Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collabora...Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.展开更多
In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the bla...In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.展开更多
The teleportation of an arbitrary n-particle state is proposed if n pairs of identical EPR states are utilized as quantum channels. Independent Bell state measurements are performed for joint measurement. By using a ...The teleportation of an arbitrary n-particle state is proposed if n pairs of identical EPR states are utilized as quantum channels. Independent Bell state measurements are performed for joint measurement. By using a special Latin square of order , explicit expressions of outcomes after the Bell state measurements by Alice (sender) and the corresponding unitary transformations by Bob (receiver) can be derived. It is shown that the teleportation of n-particle state can be implemented by a series of single-qubit teleportation.展开更多
Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and re...Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.展开更多
High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis mode...High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.展开更多
The teleportation of an arbitrary n-particle state is proposed when n pairs of entangled particles are utilized as quantum channels. It can be successfully realized with a certain probability which is determined by th...The teleportation of an arbitrary n-particle state is proposed when n pairs of entangled particles are utilized as quantum channels. It can be successfully realized with a certain probability which is determined by the smallest coefficients of n entangled pairs. Using a Latin square of order 2n, explicit expressions of two unitary operations corresponding to different Bell-basis measurements performed by Alice can be obtained at the end of Bob.展开更多
Microfinance, the provision of small size loans and other financial services to low mcome households, is often seen as the key innovation of the last 25 years in terms of means of reaching out to the poor and vulnerab...Microfinance, the provision of small size loans and other financial services to low mcome households, is often seen as the key innovation of the last 25 years in terms of means of reaching out to the poor and vulnerable. There is extensive experience in microfinance provision in both Asia and Latin America, but as yet relatively little use of the approach in China. This paper assesses different approaches to microfinance delivery using a threefoM distinction, the credit union approach, the non-government organization approach and the banking approach, to generalize across recent Asian and Latin American experience and discuss the role of microfinance in poverty reduction in a theoretical framework. Considering the current state of microfinance in China and international experience, we suggest the banking approach as the way to best increase outreach of micro-financial services in China.展开更多
Model validation is the most important part of building a supervised model.For building a model with good generalization performance one must have a sensible data splitting strategy,and this is crucial for model valid...Model validation is the most important part of building a supervised model.For building a model with good generalization performance one must have a sensible data splitting strategy,and this is crucial for model validation.In this study,we con-ducted a comparative study on various reported data splitting methods.The MixSim model was employed to generate nine simulated datasets with different probabilities of mis-classification and variable sample sizes.Then partial least squares for discriminant analysis and support vector machines for classification were applied to these datasets.Data splitting methods tested included variants of cross-validation,bootstrapping,bootstrapped Latin partition,Kennard-Stone algorithm(K-S)and sample set partitioning based on joint X-Y distances algorithm(SPXY).These methods were employed to split the data into training and validation sets.The estimated generalization performances from the validation sets were then compared with the ones obtained from the blind test sets which were generated from the same distribution but were unseen by the train-ing/validation procedure used in model construction.The results showed that the size of the data is the deciding factor for the qualities of the generalization performance estimated from the validation set.We found that there was a significant gap between the performance estimated from the validation set and the one from the test set for the all the data splitting methods employed on small datasets.Such disparity decreased when more samples were available for training/validation,and this is because the models were then moving towards approximations of the central limit theory for the simulated datasets used.We also found that having too many or too few samples in the training set had a negative effect on the estimated model performance,suggesting that it is necessary to have a good balance between the sizes of training set and validation set to have a reliable estimation of model performance.We also found that systematic sampling method such a展开更多
Urban electricity and heat networks(UEHN)consist of the coupling and interactions between electric power systems and district heating systems,in which the geographical and functional features of integrated energy syst...Urban electricity and heat networks(UEHN)consist of the coupling and interactions between electric power systems and district heating systems,in which the geographical and functional features of integrated energy systems are demonstrated.UEHN have been expected to provide an effective way to accommodate the intermittent and unpredictable renewable energy sources,in which the application of stochastic optimization approaches to UEHN analysis is highly desired.In this paper,we propose a chance-constrained coordinated optimization approach for UEHN considering the uncertainties in electricity loads,heat loads,and photovoltaic outputs,as well as the correlations between these uncertain sources.A solution strategy,which combines the Latin Hypercube Sampling Monte Carlo Simulation(LHSMCS)approach and a heuristic algorithm,is specifically designed to deal with the proposed chance-constrained coordinated optimization.Finally,test results on an UEHN comprised of a modified IEEE 33-bus system and a 32-node district heating system at Barry Island have verified the feasibility and effectiveness of the proposed framework.展开更多
Latin America shows one of the highest incidence rates of gastric cancer in the world,with variations in mortality rates among nations or even within countries belonging to this region.Gastric cancer is the result of ...Latin America shows one of the highest incidence rates of gastric cancer in the world,with variations in mortality rates among nations or even within countries belonging to this region.Gastric cancer is the result of a multifactorial complex process,for which a multistep model of carcinogenesis is currently accepted.Additionally to the infection with Helicobacter pylori,that plays a major role,environmental factors as well as genetic susceptibility factors are significant players at different stages in the gastric cancer process.The differences in population origin,demographic structure,socio-economic development,and the impact of globalization lifestyles experienced in Latin America in the last decades,all together offer opportunities for studying in this context the influence of genetic polymorphisms in the susceptibility to gastric cancer.The aim of this article is to discuss current trends on gastric cancer in Latin American countries and to review the available published information about studies of association of gene polymorphisms involved in gastric cancer susceptibility from this region of the world.A total of 40 genes or genomic regions and69 genetic variants,58%representing markers involved in inflammatory response,have been used in a number of studies in which predominates a low number of individuals(cases and controls)included.Polymorphisms of IL-1B(-511 C/T,14 studies;-31 T/C,10 studies)and IL-1RN(variable number of tandem repeats,17 studies)are the most represented ones in the reviewed studies.Other genetic variants recently evaluated in large metaanalyses and associated with gastric cancer risk were also analyzed in a few studies[e.g.,prostate stem cell antigen(PSCA),CDH1,Survivin].Further and better analysis centered in gene polymorphisms linked to other covariates,epidemiological studies and the information provided by meta-analyses and genome-wide association studies should help to improve our understanding of gastric cancer etiology in order to develop appropriate health programs in Latin Amer展开更多
Background:Rotavirus was the leading cause of childhood diarrhoea-related hospitalisations and death before the introduction of rotavirus vaccines.Methods:We describe the effectiveness of rotavirus vaccines to prevent...Background:Rotavirus was the leading cause of childhood diarrhoea-related hospitalisations and death before the introduction of rotavirus vaccines.Methods:We describe the effectiveness of rotavirus vaccines to prevent rotavirus infections and hospitalizations and the main rotavirus strains circulating before and after vaccine introduction through a systematic review and meta-analysis of studies published between 1990 and 2014.203 studies were included to estimate the proportion of infections due to rotavirus and 10 to assess the impact of the vaccines.41 of 46 studies in the post-vaccination period were used for meta-analysis of genotypes,20 to calculate VE against infection,eight for VE against hospitalisation and seven for VE against severe rotavirus-diarrhoea.Results:24.3%(95%CI 22.1–26.5)and 16.1%(95%CI 13.2–19.3)of cases of diarrhoea were due to rotavirus before and after vaccine introduction,respectively.The most prevalent G types after vaccine introduction were G2(51.6%,95%CI 38–65),G9(14.5%,95%CI 7–23)and G1(14.2%,95%CI 7–23);while the most prevalent P types were P[4](54.1%,95%CI 41–67)and P[8](33%,95%CI 22–46).G2P[4]was the most frequent genotype combination after vaccine introduction.Effectiveness was 53%(95%CI 46–60)against infection,73%(95%CI,66–78)against hospitalisation and 74%(95%CI,68.0–78.0)against severe diarrhoea.Reductions in hospitalisations and mortality due to diarrhoea were observed in countries that adopted universal rotavirus vaccination.Conclusions:Rotavirus vaccines are effective in preventing rotavirus-diarrhoea in children in Latin America.The vaccines were associated with changes in genotype distribution.展开更多
Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatia...Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.展开更多
The word “senescence” comes from the Latin senescens, meaning “to begin to age”, and is characterized by a long-lasting but reversible block in proliferation, resulting from stress-induced cell cycle arrest of pre...The word “senescence” comes from the Latin senescens, meaning “to begin to age”, and is characterized by a long-lasting but reversible block in proliferation, resulting from stress-induced cell cycle arrest of previously replication-competent cells.展开更多
Latin America, a region with a population greater than 600000000 individuals, is well known due to its wide geographic, socio-cultural and economic heterogeneity. Access to health care remains as the main barrier that...Latin America, a region with a population greater than 600000000 individuals, is well known due to its wide geographic, socio-cultural and economic heterogeneity. Access to health care remains as the main barrier that challenges routine screening, early diagnosis and proper treatment of hepatocellular carcinoma(HCC). Therefore, identification of population at risk, implementation of surveillance programs and access to curative treatments has been poorly obtained in the region. Different retrospective cohort studies from the region have shown flaws in the implementation process of routine surveillance and early HCC diagnosis. Furthermore, adherence to clinical practice guidelines recommendations assessed in two studies from Brazil and Argentina demonstrated that there is also room for improvement in this field, similarly than the one observed in Europe and the United States. In summary, Latin America shares difficulties in HCC decision-making processes similar to those from developed countries. However, a transversal limitation in the region is the poor access to health care with the consequent limitation to standard treatments for overall population. Specifically, universal health care access to the different World Health Organization levels is crucial, including improvement in research, education and continuous medical training in order to expand knowledge and generation of data promoting a continuous improvement in the care of HCC patients.展开更多
This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube s...This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.展开更多
文摘Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
基金Supported by Jiangsu Provincical Natural Science Foundation of China(Grant No.BK20140554)National Natural Science Foundation of China(Grant No.51409123)+2 种基金China Postdoctoral Science Foundation(Grant No.2015T80507)Innovation Project for Postgraduates of Jiangsu Province,China(Grant No.KYLX15_1066)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)
文摘In order to widen the high-efficiency operating range of a low-specific-speed centrifugal pump, an optimization process for considering efficiencies under 1.0Qd and 1.4Qd is proposed. Three parameters, namely, the blade outlet width b2, blade outlet angle β2, and blade wrap angle φ, are selected as design variables. Impellers are generated using the optimal Latin hypercube sampling method. The pump efficiencies are calculated using the software CFX 14.5 at two operating points selected as objectives. Surrogate models are also constructed to analyze the relationship between the objectives and the design variables. Finally, the particle swarm optimization algorithm is applied to calculate the surrogate model to determine the best combination of the impeller parameters. The results show that the performance curve predicted by numerical simulation has a good agreement with the experimental results. Compared with the efficiencies of the original impeller, the hydraulic efficiencies of the optimized impeller are increased by 4.18% and 0.62% under 1.0Qd and 1.4Qd, respectively. The comparison of inner flow between the original pump and optimized one illustrates the improvement of performance. The optimization process can provide a useful reference on performance improvement of other pumps, even on reduction of pressure fluctuations.
文摘The teleportation of an arbitrary n-particle state is proposed if n pairs of identical EPR states are utilized as quantum channels. Independent Bell state measurements are performed for joint measurement. By using a special Latin square of order , explicit expressions of outcomes after the Bell state measurements by Alice (sender) and the corresponding unitary transformations by Bob (receiver) can be derived. It is shown that the teleportation of n-particle state can be implemented by a series of single-qubit teleportation.
文摘Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel （DSM） which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerodynamic analysis are investigated, In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.
基金supported by National Natural Science Foundation of China (Grant Nos. 50875024,51105040)Excellent Young Scholars Research Fund of Beijing Institute of Technology,China (Grant No.2010Y0102)Defense Creative Research Group Foundation of China(Grant No. GFTD0803)
文摘High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.
文摘The teleportation of an arbitrary n-particle state is proposed when n pairs of entangled particles are utilized as quantum channels. It can be successfully realized with a certain probability which is determined by the smallest coefficients of n entangled pairs. Using a Latin square of order 2n, explicit expressions of two unitary operations corresponding to different Bell-basis measurements performed by Alice can be obtained at the end of Bob.
文摘Microfinance, the provision of small size loans and other financial services to low mcome households, is often seen as the key innovation of the last 25 years in terms of means of reaching out to the poor and vulnerable. There is extensive experience in microfinance provision in both Asia and Latin America, but as yet relatively little use of the approach in China. This paper assesses different approaches to microfinance delivery using a threefoM distinction, the credit union approach, the non-government organization approach and the banking approach, to generalize across recent Asian and Latin American experience and discuss the role of microfinance in poverty reduction in a theoretical framework. Considering the current state of microfinance in China and international experience, we suggest the banking approach as the way to best increase outreach of micro-financial services in China.
基金YX and RG thank Wellcome Trust for funding MetaboFlow(Grant 202952/Z/16/Z).
文摘Model validation is the most important part of building a supervised model.For building a model with good generalization performance one must have a sensible data splitting strategy,and this is crucial for model validation.In this study,we con-ducted a comparative study on various reported data splitting methods.The MixSim model was employed to generate nine simulated datasets with different probabilities of mis-classification and variable sample sizes.Then partial least squares for discriminant analysis and support vector machines for classification were applied to these datasets.Data splitting methods tested included variants of cross-validation,bootstrapping,bootstrapped Latin partition,Kennard-Stone algorithm(K-S)and sample set partitioning based on joint X-Y distances algorithm(SPXY).These methods were employed to split the data into training and validation sets.The estimated generalization performances from the validation sets were then compared with the ones obtained from the blind test sets which were generated from the same distribution but were unseen by the train-ing/validation procedure used in model construction.The results showed that the size of the data is the deciding factor for the qualities of the generalization performance estimated from the validation set.We found that there was a significant gap between the performance estimated from the validation set and the one from the test set for the all the data splitting methods employed on small datasets.Such disparity decreased when more samples were available for training/validation,and this is because the models were then moving towards approximations of the central limit theory for the simulated datasets used.We also found that having too many or too few samples in the training set had a negative effect on the estimated model performance,suggesting that it is necessary to have a good balance between the sizes of training set and validation set to have a reliable estimation of model performance.We also found that systematic sampling method such a
基金This work was supported in part by Natural Science Foundation of Jiangsu Province,China(No.BK20171433)in part by Science and Technology Project of State Grid Jiangsu Electric Power Corporation,China(No.J2018066).
文摘Urban electricity and heat networks(UEHN)consist of the coupling and interactions between electric power systems and district heating systems,in which the geographical and functional features of integrated energy systems are demonstrated.UEHN have been expected to provide an effective way to accommodate the intermittent and unpredictable renewable energy sources,in which the application of stochastic optimization approaches to UEHN analysis is highly desired.In this paper,we propose a chance-constrained coordinated optimization approach for UEHN considering the uncertainties in electricity loads,heat loads,and photovoltaic outputs,as well as the correlations between these uncertain sources.A solution strategy,which combines the Latin Hypercube Sampling Monte Carlo Simulation(LHSMCS)approach and a heuristic algorithm,is specifically designed to deal with the proposed chance-constrained coordinated optimization.Finally,test results on an UEHN comprised of a modified IEEE 33-bus system and a 32-node district heating system at Barry Island have verified the feasibility and effectiveness of the proposed framework.
文摘Latin America shows one of the highest incidence rates of gastric cancer in the world,with variations in mortality rates among nations or even within countries belonging to this region.Gastric cancer is the result of a multifactorial complex process,for which a multistep model of carcinogenesis is currently accepted.Additionally to the infection with Helicobacter pylori,that plays a major role,environmental factors as well as genetic susceptibility factors are significant players at different stages in the gastric cancer process.The differences in population origin,demographic structure,socio-economic development,and the impact of globalization lifestyles experienced in Latin America in the last decades,all together offer opportunities for studying in this context the influence of genetic polymorphisms in the susceptibility to gastric cancer.The aim of this article is to discuss current trends on gastric cancer in Latin American countries and to review the available published information about studies of association of gene polymorphisms involved in gastric cancer susceptibility from this region of the world.A total of 40 genes or genomic regions and69 genetic variants,58%representing markers involved in inflammatory response,have been used in a number of studies in which predominates a low number of individuals(cases and controls)included.Polymorphisms of IL-1B(-511 C/T,14 studies;-31 T/C,10 studies)and IL-1RN(variable number of tandem repeats,17 studies)are the most represented ones in the reviewed studies.Other genetic variants recently evaluated in large metaanalyses and associated with gastric cancer risk were also analyzed in a few studies[e.g.,prostate stem cell antigen(PSCA),CDH1,Survivin].Further and better analysis centered in gene polymorphisms linked to other covariates,epidemiological studies and the information provided by meta-analyses and genome-wide association studies should help to improve our understanding of gastric cancer etiology in order to develop appropriate health programs in Latin Amer
基金Financial support for this study was received from calls Edital MCTI/CNPq N°14/2013(#471747/2013-0)and Edital MEC/MCTI/CAPES/CNPQ/FAPS-PVE 2014(#400723/2014-0).
文摘Background:Rotavirus was the leading cause of childhood diarrhoea-related hospitalisations and death before the introduction of rotavirus vaccines.Methods:We describe the effectiveness of rotavirus vaccines to prevent rotavirus infections and hospitalizations and the main rotavirus strains circulating before and after vaccine introduction through a systematic review and meta-analysis of studies published between 1990 and 2014.203 studies were included to estimate the proportion of infections due to rotavirus and 10 to assess the impact of the vaccines.41 of 46 studies in the post-vaccination period were used for meta-analysis of genotypes,20 to calculate VE against infection,eight for VE against hospitalisation and seven for VE against severe rotavirus-diarrhoea.Results:24.3%(95%CI 22.1–26.5)and 16.1%(95%CI 13.2–19.3)of cases of diarrhoea were due to rotavirus before and after vaccine introduction,respectively.The most prevalent G types after vaccine introduction were G2(51.6%,95%CI 38–65),G9(14.5%,95%CI 7–23)and G1(14.2%,95%CI 7–23);while the most prevalent P types were P[4](54.1%,95%CI 41–67)and P[8](33%,95%CI 22–46).G2P[4]was the most frequent genotype combination after vaccine introduction.Effectiveness was 53%(95%CI 46–60)against infection,73%(95%CI,66–78)against hospitalisation and 74%(95%CI,68.0–78.0)against severe diarrhoea.Reductions in hospitalisations and mortality due to diarrhoea were observed in countries that adopted universal rotavirus vaccination.Conclusions:Rotavirus vaccines are effective in preventing rotavirus-diarrhoea in children in Latin America.The vaccines were associated with changes in genotype distribution.
基金funded by the Natural Science and Engineering Research Council (NSERC) of Canada (No. RGPIN-2014-04100)
文摘Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.
基金supported by the Ministry of Science and Innovation and the Spanish Research Agency through FEDER funds (PID2021-1261520B-100) (MICINN/AEI/FEDER, EU)CBM receives an institutional grant from the Fundación Ramón Areces, Spain。
文摘The word “senescence” comes from the Latin senescens, meaning “to begin to age”, and is characterized by a long-lasting but reversible block in proliferation, resulting from stress-induced cell cycle arrest of previously replication-competent cells.
文摘Latin America, a region with a population greater than 600000000 individuals, is well known due to its wide geographic, socio-cultural and economic heterogeneity. Access to health care remains as the main barrier that challenges routine screening, early diagnosis and proper treatment of hepatocellular carcinoma(HCC). Therefore, identification of population at risk, implementation of surveillance programs and access to curative treatments has been poorly obtained in the region. Different retrospective cohort studies from the region have shown flaws in the implementation process of routine surveillance and early HCC diagnosis. Furthermore, adherence to clinical practice guidelines recommendations assessed in two studies from Brazil and Argentina demonstrated that there is also room for improvement in this field, similarly than the one observed in Europe and the United States. In summary, Latin America shares difficulties in HCC decision-making processes similar to those from developed countries. However, a transversal limitation in the region is the poor access to health care with the consequent limitation to standard treatments for overall population. Specifically, universal health care access to the different World Health Organization levels is crucial, including improvement in research, education and continuous medical training in order to expand knowledge and generation of data promoting a continuous improvement in the care of HCC patients.
基金financially supported by the National Natural Science Foundation of China(Grant No.51278217)
文摘This paper presents an artificial neural network（ANN）-based response surface method that can be used to predict the failure probability of c-φslopes with spatially variable soil.In this method,the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model;the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties;and finally,an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables.The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability.As a result,the obtained approximate function can be used as an alternative to the specific analysis process in c-φslope reliability analyses.
