Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterativ...Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.展开更多
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat...High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.展开更多
High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation lear...High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.展开更多
A non-negative latent factor(NLF)model is able to be built efficiently via a single latent factor-dependent,non-negative and multiplicative update(SLF-NMU)algorithm for performing precise representation to high-dimens...A non-negative latent factor(NLF)model is able to be built efficiently via a single latent factor-dependent,non-negative and multiplicative update(SLF-NMU)algorithm for performing precise representation to high-dimensional and incomplete(HDI)matrix from many kinds of big-data-related applications.However,an SLF-NMU algorithm updates a latent factor relying on the current update increment only without considering past learning information,making a resultant model suffer from slow convergence.To address this issue,this study proposes a proportional integral(PI)controller-enhanced NLF(PI-NLF)model with two-fold ideas:1)Designing an increment refinement(IR)mechanism,which formulates the current and past update increments as the proportional and integral terms of a PI controller,thereby assimilating the past update information into the learning scheme smoothly with high efficiency;2)Deriving an IR-based SLF-NMU(ISN)algorithm,which updates a latent factor following the principle of an IR mechanism,thus significantly accelerating an NLF model's convergence rate.The simulation results on eight HDI matrices collected by real applications validate that a PI-NLF model outstrips several leading-edge models in both computational efficiency and accuracy when estimating missing data within an HDI matrix.The proposed PI-NLF model can be effectively applied to applications involving HDI matrix like e-commerce system,social network,and cloud service system.The code is available at https://github.com/yuanyeswu/PINLF/blob/mainIPINLF-code.zip.展开更多
Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and...Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).展开更多
Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
Latent factor models have become a workhorse for a large number of recommender systems. While these sys- tems are built using ratings data, which is typically assumed static, the ability to incorporate different kinds...Latent factor models have become a workhorse for a large number of recommender systems. While these sys- tems are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning （or refining） user parameters from such feedback. We propose a scheme where users are presented with a sequence of pair- wise preference questions： ＂Do you prefer item A over B？＂ User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporat- ing the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain cri- terion. We validate the scheme on the Netflix movie ratings data set and a proprietary television viewership data set. A user study and automated experiments validate our findings.展开更多
We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factor...We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.展开更多
There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transfo...There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transforming growth factor-β(TGF-β)signalling in the cartilage and subchondral bone of the TMJ using a temporomandibular joint disorder(TMD) rat model, an ageing mouse model and a Camurati–Engelmann disease(CED) mouse model. In the three animal models, the subchondral bone phenotypes in the mandibular condyles were evaluated by μCT, and changes in TMJ condyles were examined by TRAP staining and immunohistochemical analysis of Osterix and p-Smad2/3. Condyle degradation was confirmed by Safranin O staining, the Mankin and OARSI scoring systems and type X collagen(Col X), p-Smad2/3 a and Osterix immunohistochemical analyses. We found apparent histological phenotypes of TMJ-OA in the TMD, ageing and CED animal models, with abnormal activation of TGF-βsignalling in the condylar cartilage and subchondral bone. Moreover, inhibition of TGF-β receptor I attenuated TMJ-OA progression in the TMD models. Therefore, aberrant activation of TGF-β signalling could be a key player in TMJ-OA development.展开更多
Psychiatric comorbidity is common in symptombased diagnoses like autism spectrum disorder(ASD),attention/deficit hyper-activity disorder(ADHD),and obsessivecompulsive disorder(OCD).However,these co-occurring symptoms ...Psychiatric comorbidity is common in symptombased diagnoses like autism spectrum disorder(ASD),attention/deficit hyper-activity disorder(ADHD),and obsessivecompulsive disorder(OCD).However,these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level.Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework,we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis.Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention.Four factors,identified as variably co-expressed in each patient,were significantly correlated with distinct symptom domains(r=–0.26–0.53,P<0.05):behavioral regulation(Factor-1),communication(Factor-2),anxiety(Factor-3),adaptive behaviors(Factor-4).Moreover,we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety,at the degree to which factor expression was significantly predictive of individual symptom scores(r=0.18–0.5,P<0.01).Importantly,peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes(r=0.39,P<0.05).Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts,which may promote quantitative psychiatric diagnosis and personalized intervention.展开更多
AIM: To investigate the role of nuclear factor κB(NF-κB) in the regulation of Epstein-Barr virus(EBV) latent membrane protein 1(LMP1) in EBV transformed cells. METHODS: LMP1 expression was examined in EBV transforme...AIM: To investigate the role of nuclear factor κB(NF-κB) in the regulation of Epstein-Barr virus(EBV) latent membrane protein 1(LMP1) in EBV transformed cells. METHODS: LMP1 expression was examined in EBV transformed human B lymphocytes with modulation of NF-κB activity. RESULTS: EBV infection is associated with several human cancers. EBV LMP1 is required for efficient transformation of adult primary B cells in vitro, and is expressed in several pathogenic stages of EBVassociated cancers. Regulation of EBV LMP1 involves both viral and cellular factors. LMP1 activates NF-κB signaling pathway that is a part of the EBV transformation program. However, the relation between NF-κB and LMP1 expression is not well established yet. In this report, we found that blocking the NF-κB activity by Inhibitor of κB stimulated LMP1 expression, while the overexpression of NF-κB repressed LMP1 expression in EBV-transformed IB4 cells. In addition, LMP1 repressed its own promoter activities in reporter assays, and the repression was associated with the activation of NF-κB. Moreover, NF-κB alone is sufficient to repress LMP1 promoter activities. CONCLUSION: Our data suggest LMP1 may repress its own expression through NF-κB in EBV transformed cells and shed a light on LMP1 regulation during EBV transformation.展开更多
The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advan...The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advances in technology create new levels of stress and expectations on healthcare workers (HCW) with an endless infusion of requirements from multiple authoritative sources that are tracked and monitored. The quality of care and safety of patients is affected by the wellbeing of HCWs who now practice in an environment that has become more complex to navigate, often expending limited neural resource (brainpower) on classifying, organizing, constantly making decisions on how and when they can accomplish what is required(extraneous cognitive load) in addition to direct patient care. New information demonstrates profound biological impact on the brains of those who have burnout in areas that affect the quality and safety of the decisions they make-which affects risk to patients in healthcare. Healthcare administration curriculum currently does not include ways to address these stress-induced problems in healthcare delivery. The science of human factors and ergonomics (HFE) promotes system performance and worker wellbeing. Patient safety is one component of system performance. Since many requirements come without resource to accomplish them, it becomes incumbent upon health system leadership to organize the means for completion of these to minimize the needless loss of brain power diverted away from the delivery of patient care. Human Factor-Based Leadership (HFBL) is an interactive, problem solving seminar series designed for healthcare leaders. The purpose is to provide relevant human factor science to integrate into their leadership and management decisions to make HCWs occupational environment more manageable and sustainable-which makes safer conditions for clinician wellbeing and patient care. After learning the content, a cohort of healthcare leaders believed that adequately addressing HFE in healthcare delivery would significantly reduce clinician burnout and risk of latent errors from upstream leadership decisions. An overview of the content of the seminars is described. Leadership feedback on usability of these seminars is reported. Three HFBL seminars described are Human Factor Relevance in Leadership, Biopsychosocial Approach to Wellness and Burnout, Human Factor Based Leadership: Examples and Applications.展开更多
Introduction: Tuberculosis is a major health problem in developing countries including Sudan. Screening for TB cases through Household contacts (HHCs) investigation is an appropriate strategy to interrupt transmission...Introduction: Tuberculosis is a major health problem in developing countries including Sudan. Screening for TB cases through Household contacts (HHCs) investigation is an appropriate strategy to interrupt transmission of TB. Objectives: To determine the prevalence tuberculosis infection and risk factors for tuberculosis infection among household contacts in Wadimadani locality, Central State, Sudan, between November 2015 and April 2016. Methods: An analytical cross-sectional study conducted. During study period, to confirm TB diagnosis, all suspect contacts were tested through sputum samples, tuberculin skin test or chest X-ray. Structured questionnaire was used to collect socio-demographic and environmental factors. Results: One hundred forty six patients of smear-positive pulmonary tuberculosis were included in the study, 657 household contacts were identified and screened. Forty three new TB cases were detected from household contacts, yielding a prevalence of 6.5% (95% confidence interval = 0.05, 0.09) of latent tuberculosis infection (LTBI). Two factors were significantly associated with LTBI among HHCs: duration of contact with a TB patient ≤ 4 months (P = 0.03) and the educational status (P = 0.02). Conclusion: Screening of HHCs of index case of TB will contribute in early detection and treatment of new cases, and considered as a forward step towards eliminating TB.展开更多
基金supported in part by the National Natural Science Foundation of China (6177249391646114)+1 种基金Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020)in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences
文摘Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.
基金supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337)in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019jcyjjqX0013)+1 种基金in part by the Chinese Academy of Sciences“Light of West China”Program,in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciencesby Technology Innovation and Application Development Project of Chongqing,China(cstc2019jscx-fxydX0027)。
文摘High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.
基金supported in part by the National Natural Science Foundation of China (62372385, 62272078, 62002337)the Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX1486, CSTB2023NSCQ-LZX0069)the Deanship of Scientific Research at King Abdulaziz University, Jeddah, Saudi Arabia (RG-12-135-43)。
文摘High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078)the Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069).
文摘A non-negative latent factor(NLF)model is able to be built efficiently via a single latent factor-dependent,non-negative and multiplicative update(SLF-NMU)algorithm for performing precise representation to high-dimensional and incomplete(HDI)matrix from many kinds of big-data-related applications.However,an SLF-NMU algorithm updates a latent factor relying on the current update increment only without considering past learning information,making a resultant model suffer from slow convergence.To address this issue,this study proposes a proportional integral(PI)controller-enhanced NLF(PI-NLF)model with two-fold ideas:1)Designing an increment refinement(IR)mechanism,which formulates the current and past update increments as the proportional and integral terms of a PI controller,thereby assimilating the past update information into the learning scheme smoothly with high efficiency;2)Deriving an IR-based SLF-NMU(ISN)algorithm,which updates a latent factor following the principle of an IR mechanism,thus significantly accelerating an NLF model's convergence rate.The simulation results on eight HDI matrices collected by real applications validate that a PI-NLF model outstrips several leading-edge models in both computational efficiency and accuracy when estimating missing data within an HDI matrix.The proposed PI-NLF model can be effectively applied to applications involving HDI matrix like e-commerce system,social network,and cloud service system.The code is available at https://github.com/yuanyeswu/PINLF/blob/mainIPINLF-code.zip.
文摘Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
文摘Latent factor models have become a workhorse for a large number of recommender systems. While these sys- tems are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning （or refining） user parameters from such feedback. We propose a scheme where users are presented with a sequence of pair- wise preference questions： ＂Do you prefer item A over B？＂ User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporat- ing the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain cri- terion. We validate the scheme on the Netflix movie ratings data set and a proprietary television viewership data set. A user study and automated experiments validate our findings.
基金supported by grants from the National Natural Science Foundation of China(72171088,71803049,72003205)the Ministry of Education of the People's Republic of China of Humanities and Social Sciences Youth Fundation(20YJC790142)the General Project of Social Science Planning in Guangdong Province,China(GD22CYJ12).
文摘We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.
基金supported by 2016JQ0054 and NSFC grants 81470711 to L.Z.National Key Research and Development Program of China 2016YFC1102700 to X.Z.
文摘There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transforming growth factor-β(TGF-β)signalling in the cartilage and subchondral bone of the TMJ using a temporomandibular joint disorder(TMD) rat model, an ageing mouse model and a Camurati–Engelmann disease(CED) mouse model. In the three animal models, the subchondral bone phenotypes in the mandibular condyles were evaluated by μCT, and changes in TMJ condyles were examined by TRAP staining and immunohistochemical analysis of Osterix and p-Smad2/3. Condyle degradation was confirmed by Safranin O staining, the Mankin and OARSI scoring systems and type X collagen(Col X), p-Smad2/3 a and Osterix immunohistochemical analyses. We found apparent histological phenotypes of TMJ-OA in the TMD, ageing and CED animal models, with abnormal activation of TGF-βsignalling in the condylar cartilage and subchondral bone. Moreover, inhibition of TGF-β receptor I attenuated TMJ-OA progression in the TMD models. Therefore, aberrant activation of TGF-β signalling could be a key player in TMJ-OA development.
基金supported by the National Natural Science Foundation(82151303)the National Key R&D Program of China(2021ZD0204002)+3 种基金the Key-Area Research and Development Program of Guangdong Province(2019B030335001)Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)Strategic Priority Research Program of Chinese Academy of Science(XDB32000000)supported in part by the Postdoctoral Fellowship of the Peking-Tsinghua Center for Life Sciences.GSP data were provided by the Brain Genomics Superstruct Project of Harvard University and the Massachusetts General Hospital,(Principal Investigators:Randy Buckner,Joshua Roffman,and Jordan Smoller),with support from the Center for Brain Science Neuroinformatics Research Group,the Athinoula A.Martinos Center for Biomedical Imaging,and GSP Open Access Documentation the Center for Human Genetic Research.20 individual investigators at Harvard and MGH generously contributed data to the overall project.
文摘Psychiatric comorbidity is common in symptombased diagnoses like autism spectrum disorder(ASD),attention/deficit hyper-activity disorder(ADHD),and obsessivecompulsive disorder(OCD).However,these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level.Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework,we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis.Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention.Four factors,identified as variably co-expressed in each patient,were significantly correlated with distinct symptom domains(r=–0.26–0.53,P<0.05):behavioral regulation(Factor-1),communication(Factor-2),anxiety(Factor-3),adaptive behaviors(Factor-4).Moreover,we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety,at the degree to which factor expression was significantly predictive of individual symptom scores(r=0.18–0.5,P<0.01).Importantly,peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes(r=0.39,P<0.05).Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts,which may promote quantitative psychiatric diagnosis and personalized intervention.
基金Supported by Grants from the NIH CA138213,RR15635Department of Defense W81XWH-12-1-0225(Luwen Zhang)Qianli Wang was partially supported by Undergraduate Creative Activities and Research Experiences and Beckman Scholars Program
文摘AIM: To investigate the role of nuclear factor κB(NF-κB) in the regulation of Epstein-Barr virus(EBV) latent membrane protein 1(LMP1) in EBV transformed cells. METHODS: LMP1 expression was examined in EBV transformed human B lymphocytes with modulation of NF-κB activity. RESULTS: EBV infection is associated with several human cancers. EBV LMP1 is required for efficient transformation of adult primary B cells in vitro, and is expressed in several pathogenic stages of EBVassociated cancers. Regulation of EBV LMP1 involves both viral and cellular factors. LMP1 activates NF-κB signaling pathway that is a part of the EBV transformation program. However, the relation between NF-κB and LMP1 expression is not well established yet. In this report, we found that blocking the NF-κB activity by Inhibitor of κB stimulated LMP1 expression, while the overexpression of NF-κB repressed LMP1 expression in EBV-transformed IB4 cells. In addition, LMP1 repressed its own promoter activities in reporter assays, and the repression was associated with the activation of NF-κB. Moreover, NF-κB alone is sufficient to repress LMP1 promoter activities. CONCLUSION: Our data suggest LMP1 may repress its own expression through NF-κB in EBV transformed cells and shed a light on LMP1 regulation during EBV transformation.
文摘The majority of errors in healthcare are from systems factors that create the latent conditions for error to occur. The majority of occupational stressors causing burnout are also the result of systemic factors. Advances in technology create new levels of stress and expectations on healthcare workers (HCW) with an endless infusion of requirements from multiple authoritative sources that are tracked and monitored. The quality of care and safety of patients is affected by the wellbeing of HCWs who now practice in an environment that has become more complex to navigate, often expending limited neural resource (brainpower) on classifying, organizing, constantly making decisions on how and when they can accomplish what is required(extraneous cognitive load) in addition to direct patient care. New information demonstrates profound biological impact on the brains of those who have burnout in areas that affect the quality and safety of the decisions they make-which affects risk to patients in healthcare. Healthcare administration curriculum currently does not include ways to address these stress-induced problems in healthcare delivery. The science of human factors and ergonomics (HFE) promotes system performance and worker wellbeing. Patient safety is one component of system performance. Since many requirements come without resource to accomplish them, it becomes incumbent upon health system leadership to organize the means for completion of these to minimize the needless loss of brain power diverted away from the delivery of patient care. Human Factor-Based Leadership (HFBL) is an interactive, problem solving seminar series designed for healthcare leaders. The purpose is to provide relevant human factor science to integrate into their leadership and management decisions to make HCWs occupational environment more manageable and sustainable-which makes safer conditions for clinician wellbeing and patient care. After learning the content, a cohort of healthcare leaders believed that adequately addressing HFE in healthcare delivery would significantly reduce clinician burnout and risk of latent errors from upstream leadership decisions. An overview of the content of the seminars is described. Leadership feedback on usability of these seminars is reported. Three HFBL seminars described are Human Factor Relevance in Leadership, Biopsychosocial Approach to Wellness and Burnout, Human Factor Based Leadership: Examples and Applications.
文摘Introduction: Tuberculosis is a major health problem in developing countries including Sudan. Screening for TB cases through Household contacts (HHCs) investigation is an appropriate strategy to interrupt transmission of TB. Objectives: To determine the prevalence tuberculosis infection and risk factors for tuberculosis infection among household contacts in Wadimadani locality, Central State, Sudan, between November 2015 and April 2016. Methods: An analytical cross-sectional study conducted. During study period, to confirm TB diagnosis, all suspect contacts were tested through sputum samples, tuberculin skin test or chest X-ray. Structured questionnaire was used to collect socio-demographic and environmental factors. Results: One hundred forty six patients of smear-positive pulmonary tuberculosis were included in the study, 657 household contacts were identified and screened. Forty three new TB cases were detected from household contacts, yielding a prevalence of 6.5% (95% confidence interval = 0.05, 0.09) of latent tuberculosis infection (LTBI). Two factors were significantly associated with LTBI among HHCs: duration of contact with a TB patient ≤ 4 months (P = 0.03) and the educational status (P = 0.02). Conclusion: Screening of HHCs of index case of TB will contribute in early detection and treatment of new cases, and considered as a forward step towards eliminating TB.
