Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technol...Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technology(5G)based mobile devices.The EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition methods.However,there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time.Hence,it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time.In this paper,we propose a user recognition system that applies EMG signals to the short-time fourier transform(STFT),and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features.The proposed system is composed of a data pre-processing and normalization process,spectrogram image conversion process,and final classification process.The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4%accuracy performance,which is 13%higher than the EMGsignal-based system.Such a user recognition accuracy improvement was achieved by using multidimensional features,in the time-frequency domain.展开更多
Android OS provides such security mechanisms as application signature, privilege limit and sandbox to protect the security of operational system. However, these methods are unable to protect the applications of Androi...Android OS provides such security mechanisms as application signature, privilege limit and sandbox to protect the security of operational system. However, these methods are unable to protect the applications of Android against anti-reverse engineering and the codes of such applications face the risk of being obtained or modified, which are always the first step for further attacks. In this paper, a security enhancement system with online authentication （SeSoa） for Android APK is proposed, in which the code of Android application package （APK） can be automatically encrypted. The encrypted code is loaded and run in the Android system after being successfully decrypted. Compared with the exiting software protecting systems, SeSoa uses online authentication mechanism to ensure the improvementof the APK security and good balance between security and usability.展开更多
Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image t...Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.展开更多
As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts h...As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.展开更多
As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal reco...As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.展开更多
Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to foreca...Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.展开更多
Multi-factor authentication(MFA)was proposed by Pointcheval et al.[Pointcheval and Zimmer(2008)]to improve the security of single-factor(and two-factor)authentication.As the backbone of multi-factor authentication,bio...Multi-factor authentication(MFA)was proposed by Pointcheval et al.[Pointcheval and Zimmer(2008)]to improve the security of single-factor(and two-factor)authentication.As the backbone of multi-factor authentication,biometric data are widely observed.Especially,how to keep the privacy of biometric at the password database without impairing efficiency is still an open question.Using the vulnerability of encryption(or hash)algorithms,the attacker can still launch offline brute-force attacks on encrypted(or hashed)biometric data.To address the potential risk of biometric disclosure at the password database,in this paper,we propose a novel efficient and secure MFA key exchange(later denoted as MFAKE)protocol leveraging the Pythia PRF service and password-to-random(or PTR)protocol.Armed with the PTR protocol,a master password pwd can be translated by the user into independent pseudorandom passwords(or rwd)for each user account with the help of device(e.g.,smart phone).Meanwhile,using the Pythia PRF service,the password database can avoid leakage of the local user’s password and biometric data.This is the first paper to achieve the password and biometric harden service simultaneously using the PTR protocol and Pythia PRF.展开更多
Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved c...Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.展开更多
In recent years, Hyper Text Transfer Protocol （HTTP） spreads quickly and steadily in the usage of mobile applications as a common web protocol, so that the mobile applications can also benefit from HTTP/2, which is ...In recent years, Hyper Text Transfer Protocol （HTTP） spreads quickly and steadily in the usage of mobile applications as a common web protocol, so that the mobile applications can also benefit from HTTP/2, which is the new version of HTTP based on SPDY developed by Google to speed up the Internet transmission speed. HTTP/2 enables a more efficient use of network resources and a reduced perception of latency by in- troducing header field compression and allowing multiple concurrent exchanges on the same connection. However, what H3TP/2 focuses on is visiting websites through a browser, and mobile applications are not considered much. In this paper, firstly, mobile applications are classified based on the da- ta flow characteristics. Based on the classification, we propose an optimization of HTTP/2 for mobile applications, called HTTP/2-Advance, which uses multiple Transmission Control Protocol （TCP） connections to multiplex HTYP requests and responses. Then we build a tiny system which simulates actu- al requests and responses between mobile applications and servers. We figure out the best choice of the number of multiple TCP connections for mobile applications, and compare the performance of HTTP, HTTP/2 and HTrP/2-Advance in both simulated and in-situ experiments in our system.展开更多
Dengue infection affects more than half of the world’s population,with 1 billion symp-tomatic cases identified per year and several distinct genetic serotypes:DENV 1–4.Transmitted via the mosquito bite,the dengue vi...Dengue infection affects more than half of the world’s population,with 1 billion symp-tomatic cases identified per year and several distinct genetic serotypes:DENV 1–4.Transmitted via the mosquito bite,the dengue virus infects Langerhans cells.Monocytes,B lymphocytes,and mast cells infected with dengue virus produce various cytokines although it is not clear which ones are predominant during DHF disease.A mathemat-ical model of the Dengue virus infection is developed according to complex dynamics determined by many factors.Starting from a state of equilibrium that we could define as“virus-free”asymptotically stable with a viral reproduction number lower than one which means a very effective action of the innate immune system:it stops the infectious process,the mathematical analysis of stability in the presence of the virus demonstrates that the proposed model is dynamically influenced.Dengue fever affects more than half of the world’s population,with 1 billion symptomatic cases and multiple genetic serotypes confirmed each year,which simulates a network of interactions between the various populations involved without considering the speeds of the processes in question which are indicated in a separate computation.In this research,a hybrid approach of petri nets is utilized to connect the discrete models of dengue.展开更多
Background:CAR-T cells are chimeric antigen receptor(CAR)-T cells;they are targetspecific engineered cells on tumor cells and produce T cell-mediated antitumor responses.CAR-T cell therapy is the“first-line”therapy ...Background:CAR-T cells are chimeric antigen receptor(CAR)-T cells;they are targetspecific engineered cells on tumor cells and produce T cell-mediated antitumor responses.CAR-T cell therapy is the“first-line”therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and leukemia.This adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since,precisely for this type of tumor,the use of immune checkpoint inhibitors remained disappointing.Although CAR-T is a promising therapeutic technique,there are therapeutic limits linked to the persistence of these cells and to the tumor’s immune escape.CAR-T cell engineering techniques are allowed to express interleukin IL-36,and seem to be much more efficient in antitumoral action.IL-36 is involved in the long-term antitumor action,allowing CAR-T cells to be more efficient in their antitumor action due to a“cross-talk”action between the“IL-36/dendritic cells”axis and the adaptive immunity.Methods:This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of tumor.The model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy:IL-36,“armored”CAR-T cells(i.e.,engineered to produce IL-36)and the tumor cell population,focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T cells.Mathematical model was developed and numerical simulations were carried out during this research.The development of the model with stability analysis by conditions of Routh–Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment,making therapy more effective against the“solid tumor”.Findings:Primary malignant bone tumors are quite rare(about 3%of all tumors)and the vast majority consist of osteosarcomas and Ewing’s sarcoma and,approximately,the 20%of patients undergo metastasis situations that is the most likely cause of death.Interpretation:In bone tumor like osteosarcoma,there is a variation of the cellular mechanical characteristics that can influence the efficacy of chemotherapy and increase the metastatic capacity;an approach related to adoptive immunotherapy with CAR-T cells may be a possible solution because this type of therapy is not influenced by the biomechanics of cancer cells which show peculiar characteristics.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2017R1A6A1A03015496)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1014033).
文摘Recently,user recognitionmethods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things(IoT)services through fifth-generation technology(5G)based mobile devices.The EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition methods.However,there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time.Hence,it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time.In this paper,we propose a user recognition system that applies EMG signals to the short-time fourier transform(STFT),and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features.The proposed system is composed of a data pre-processing and normalization process,spectrogram image conversion process,and final classification process.The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4%accuracy performance,which is 13%higher than the EMGsignal-based system.Such a user recognition accuracy improvement was achieved by using multidimensional features,in the time-frequency domain.
基金supported by National Natural Science Foundation of China(61370195)ZTE Industry-Academia-Research Cooperation Funds
文摘Android OS provides such security mechanisms as application signature, privilege limit and sandbox to protect the security of operational system. However, these methods are unable to protect the applications of Android against anti-reverse engineering and the codes of such applications face the risk of being obtained or modified, which are always the first step for further attacks. In this paper, a security enhancement system with online authentication （SeSoa） for Android APK is proposed, in which the code of Android application package （APK） can be automatically encrypted. The encrypted code is loaded and run in the Android system after being successfully decrypted. Compared with the exiting software protecting systems, SeSoa uses online authentication mechanism to ensure the improvementof the APK security and good balance between security and usability.
基金supported by the Healthcare AI Convergence R&D Program through the National IT Industry Promotion Agency of Korea(NIPA)funded by the Ministry of Science and ICT(No.S0102-23-1007)the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2017R1A6A1A03015496).
文摘Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces.Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model.In contrast,this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions,especially around the eyes,eyebrows,nose,andmouth.Then,we apply a newclassifier using an ensemble network to increase emotion recognition accuracy.The emotion recognition performance was compared with the conventional algorithms using public databases.The results indicated that the proposed method achieved higher accuracy than the traditional based on facial expressions for emotion recognition.In particular,our experiments with the FER2013 database show that our proposed method is robust to lighting conditions and backgrounds,with an average of 25% higher performance than previous studies.Consequently,the proposed method is expected to recognize facial expressions,especially fear and anger,to help prevent severe accidents by detecting security-related or dangerous actions in advance.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea,funded by the Ministry of Education(Nos.NRF2017R1A6A1A03015496,RS-2023-00249555).
文摘As Internet of Things(IoT)technology develops,integrating network functions into diverse equipment introduces new challenges,particularly in dealing with counterfeit issues.Over the past few decades,research efforts have focused on leveraging electromyogram(EMG)for personal recognition,aiming to address security concerns.However,obtaining consistent EMG signals from the same individual is inherently challenging,resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition.Notably,conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities.This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary classifier,effectively mitigating the impact of data irregularities in EMG-based recognition.The proposed method employs empirical mode decomposition(EMD)to extract distinctive features.The model comprises two sub-networks designed to follow the siamese network architecture and a decision network integrated with the novel auxiliary classifier,specifically designed to address data irregularities.The two sub-networks sharing a weight calculate the compatibility function.The auxiliary classifier collaborates with a neural network to implement an attention mechanism.The attention mechanism using the auxiliary classifier solves the data irregularity problem by improving the importance of the EMG gesture section.Experimental results validated the efficacy of the proposed personal recognition method,achieving a remarkable 94.35%accuracy involving 100 subjects from the multisession CU_sEMG database(DB).This performance outperforms the existing approaches by 3%,employing auxiliary classifiers.Furthermore,an additional experiment yielded an improvement of over 0.85%of Ninapro DB,3%of CU_sEMG DB compared to the existing EMG-based recognition methods.Consequently,the proposed personal recognition using EMG proves to secure IoT devices,offering robustness against data irregularities.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2017R1A6A1A03015496)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1014033).
文摘As fifth generation technology standard(5G)technology develops,the possibility of being exposed to the risk of cyber-attacks that exploits vulnerabilities in the 5G environment is increasing.The existing personal recognitionmethod used for granting permission is a password-basedmethod,which causes security problems.Therefore,personal recognition studies using bio-signals are being conducted as a method to access control to devices.Among bio-signal,surface electromyogram(sEMG)can solve the existing personal recognition problem that was unable to the modification of registered information owing to the characteristic changes in its signal according to the performed operation.Furthermore,as an advantage,sEMG can be conveniently measured from arms and legs.This paper proposes a personal recognition method using sEMG,based on a multi-stream convolutional neural network(CNN).The proposed method decomposes sEMG signals into intrinsic mode functions(IMF)using empirical mode decomposition(EMD)and transforms each IMF into a spectrogram.Personal recognition is performed by analyzing time–frequency features from the spectrogram transformed intomulti-streamCNN.The database(DB)adopted in this paper is the Ninapro DB,which is a benchmark EMG DB.The experimental results indicate that the personal recognition performance of the multi-stream CNN using the IMF spectrogram improved by 1.91%,compared with the singlestream CNN using the spectrogram of raw sEMG.
基金supported by Institute for Information&communications Technology Planning&Evaluation(IITP)funded by the Korea government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security)Research Cluster Project,R20143,by Zayed University Research Office.
文摘Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.
基金This work was supported by the National Natural Science Foundation of China(No.61802214)the Natural Science Foundation of Shandong Province(Nos.ZR2019BF009,ZR2018LF007,ZR2017MF0,ZR2016YL011)+2 种基金the Shandong Provincial Key Research and Development Program of China(2018GGX1010052017,CXGC07012016,GGX109001)the Project of Shandong Province Higher Educational Science and Technology Program(No.J17KA049)the Global Infrastructure Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2018K1A3A1A20026485).
文摘Multi-factor authentication(MFA)was proposed by Pointcheval et al.[Pointcheval and Zimmer(2008)]to improve the security of single-factor(and two-factor)authentication.As the backbone of multi-factor authentication,biometric data are widely observed.Especially,how to keep the privacy of biometric at the password database without impairing efficiency is still an open question.Using the vulnerability of encryption(or hash)algorithms,the attacker can still launch offline brute-force attacks on encrypted(or hashed)biometric data.To address the potential risk of biometric disclosure at the password database,in this paper,we propose a novel efficient and secure MFA key exchange(later denoted as MFAKE)protocol leveraging the Pythia PRF service and password-to-random(or PTR)protocol.Armed with the PTR protocol,a master password pwd can be translated by the user into independent pseudorandom passwords(or rwd)for each user account with the help of device(e.g.,smart phone).Meanwhile,using the Pythia PRF service,the password database can avoid leakage of the local user’s password and biometric data.This is the first paper to achieve the password and biometric harden service simultaneously using the PTR protocol and Pythia PRF.
文摘Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.
文摘In recent years, Hyper Text Transfer Protocol （HTTP） spreads quickly and steadily in the usage of mobile applications as a common web protocol, so that the mobile applications can also benefit from HTTP/2, which is the new version of HTTP based on SPDY developed by Google to speed up the Internet transmission speed. HTTP/2 enables a more efficient use of network resources and a reduced perception of latency by in- troducing header field compression and allowing multiple concurrent exchanges on the same connection. However, what H3TP/2 focuses on is visiting websites through a browser, and mobile applications are not considered much. In this paper, firstly, mobile applications are classified based on the da- ta flow characteristics. Based on the classification, we propose an optimization of HTTP/2 for mobile applications, called HTTP/2-Advance, which uses multiple Transmission Control Protocol （TCP） connections to multiplex HTYP requests and responses. Then we build a tiny system which simulates actu- al requests and responses between mobile applications and servers. We figure out the best choice of the number of multiple TCP connections for mobile applications, and compare the performance of HTTP, HTTP/2 and HTrP/2-Advance in both simulated and in-situ experiments in our system.
文摘Dengue infection affects more than half of the world’s population,with 1 billion symp-tomatic cases identified per year and several distinct genetic serotypes:DENV 1–4.Transmitted via the mosquito bite,the dengue virus infects Langerhans cells.Monocytes,B lymphocytes,and mast cells infected with dengue virus produce various cytokines although it is not clear which ones are predominant during DHF disease.A mathemat-ical model of the Dengue virus infection is developed according to complex dynamics determined by many factors.Starting from a state of equilibrium that we could define as“virus-free”asymptotically stable with a viral reproduction number lower than one which means a very effective action of the innate immune system:it stops the infectious process,the mathematical analysis of stability in the presence of the virus demonstrates that the proposed model is dynamically influenced.Dengue fever affects more than half of the world’s population,with 1 billion symptomatic cases and multiple genetic serotypes confirmed each year,which simulates a network of interactions between the various populations involved without considering the speeds of the processes in question which are indicated in a separate computation.In this research,a hybrid approach of petri nets is utilized to connect the discrete models of dengue.
文摘Background:CAR-T cells are chimeric antigen receptor(CAR)-T cells;they are targetspecific engineered cells on tumor cells and produce T cell-mediated antitumor responses.CAR-T cell therapy is the“first-line”therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and leukemia.This adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since,precisely for this type of tumor,the use of immune checkpoint inhibitors remained disappointing.Although CAR-T is a promising therapeutic technique,there are therapeutic limits linked to the persistence of these cells and to the tumor’s immune escape.CAR-T cell engineering techniques are allowed to express interleukin IL-36,and seem to be much more efficient in antitumoral action.IL-36 is involved in the long-term antitumor action,allowing CAR-T cells to be more efficient in their antitumor action due to a“cross-talk”action between the“IL-36/dendritic cells”axis and the adaptive immunity.Methods:This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of tumor.The model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy:IL-36,“armored”CAR-T cells(i.e.,engineered to produce IL-36)and the tumor cell population,focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T cells.Mathematical model was developed and numerical simulations were carried out during this research.The development of the model with stability analysis by conditions of Routh–Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment,making therapy more effective against the“solid tumor”.Findings:Primary malignant bone tumors are quite rare(about 3%of all tumors)and the vast majority consist of osteosarcomas and Ewing’s sarcoma and,approximately,the 20%of patients undergo metastasis situations that is the most likely cause of death.Interpretation:In bone tumor like osteosarcoma,there is a variation of the cellular mechanical characteristics that can influence the efficacy of chemotherapy and increase the metastatic capacity;an approach related to adoptive immunotherapy with CAR-T cells may be a possible solution because this type of therapy is not influenced by the biomechanics of cancer cells which show peculiar characteristics.
