Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function...Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.展开更多
Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications...Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.展开更多
Traffic flow prediction is an important component of intelligent transportation systems.Recently,unprecedented data availability and rapid development of machine learning techniques have led to tremendous progress in ...Traffic flow prediction is an important component of intelligent transportation systems.Recently,unprecedented data availability and rapid development of machine learning techniques have led to tremendous progress in this field.This article first introduces the research on traffic flow prediction and the challenges it currently faces.It then proposes a classification method for literature,discussing and analyzing existing research on using machine learning methods to address traffic flow prediction from the perspectives of the prediction preparation process and the construction of prediction models.The article also summarizes innovative modules in these models.Finally,we provide improvement strategies for current baseline models and discuss the challenges and research directions in the field of traffic flow prediction in the future.展开更多
Objective:To explore the significance of osteopontin and nuclear factorκB(NF-κB) expression in patients with knee osteoarthritis.Methods:RT-PCR and enzyme-linked immunosorbent assay were used to measure the Osteopon...Objective:To explore the significance of osteopontin and nuclear factorκB(NF-κB) expression in patients with knee osteoarthritis.Methods:RT-PCR and enzyme-linked immunosorbent assay were used to measure the Osteopontin(OPN) and NF-κB concentration of knee joint synovial fluid of patients with knee osteoarthritis and trauma fractures,and analyze the relationship between the expressiones of them.Results:OPN and NF-κB expression at the mRNA and protein levels of patients with knee osteoarthritis were significantly higher than the control group, the result showed statistical significance(P【0.05).There was a positive correlation between the OPN levels in synovial fluid of patients with knee osteoarthritis and NF-κB expression levels (P【0.05).Conclusions:The high expression of OPN and NF-κB are closely related to occurrence and development of knee osteoarthritis.展开更多
Tetraspanin CD151 was found to be upregulated in malignant cell types and has been identified as a tumor metastasis promoter.In this study,we aimed to examine the role of the CD151-integrin complex in lung cancer meta...Tetraspanin CD151 was found to be upregulated in malignant cell types and has been identified as a tumor metastasis promoter.In this study,we aimed to examine the role of the CD151-integrin complex in lung cancer metastasis and the underlying mechanisms.CD151 QRD194–196→AAA194–196 mutant was generated and used to transfect A549 human lung adenocarcinoma cells.We found that there was no significant difference in CD151 protein expression between CD151 and CD151-AAA mutant groups.In vitro,CD151-AAA mutant delivery abrogated the migration and invasion of A549 cells,which was promoted by CD151 gene transfer.Furthermore,CD151-AAA delivery failed to activate FAK and p130Cas signaling pathways.Western blot and immunohistochemical staining showed strong CD151 expression in lung cancerous tissues but not in adjacent normal tissues.Increased level of CD151 protein was observed in 20 of the patients and the positive rate of CD151 protein in specimens was 62.5%(20/32).In addition,CD151 was co-localized withα3 integrin at the cell-cell contact site in carcinoma tissues.These results suggested that the disruption of the CD151-α3 integrin complex may impair the metastasis-promoting effects and signaling events induced by CD151 in lung cancer.Our findings identified a key role for CD151-α3 integrin complex as a promoter in the lung cancer.展开更多
The features and treatment of 98 Chinese patients with immunoglobulin G4 (IgG4)-related disease (IgG4-RD) referred to a single tertiary referring centre were reviewed. Patientsdiagnosed with IgG4-RD according to the c...The features and treatment of 98 Chinese patients with immunoglobulin G4 (IgG4)-related disease (IgG4-RD) referred to a single tertiary referring centre were reviewed. Patientsdiagnosed with IgG4-RD according to the comprehensive diagnostic criteria (CDC) were includedin the retrospective study from May 2012 to March 2019. We collcted data on clinical, laboratory,imaging, histological features and treatment. Totally, 98 patients with IgG4-RD were enrolled.The common clinical manifestations included abdominal pain, salivary gland swelling andlymphadenopathy. 51% of the patients had multiple organs involvement. Lymph nodes, pancreasand salivary glands were most commonly involved. Four rare sites including ulna, cerebellum,scalp, and mammary gland were found. The serum IgG4 level was increased by 85.7%. The serumIgG4 level was positively correlated with the number of involved organs, IgG and IgG4/IgG. LowC3 and C4 levels were observed in 37.5% and 12.2% patients respectively, and all patients withkidney involvement had hypocomplementemia. A total of 54 patients underwent tissue biopsies,and 55.6%, 31.5% and 11.1% cases were diagnosed as definite, probable and possible IgG4-RD,respectively. Eighty-eight patients received glucocorticoids (GCs) therapy. Five patients underwentradical surgery to remove the lesion. 73% of them presented a complete or partial remission. IgG4-RD is a systemic fibroinflammatory disease with involvement of multiple organs throughout thebody including some rare sites. Most IgG4-RD patients had increased serum IgG4 levels andpatients with kidney involvement showed bypocomplementermia. GCs therapy is effective. Moreresearch is needed to provide a more reliable basis for the diagnosis and treatment of patients.展开更多
This paper considers the problem of delay-dependent exponential stability in mean square for stochastic systems with polytopic-type uncertainties and time-varying delay. Applying the descriptor model transformation an...This paper considers the problem of delay-dependent exponential stability in mean square for stochastic systems with polytopic-type uncertainties and time-varying delay. Applying the descriptor model transformation and introducing free weighting matrices, a new type of Lyapunov-Krasovskii functional is constructed based on linear matrix inequalities （LMIs）, and some new delay-dependent criteria are obtained. These criteria include the delay-independent/rate- dependent and delay-dependent/rate-independent exponential stability criteria. These new criteria are less conservative than existing ones. Numerical examples demonstrate that these new criteria are effective and are an improvement over existing ones.展开更多
文摘Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.
基金supported by 2022 Shenyang Philosophy and Social Science Planning under grant SY202201Z,Liaoning Provincial Department of Education Project under grant LJKZ0588.
文摘Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.
基金supported by 2022 Shenyang Philosophy and Social Science Planning under grant SY202201Z,Liaoning Provincial Department of Education Project under grant LJKZ0588.
文摘Traffic flow prediction is an important component of intelligent transportation systems.Recently,unprecedented data availability and rapid development of machine learning techniques have led to tremendous progress in this field.This article first introduces the research on traffic flow prediction and the challenges it currently faces.It then proposes a classification method for literature,discussing and analyzing existing research on using machine learning methods to address traffic flow prediction from the perspectives of the prediction preparation process and the construction of prediction models.The article also summarizes innovative modules in these models.Finally,we provide improvement strategies for current baseline models and discuss the challenges and research directions in the field of traffic flow prediction in the future.
文摘Objective:To explore the significance of osteopontin and nuclear factorκB(NF-κB) expression in patients with knee osteoarthritis.Methods:RT-PCR and enzyme-linked immunosorbent assay were used to measure the Osteopontin(OPN) and NF-κB concentration of knee joint synovial fluid of patients with knee osteoarthritis and trauma fractures,and analyze the relationship between the expressiones of them.Results:OPN and NF-κB expression at the mRNA and protein levels of patients with knee osteoarthritis were significantly higher than the control group, the result showed statistical significance(P【0.05).There was a positive correlation between the OPN levels in synovial fluid of patients with knee osteoarthritis and NF-κB expression levels (P【0.05).Conclusions:The high expression of OPN and NF-κB are closely related to occurrence and development of knee osteoarthritis.
基金The project was supported by a grant from the National Natural Science Foundation of China(No.81873535)the Natural Science Foundation of Hubei Province(No.2020CFB573).
文摘Tetraspanin CD151 was found to be upregulated in malignant cell types and has been identified as a tumor metastasis promoter.In this study,we aimed to examine the role of the CD151-integrin complex in lung cancer metastasis and the underlying mechanisms.CD151 QRD194–196→AAA194–196 mutant was generated and used to transfect A549 human lung adenocarcinoma cells.We found that there was no significant difference in CD151 protein expression between CD151 and CD151-AAA mutant groups.In vitro,CD151-AAA mutant delivery abrogated the migration and invasion of A549 cells,which was promoted by CD151 gene transfer.Furthermore,CD151-AAA delivery failed to activate FAK and p130Cas signaling pathways.Western blot and immunohistochemical staining showed strong CD151 expression in lung cancerous tissues but not in adjacent normal tissues.Increased level of CD151 protein was observed in 20 of the patients and the positive rate of CD151 protein in specimens was 62.5%(20/32).In addition,CD151 was co-localized withα3 integrin at the cell-cell contact site in carcinoma tissues.These results suggested that the disruption of the CD151-α3 integrin complex may impair the metastasis-promoting effects and signaling events induced by CD151 in lung cancer.Our findings identified a key role for CD151-α3 integrin complex as a promoter in the lung cancer.
文摘The features and treatment of 98 Chinese patients with immunoglobulin G4 (IgG4)-related disease (IgG4-RD) referred to a single tertiary referring centre were reviewed. Patientsdiagnosed with IgG4-RD according to the comprehensive diagnostic criteria (CDC) were includedin the retrospective study from May 2012 to March 2019. We collcted data on clinical, laboratory,imaging, histological features and treatment. Totally, 98 patients with IgG4-RD were enrolled.The common clinical manifestations included abdominal pain, salivary gland swelling andlymphadenopathy. 51% of the patients had multiple organs involvement. Lymph nodes, pancreasand salivary glands were most commonly involved. Four rare sites including ulna, cerebellum,scalp, and mammary gland were found. The serum IgG4 level was increased by 85.7%. The serumIgG4 level was positively correlated with the number of involved organs, IgG and IgG4/IgG. LowC3 and C4 levels were observed in 37.5% and 12.2% patients respectively, and all patients withkidney involvement had hypocomplementemia. A total of 54 patients underwent tissue biopsies,and 55.6%, 31.5% and 11.1% cases were diagnosed as definite, probable and possible IgG4-RD,respectively. Eighty-eight patients received glucocorticoids (GCs) therapy. Five patients underwentradical surgery to remove the lesion. 73% of them presented a complete or partial remission. IgG4-RD is a systemic fibroinflammatory disease with involvement of multiple organs throughout thebody including some rare sites. Most IgG4-RD patients had increased serum IgG4 levels andpatients with kidney involvement showed bypocomplementermia. GCs therapy is effective. Moreresearch is needed to provide a more reliable basis for the diagnosis and treatment of patients.
基金supported by the National Natural Science Foundation of China (No.60525303, 60604004, 60704009) Natural Science Foundationof Hebei Province, China (No.F2005000390, F2006000270)
文摘This paper considers the problem of delay-dependent exponential stability in mean square for stochastic systems with polytopic-type uncertainties and time-varying delay. Applying the descriptor model transformation and introducing free weighting matrices, a new type of Lyapunov-Krasovskii functional is constructed based on linear matrix inequalities （LMIs）, and some new delay-dependent criteria are obtained. These criteria include the delay-independent/rate- dependent and delay-dependent/rate-independent exponential stability criteria. These new criteria are less conservative than existing ones. Numerical examples demonstrate that these new criteria are effective and are an improvement over existing ones.
