Background:Cytotoxic T lymphocytes(CD8+T)cells function critically in mediating anti-tumor immune response in cancer patients.Characterizing the specific functions of CD8+T cells in lung adenocarcinoma(LUAD)could help ...Background:Cytotoxic T lymphocytes(CD8+T)cells function critically in mediating anti-tumor immune response in cancer patients.Characterizing the specific functions of CD8+T cells in lung adenocarcinoma(LUAD)could help better understand local anti-tumor immune responses and estimate the effect of immunotherapy.Methods:Gens related to CD8+T cells were identified by cluster analysis based on the single-cell sequencing data of three LUAD tissues and their paired normal tissues.Weighted gene co-expression network analysis(WGCNA),consensus clustering,differential expression analysis,least absolute shrinkage and selection operator(LASSO)and Cox regression analysis were conducted to classify molecular subtypes for LUAD and to develop a risk model using prognostic genes related to CD8+T cells.Expression of the genes in the prognostic model,their effects on tumor cell invasion,and interactions with CD8+T cells were verified by cell experiments.Results:This study defined two LUAD clusters(CD8+0 and CD8+1)based on CD8+T cells,with cluster CD8+0 being significantly associated with the prognosis of LUAD.Three heterogeneous subtypes(clusters 1,2,and 3)differing in prognosis,genome mutation events,and immune status were categorized using 42 prognostic genes.A prognostic model created based on 11 significant genes(including CD200R1,CLEC17A,ZC3H12D,GNG7,SNX30,CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2,and KRT81)was able to independently estimate the death risk for patients in different LUAD cohorts.Moreover,the model also showed general applicability in external validation cohorts.Low-risk patients could benefit more from taking immunotherapy and were significantly related to the resistance to anticancer drugs.The results from cell experiments demonstrated that the expression of CD200R1,CLEC17A,ZC3H12D,GNG7,and SNX30 was significantly downregulated,while that of CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2 and KRT81 was upregulated in LUAD cells.Inhibition of CD200R1 greatly increased the invasiveness of the LUAD cells,but inhibiting CDCP1 expression weakened the invasion ability of LUAD cells.Conclusion:This study defined two prognostic CD8+T cell clusters and classified three heterogeneous molecular subtypes for LUAD.A prognostic model predictive of the potential effects of immunotherapy on LUAD patients was developed.展开更多
It is well-known that connectivity is closely related to diagnosability.If the relationships be-tween them can be established,many kinds of diagnosability will be determined directly.So far,some notable relationships ...It is well-known that connectivity is closely related to diagnosability.If the relationships be-tween them can be established,many kinds of diagnosability will be determined directly.So far,some notable relationships between connectivity and diagnosability had been revealed.This paper in-tends to find out the relationship between extra connectivity and t/k-diagnosability under the PMC(Preparata,Metze,and Chien)model.Then,applying this relationship,the t/k-diagnosability of bijective connection(BC)networks are determined conveniently.展开更多
This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents ...This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.展开更多
Background: Diabetes education is crucial in empowering persons with Type 1 diabetes (T1DM) and their families to properly manage the condition by providing comprehensive knowledge, tools, and support. It boosts one’...Background: Diabetes education is crucial in empowering persons with Type 1 diabetes (T1DM) and their families to properly manage the condition by providing comprehensive knowledge, tools, and support. It boosts one’s belief in their ability to succeed, encourages following medical advice, and adds to the general enhancement of health. Objective: This study is to investigate the effectiveness of diabetes education in empowering individuals with Type 1 Diabetes Mellitus (T1DM) and their families to effectively manage the condition. Furthermore, it strives to improve nursing care for families whose children have been diagnosed with Type 1 Diabetes Mellitus (T1DM). Design: This research study investigates the efficacy of diabetes education in empowering individuals with Type 1 Diabetes Mellitus (T1DM) and their families to effectively handle the condition. Materials and Methods: A systematic search was conducted between the years 2000 and 2022, utilizing the Medline and Google Scholar databases. The purpose of the search was to uncover relevant papers pertaining to diabetes education, management of Type 1 Diabetes Mellitus (T1DM), nurse care, and empowerment. The search focused on peer-reviewed research, clinical trials, and scholarly articles that evaluated the efficacy of diabetes education in empowering individuals and families. Results: Diabetes education is crucial for understanding and controlling T1DM. It includes personalized sessions, webinars, group classes, and clinics that provide customized therapies. Comprehensive education enhances glycemic control and family dynamics. Nevertheless, the implementation of diabetes education for families requires specific standards, especially in the field of nursing. Conclusion: Diabetes education is essential for effectively managing Type 1 Diabetes Mellitus (T1DM), providing patients and families with crucial knowledge, resources, and confidence. It encourages independence in-home care and provides explicit guidelines for diabetic nurses to improve nursing care.展开更多
文摘Background:Cytotoxic T lymphocytes(CD8+T)cells function critically in mediating anti-tumor immune response in cancer patients.Characterizing the specific functions of CD8+T cells in lung adenocarcinoma(LUAD)could help better understand local anti-tumor immune responses and estimate the effect of immunotherapy.Methods:Gens related to CD8+T cells were identified by cluster analysis based on the single-cell sequencing data of three LUAD tissues and their paired normal tissues.Weighted gene co-expression network analysis(WGCNA),consensus clustering,differential expression analysis,least absolute shrinkage and selection operator(LASSO)and Cox regression analysis were conducted to classify molecular subtypes for LUAD and to develop a risk model using prognostic genes related to CD8+T cells.Expression of the genes in the prognostic model,their effects on tumor cell invasion,and interactions with CD8+T cells were verified by cell experiments.Results:This study defined two LUAD clusters(CD8+0 and CD8+1)based on CD8+T cells,with cluster CD8+0 being significantly associated with the prognosis of LUAD.Three heterogeneous subtypes(clusters 1,2,and 3)differing in prognosis,genome mutation events,and immune status were categorized using 42 prognostic genes.A prognostic model created based on 11 significant genes(including CD200R1,CLEC17A,ZC3H12D,GNG7,SNX30,CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2,and KRT81)was able to independently estimate the death risk for patients in different LUAD cohorts.Moreover,the model also showed general applicability in external validation cohorts.Low-risk patients could benefit more from taking immunotherapy and were significantly related to the resistance to anticancer drugs.The results from cell experiments demonstrated that the expression of CD200R1,CLEC17A,ZC3H12D,GNG7,and SNX30 was significantly downregulated,while that of CDCP1,NEIL3,IGF2BP1,RHOV,ABCC2 and KRT81 was upregulated in LUAD cells.Inhibition of CD200R1 greatly increased the invasiveness of the LUAD cells,but inhibiting CDCP1 expression weakened the invasion ability of LUAD cells.Conclusion:This study defined two prognostic CD8+T cell clusters and classified three heterogeneous molecular subtypes for LUAD.A prognostic model predictive of the potential effects of immunotherapy on LUAD patients was developed.
基金Supported by the National Natural Science Foundation of China(No.62262032,61862035,61562046)National Natural Science Foundation of Jiangxi Province(No.20202BABL202042)the Science and Technology Project of Jiangxi Provincial Education Department(No.GJJ2201604,GJJ201033,GJJ190560).
文摘It is well-known that connectivity is closely related to diagnosability.If the relationships be-tween them can be established,many kinds of diagnosability will be determined directly.So far,some notable relationships between connectivity and diagnosability had been revealed.This paper in-tends to find out the relationship between extra connectivity and t/k-diagnosability under the PMC(Preparata,Metze,and Chien)model.Then,applying this relationship,the t/k-diagnosability of bijective connection(BC)networks are determined conveniently.
基金This study was co-supported by the National Key R&D Program of China(No.2021YFF0603904)National Natural Science Foundation of China(U1733203)Safety Capacity Building Project of Civil Aviation Administration of China(TM2019-16-1/3).
文摘This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.
文摘Background: Diabetes education is crucial in empowering persons with Type 1 diabetes (T1DM) and their families to properly manage the condition by providing comprehensive knowledge, tools, and support. It boosts one’s belief in their ability to succeed, encourages following medical advice, and adds to the general enhancement of health. Objective: This study is to investigate the effectiveness of diabetes education in empowering individuals with Type 1 Diabetes Mellitus (T1DM) and their families to effectively manage the condition. Furthermore, it strives to improve nursing care for families whose children have been diagnosed with Type 1 Diabetes Mellitus (T1DM). Design: This research study investigates the efficacy of diabetes education in empowering individuals with Type 1 Diabetes Mellitus (T1DM) and their families to effectively handle the condition. Materials and Methods: A systematic search was conducted between the years 2000 and 2022, utilizing the Medline and Google Scholar databases. The purpose of the search was to uncover relevant papers pertaining to diabetes education, management of Type 1 Diabetes Mellitus (T1DM), nurse care, and empowerment. The search focused on peer-reviewed research, clinical trials, and scholarly articles that evaluated the efficacy of diabetes education in empowering individuals and families. Results: Diabetes education is crucial for understanding and controlling T1DM. It includes personalized sessions, webinars, group classes, and clinics that provide customized therapies. Comprehensive education enhances glycemic control and family dynamics. Nevertheless, the implementation of diabetes education for families requires specific standards, especially in the field of nursing. Conclusion: Diabetes education is essential for effectively managing Type 1 Diabetes Mellitus (T1DM), providing patients and families with crucial knowledge, resources, and confidence. It encourages independence in-home care and provides explicit guidelines for diabetic nurses to improve nursing care.
