AIM: To validate a non-invasive method to detect gastric mucosal atrophy in a Chilean population with high prevalence of gastric cancer and a poor survival rate. METHODS: We first determined the optimal cut-off level ...AIM: To validate a non-invasive method to detect gastric mucosal atrophy in a Chilean population with high prevalence of gastric cancer and a poor survival rate. METHODS: We first determined the optimal cut-off level of serum pepsinogen (PG)-1, PG-1/PG-2 ratio and 17-gastrin in 31 voluntary symptomatic patients (mean age: 66.1 years), of them 61% had histologically confirmed gastric atrophy. Then, in a population-based sample of 536 healthy individuals (209 residents in counties with higher relative risk and 327 residents in counties with lower relative risk for gastric cancer), we measured serum anti-H pylori antibodies, PG and 17-gastrin and estimated their risk of gastric cancer. RESULTS: We found that serum PG-1 < 61.5 μg/L, PG-1/PG-2 ratio < 2.2 and 17-gastrin > 13.3 pmol/L had a high specificity (91%-100%) and a fair sensitivity (56%-78%) to detect corpus-predominant atrophy. Based on low serum PG-1 and PG-1/PG-2 ratio together as diagnostic criteria, 12.5% of the asymptomatic subjects had corpus-predominant atrophy (0% of those under 25 years and 20.2% over 65 years old). The frequency of gastric atrophy was similar (12% vs 13%) but H pylori infection rate was slightly higher (77% vs 71%) in the high-risk compared to the low-risk counties. Based on their estimated gastric cancer risk, individuals were classified as: low-risk group (no H pylori infection and no atrophy; n = 115; 21.4%); moderate-risk group(H pylori infection but no atrophy; n = 354, 66.0%); and high-risk group (gastric atrophy, with or without H pylori infection; n = 67, 12.5%). The high-risk group was significantly older (mean age: 61.9 ± 13.3 years), more frequently men and less educated as compared with the low-risk group. CONCLUSION: We propose to concentrate on an upper gastrointestinal endoscopy for detection of early gastric cancer in the high-risk group. This intervention model could improve the poor prognosis of gastric cancer in Chile.展开更多
AIM: To optimize diagnosis and treatment guidelines for this geographic region, a panel of gastroenterologists, epidemiologists, and basic scientists carried out a structured evaluation of available literature.
Explosion models based on Finite Element Analysis(FEA)can be used to simulate how a warhead fragments.However their execution times are extensive.Active protection systems need to make very fast predictions,before a f...Explosion models based on Finite Element Analysis(FEA)can be used to simulate how a warhead fragments.However their execution times are extensive.Active protection systems need to make very fast predictions,before a fast attacking weapon hits the target.Fast execution times are also needed in real time simulations where the impact of many different computer models is being assessed.Hence,FEA explosion models are not appropriate for these real-time systems.The research presented in this paper delivers a fast simulation model based on Mott’s equation that calculates the number and weight of fragments created by an explosion.In addition,the size and shape of fragments,unavailable in Mott’s equation,are calculated using photographic evidence and a distribution of a fragment’s length to its width.The model also identifies the origin of fragments on the warhead’s casing.The results are verified against experimental data and a fast execution time is achieved using uncomplicated simulation steps.The developed model then can be made available for real-time simulation and fast computation.展开更多
In this study,we present a collection of local models,termed geographically weighted(GW)models,which can be found within the GWmodel R package.A GW model suits situations when spatial data are poorly described by the ...In this study,we present a collection of local models,termed geographically weighted(GW)models,which can be found within the GWmodel R package.A GW model suits situations when spatial data are poorly described by the global form,and for some regions the localized fit provides a better description.The approach uses a moving window weighting technique,where a collection of local models are estimated at target locations.Commonly,model parameters or outputs are mapped so that the nature of spatial heterogeneity can be explored and assessed.In particular,we present case studies using:(i)GW summary statistics and a GW principal components analysis;(ii)advanced GW regression fits and diagnostics;(iii)associated Monte Carlo significance tests for non-stationarity;(iv)a GW discriminant analysis;and(v)enhanced kernel bandwidth selection procedures.General Election data-sets from the Republic of Ireland and US are used for demonstration.This study is designed to complement a companion GWmodel study,which focuses on basic and robust GW models.展开更多
As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalen...As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.展开更多
文摘AIM: To validate a non-invasive method to detect gastric mucosal atrophy in a Chilean population with high prevalence of gastric cancer and a poor survival rate. METHODS: We first determined the optimal cut-off level of serum pepsinogen (PG)-1, PG-1/PG-2 ratio and 17-gastrin in 31 voluntary symptomatic patients (mean age: 66.1 years), of them 61% had histologically confirmed gastric atrophy. Then, in a population-based sample of 536 healthy individuals (209 residents in counties with higher relative risk and 327 residents in counties with lower relative risk for gastric cancer), we measured serum anti-H pylori antibodies, PG and 17-gastrin and estimated their risk of gastric cancer. RESULTS: We found that serum PG-1 < 61.5 μg/L, PG-1/PG-2 ratio < 2.2 and 17-gastrin > 13.3 pmol/L had a high specificity (91%-100%) and a fair sensitivity (56%-78%) to detect corpus-predominant atrophy. Based on low serum PG-1 and PG-1/PG-2 ratio together as diagnostic criteria, 12.5% of the asymptomatic subjects had corpus-predominant atrophy (0% of those under 25 years and 20.2% over 65 years old). The frequency of gastric atrophy was similar (12% vs 13%) but H pylori infection rate was slightly higher (77% vs 71%) in the high-risk compared to the low-risk counties. Based on their estimated gastric cancer risk, individuals were classified as: low-risk group (no H pylori infection and no atrophy; n = 115; 21.4%); moderate-risk group(H pylori infection but no atrophy; n = 354, 66.0%); and high-risk group (gastric atrophy, with or without H pylori infection; n = 67, 12.5%). The high-risk group was significantly older (mean age: 61.9 ± 13.3 years), more frequently men and less educated as compared with the low-risk group. CONCLUSION: We propose to concentrate on an upper gastrointestinal endoscopy for detection of early gastric cancer in the high-risk group. This intervention model could improve the poor prognosis of gastric cancer in Chile.
文摘AIM: To optimize diagnosis and treatment guidelines for this geographic region, a panel of gastroenterologists, epidemiologists, and basic scientists carried out a structured evaluation of available literature.
文摘Explosion models based on Finite Element Analysis(FEA)can be used to simulate how a warhead fragments.However their execution times are extensive.Active protection systems need to make very fast predictions,before a fast attacking weapon hits the target.Fast execution times are also needed in real time simulations where the impact of many different computer models is being assessed.Hence,FEA explosion models are not appropriate for these real-time systems.The research presented in this paper delivers a fast simulation model based on Mott’s equation that calculates the number and weight of fragments created by an explosion.In addition,the size and shape of fragments,unavailable in Mott’s equation,are calculated using photographic evidence and a distribution of a fragment’s length to its width.The model also identifies the origin of fragments on the warhead’s casing.The results are verified against experimental data and a fast execution time is achieved using uncomplicated simulation steps.The developed model then can be made available for real-time simulation and fast computation.
基金presented in this paper was funded by a Strategic Research Cluster grant(07/SRC/I1168)by Science Foundation Ireland under the National Development Plan.
文摘In this study,we present a collection of local models,termed geographically weighted(GW)models,which can be found within the GWmodel R package.A GW model suits situations when spatial data are poorly described by the global form,and for some regions the localized fit provides a better description.The approach uses a moving window weighting technique,where a collection of local models are estimated at target locations.Commonly,model parameters or outputs are mapped so that the nature of spatial heterogeneity can be explored and assessed.In particular,we present case studies using:(i)GW summary statistics and a GW principal components analysis;(ii)advanced GW regression fits and diagnostics;(iii)associated Monte Carlo significance tests for non-stationarity;(iv)a GW discriminant analysis;and(v)enhanced kernel bandwidth selection procedures.General Election data-sets from the Republic of Ireland and US are used for demonstration.This study is designed to complement a companion GWmodel study,which focuses on basic and robust GW models.
基金supported by National Key Research and Development Program of China[grant num-ber 2021YFB3900904]the National Natural Science Foundation of China[grant numbers 42071368,U2033216,41871287].
文摘As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.
