factors.Abnormal sleep duration was detrimentally associated with retention capacity(too short:OR=0.728,95%CI:0.591-0.897;too long:OR=0.810,95%CI:0.670-0.980).Significant associations were also observed for control variables:urban residence(OR=1.270,95%CI:1.100-1.467),high school education or above(OR=1.543,95%CI:1.293-1.841),and better self-rated health status(OR=1.156,95%CI:1.089-1.227)were positively correlated with better memory retention.Conclusions:Optimal sleep duration,regular physical exercise,meat intake,habitual reading,and internet engagement positively predict self-assessed recent episodic memory retention capacity in Chinese young-old adults.These findings underscore the potential for multi-faceted lifestyle interventions to enhance cog-nitive health in aging populations.Specifically,strategies should encompass community-based sleep hygiene management,tailored nutritional interventions(especially promoting adequate protein sources like meat),enhanced digital literacy and internet accessibility programs,and the promotion of age-appropriate physical activity initiatives.Furthermore,implementing culturally responsive strategies adapted to urban-rural contexts-such as deploying“mobile cognitive health units”in rural areas and fostering digital reading platforms in urban settings-is recommended to optimize intervention effectiveness.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
基金Shenyang Pharmaceutical University Young and Middle aged Teacher Career Development Support PlanPublic Welfare Research Fund for Scientific Undertakings of Liaoning Province in 2022(Soft Science Research Plan)(No.2022JH4/10100040).
文摘Objective To study the causal relationship between R&D investment and enterprise performance of domestic pharmaceutical enterprises.Methods Panel data model was adopted for empirical analysis.Results and Conclusion Increasing the R&D investment intensity of pharmaceutical enterprises in the Yangtze River Delta and Zhejiang by 1%will increase their profit margins by 0.79%and 0.46%.On the contrary,if the profit margin increases by 1%,the R&D investment intensity will increase by 0.25%and 0.19%.If the profit margin of pharmaceutical enterprises in Beijing,Tianjin,Hebei,Chengdu,Chongqing and other regions increases by 1%,the R&D investment intensity will increase by 0.14%,0.07%and 0.1%,respectively,which are lower than those in the Yangtze River Delta and Zhejiang.The relationship between R&D investment and enterprise performance of pharmaceutical enterprises in the Yangtze River Delta and Zhejiang Province is Granger causality,showing a two-way positive effect.Profits and R&D investment of pharmaceutical enterprises in Beijing,Tianjin,Hebei,Chengdu,Chongqing and other regions are also Granger causality.But in the Pearl River Delta,profits and R&D investment have not passed the stability test,it is impossible to determine the causality between them.
文摘This study investigates the convergence hypothesis and stochastic dynamics of agricultural land use and ecological balance across 13 major agricultural countries from 1992 to 2022.The study's concentrated samples are Russia,the United States,the Netherlands,Brazil,Germany,China,France,Spain,Italy,Canada,Belgium,Indonesia,and India.The research uncovers notable variations in ecological balance by utilizing a comprehensive set of advanced panel unit root tests(Panel CIPS,CADF,Panel-LM,Panel-KPSS,and Bahmani-Oskooee et al.’s Panel KPSS Unit Root Test).The findings highlight significant improvements in Canada,contrasting with declines in the Netherlands,France,Germany,and the United States.The results indicate convergence in ecological balance among these countries,suggesting that agricultural practices are progressively aligning with sustainability objectives.The considered countries can determine and enact joint and collective policy actions addressing cropland sustainability.However,the univariate outcome also shows that the cropland ecological balance of Germany,China,France,Indonesia,and India does contain a unit root and stationary which means the presence of the constant-mean.The univariate actions from the mentioned governments will not promote persistent impact.Therefore,joint actions determined by the countries considered are recommended for the mentioned countries.However,the rest of the countries also enact local policies.The insights gained are critical for informing global sustainability strategies and aiding policymakers in developing effective measures to enhance agricultural practices and mitigate environmental impacts.This research provides a data-driven foundation for optimizing agricultural sustainability and supports international efforts to achieve long-term ecological stability.
文摘Objective:This study aimed to examine the influence of behavioral lifestyle factors on recent episodic memory retention capacity among young-old adults(aged 60-69 years)in China.The findings provide scientific evidence to inform proactive strategies to mitigate cognitive decline risk within China’s rapidly aging population.Methods:Utilizing data from the 2022 wave of the China Family Panel Studies(CFPS),a total of 2,772 adults aged 60-69 were included in the analytical sample.Recent episodic memory retention capacity(scored 0-5 points,based on self-reported assessment)served as the dependent variable.Six categories of behavioral lifestyle indicators(including exercise frequency,sleep quality,dietary patterns,etc.)were analyzed as independent variables.Associations were assessed using multivariate ordinal logistic regression models,controlling for relevant covariates.Results:Self-reported potential impairment in recent episodic memory was identified by 47.19%of respondents.Multivariate analysis revealed significant associations between behavioral lifestyle factors and memory retention capacity.Regular exercise(OR=1.297,95%CI:1.118-1.504),meat consumption(OR=1.765,95%CI:1.393-2.237),regular reading habits(OR=1.599,95%CI:1.283-1.992),and internet use(OR=1.413,95%CI:1.217-1.641)emerged as significant protective factors.Abnormal sleep duration was detrimentally associated with retention capacity(too short:OR=0.728,95%CI:0.591-0.897;too long:OR=0.810,95%CI:0.670-0.980).Significant associations were also observed for control variables:urban residence(OR=1.270,95%CI:1.100-1.467),high school education or above(OR=1.543,95%CI:1.293-1.841),and better self-rated health status(OR=1.156,95%CI:1.089-1.227)were positively correlated with better memory retention.Conclusions:Optimal sleep duration,regular physical exercise,meat intake,habitual reading,and internet engagement positively predict self-assessed recent episodic memory retention capacity in Chinese young-old adults.These findings underscore the potential for multi-faceted lifestyle interventions to enhance cog-nitive health in aging populations.Specifically,strategies should encompass community-based sleep hygiene management,tailored nutritional interventions(especially promoting adequate protein sources like meat),enhanced digital literacy and internet accessibility programs,and the promotion of age-appropriate physical activity initiatives.Furthermore,implementing culturally responsive strategies adapted to urban-rural contexts-such as deploying“mobile cognitive health units”in rural areas and fostering digital reading platforms in urban settings-is recommended to optimize intervention effectiveness.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
