Objective:Pathologic complete response(pCR)following neoadjuvant therapy(NAT)for gastric cancer(GC)is rare but associated with a favorable prognosis.This study aims to reassess the optimal response population(ORP)foll...Objective:Pathologic complete response(pCR)following neoadjuvant therapy(NAT)for gastric cancer(GC)is rare but associated with a favorable prognosis.This study aims to reassess the optimal response population(ORP)following NAT by evaluating the prognostic outcomes associated with various T and N stages,utilizing multicenter data from China.Methods:Patients who underwent NAT following radical gastrectomy at 10 tertiary hospitals in China between2008 and 2021 were included.The ORP was introduced to explore the disease-free survival(DFS),overall survival(OS),recurrence patterns,and influencing factors following propensity score matching(PSM).Results:A total of 1,076 patients were enrolled in this study(median follow-up period:60 months).We defined ORP as a pCR or tumor infiltration of the mucosal or submucosal layer without lymph node metastasis(pCR or yp T1N0)after NAT.The ORP group comprised 136 patients(12.6%),while the non-ORP group comprised 940patients(87.4%).After applying a 1:4 PSM,we obtained an ORP group of 136 patients and non-ORP group of 544patients.Survival analysis demonstrated that both the 3-year OS(before PSM:89.0%vs.55.0%,P<0.001;after PSM:89.0%vs.55.4%,P<0.001)and DFS(before PSM:85.8%vs.49.7%,P<0.001;after PSM:85.8%vs.50.6%,P<0.001)were significantly superior in the ORP group compared to that in the non-ORP group.Remarkably,adjuvant chemotherapy did not impact the prognosis of patients in the ORP group(3-year OS:89.0%vs.89.7%,P=0.988;3-year DFS:84.9%vs.89.7%,P=0.700).Conclusions:This study reevaluates patients with ORP following NAT,providing a more comprehensive and accurate depiction of the potential beneficiary group and survival outcomes in patients with locally advanced GC.展开更多
Small rodents in general and the multimammate rat Apodemus agrarius in particular, damage crops and cause major economic losses in China. Therefore, accurate predic- tions of the population size of A. agrarius and an ...Small rodents in general and the multimammate rat Apodemus agrarius in particular, damage crops and cause major economic losses in China. Therefore, accurate predic- tions of the population size of A. agrarius and an efficient control strategy are urgently needed. We developed a population dynamics model by applying a Leslie matrix method, and a capture model based on optimal harvesting theory for A. agrarius. Our models were parametrized using demographic estimates from a capture-mark-recapture （CMR） study conducted on the Qinshui Forest Farm in Northwestern China. The population dynamics model incorporated 12 equally balanced age groups and included immigra- tion and emigration parameters. The model was evaluated by assessing the predictions for four years based on the known starting population in 2004 from the 2004-2007 CMR data. The capture model incorporated two functional age categories （juvenile and adult） and used density-dependent and density-independent factors. The models were used to assess the effect of rodent control measures between 2004 and 2023 on population dynamics and the resulting numbers of rats. Three control measures affecting survival rates were considered. We found that the predicted population dynamics of A. agrarius between 2004 and 2007 compared favorably with the observed population dynamics. The models predicted that the population sizes of A. agrarius in the period between 2004 and 2023 under the control measure applied in August 2004 were very similar to the optimal population sizes, and no significant difference was found between the two pop- ulation sizes. We recommend using the population dynamics and capture models based on CMR-estimated demographic schedules for rodent, provided these data are available.The models that we have developed have the potential to play an important role in pre- dicting the effects of rodent management and in evaluating different control strategies.展开更多
Genomic selection,the application of genomic prediction(GP)models to select candidate individuals,has significantly advanced in the past two decades,effectively accelerating genetic gains in plant breeding.This articl...Genomic selection,the application of genomic prediction(GP)models to select candidate individuals,has significantly advanced in the past two decades,effectively accelerating genetic gains in plant breeding.This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period.We delved into the pivotal roles of training population size and genetic diversity,and their relationship with the breeding population,in determining GP accuracy.Special emphasis was placed on optimizing training population size.We explored its benefits and the associated diminishing returns beyond an optimum size.This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms.The density and distribution of single-nucleotide polymorphisms,level of linkage disequilibrium,genetic complexity,trait heritability,statistical machine-learning methods,and non-additive effects are the other vital factors.Using wheat,maize,and potato as examples,we summarize the effect of these factors on the accuracy of GP for various traits.The search for high accuracy in GP—theoretically reaching one when using the Pearson’s correlation as a metric—is an active research area as yet far from optimal for various traits.We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets,effective training population optimization methods and support from other omics approaches(transcriptomics,metabolomics and proteomics)coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy,making genomic selection an effective tool in plant breeding.展开更多
Extreme nutrient solution temperature significantly affects photosynthetic characteristics of hydroponic vegetables and gives rise to slow plant growth.In this study,a method was proposed to obtain the suitable nutrie...Extreme nutrient solution temperature significantly affects photosynthetic characteristics of hydroponic vegetables and gives rise to slow plant growth.In this study,a method was proposed to obtain the suitable nutrient solution temperature range of hydroponic crops.Nested experiments of net photosynthetic rates were designed.The experiments considered the impact of nutrient solution temperatures,air temperatures,photon flux densities,and CO_(2) concentrations.Then we established a prediction model of photosynthetic rate based on a regression support vector machine.The results have shown that the coefficient of determination between the measured values and the predicted values of photosynthetic rate is 0.982,and the root mean square error is 0.990μmol/m2·s.Taking the net photosynthetic rate prediction model as the objective function,the maximum photosynthetic rate could be found using multiple population genetic algorithms,and then the nutrient solution temperature response curve could be created.According to the U-chord curvature theory,the suitable nutrient solution temperature range was calculated.After optimization by the multi-population genetic algorithm,the coefficient of determination between measured values and optimized values of maximum photosynthetic rate was 0.989 and the mean square error was 0.003.An analysis of the calculation based on the theory of U-chord curvature indicated that the suitable nutrient solution temperature range to grow hydroponic lettuce is 20.04°C-26.32°C.The proposed method provides a solid foundation to accurately acquire the suitable nutrient solution temperature range for a crop grown in hydroponics.展开更多
基金supported by the construction funds for“high-level hospitals and clinical specialties”of Fujian Province(No.[2021]76)。
文摘Objective:Pathologic complete response(pCR)following neoadjuvant therapy(NAT)for gastric cancer(GC)is rare but associated with a favorable prognosis.This study aims to reassess the optimal response population(ORP)following NAT by evaluating the prognostic outcomes associated with various T and N stages,utilizing multicenter data from China.Methods:Patients who underwent NAT following radical gastrectomy at 10 tertiary hospitals in China between2008 and 2021 were included.The ORP was introduced to explore the disease-free survival(DFS),overall survival(OS),recurrence patterns,and influencing factors following propensity score matching(PSM).Results:A total of 1,076 patients were enrolled in this study(median follow-up period:60 months).We defined ORP as a pCR or tumor infiltration of the mucosal or submucosal layer without lymph node metastasis(pCR or yp T1N0)after NAT.The ORP group comprised 136 patients(12.6%),while the non-ORP group comprised 940patients(87.4%).After applying a 1:4 PSM,we obtained an ORP group of 136 patients and non-ORP group of 544patients.Survival analysis demonstrated that both the 3-year OS(before PSM:89.0%vs.55.0%,P<0.001;after PSM:89.0%vs.55.4%,P<0.001)and DFS(before PSM:85.8%vs.49.7%,P<0.001;after PSM:85.8%vs.50.6%,P<0.001)were significantly superior in the ORP group compared to that in the non-ORP group.Remarkably,adjuvant chemotherapy did not impact the prognosis of patients in the ORP group(3-year OS:89.0%vs.89.7%,P=0.988;3-year DFS:84.9%vs.89.7%,P=0.700).Conclusions:This study reevaluates patients with ORP following NAT,providing a more comprehensive and accurate depiction of the potential beneficiary group and survival outcomes in patients with locally advanced GC.
文摘Small rodents in general and the multimammate rat Apodemus agrarius in particular, damage crops and cause major economic losses in China. Therefore, accurate predic- tions of the population size of A. agrarius and an efficient control strategy are urgently needed. We developed a population dynamics model by applying a Leslie matrix method, and a capture model based on optimal harvesting theory for A. agrarius. Our models were parametrized using demographic estimates from a capture-mark-recapture （CMR） study conducted on the Qinshui Forest Farm in Northwestern China. The population dynamics model incorporated 12 equally balanced age groups and included immigra- tion and emigration parameters. The model was evaluated by assessing the predictions for four years based on the known starting population in 2004 from the 2004-2007 CMR data. The capture model incorporated two functional age categories （juvenile and adult） and used density-dependent and density-independent factors. The models were used to assess the effect of rodent control measures between 2004 and 2023 on population dynamics and the resulting numbers of rats. Three control measures affecting survival rates were considered. We found that the predicted population dynamics of A. agrarius between 2004 and 2007 compared favorably with the observed population dynamics. The models predicted that the population sizes of A. agrarius in the period between 2004 and 2023 under the control measure applied in August 2004 were very similar to the optimal population sizes, and no significant difference was found between the two pop- ulation sizes. We recommend using the population dynamics and capture models based on CMR-estimated demographic schedules for rodent, provided these data are available.The models that we have developed have the potential to play an important role in pre- dicting the effects of rodent management and in evaluating different control strategies.
基金supported by SLU Grogrund(#SLU-LTV.2020.1.1.1-654)an Einar and Inga Nilsson Foundation grant.J.I.y.S.was supported by grant PID2021-123718OB-I00+4 种基金funded by MCIN/AEI/10.13039/501100011033by“ERDF A way of making Europe,”CEX2020-000999-S.R.R.V.supported by Novo Nordisk Fonden(0074727)SLU’s Centre for Biological ControlIn addition,J.I.y.S.and J.F.-G.were supported by the Beatriz Galindo Program BEAGAL 18/00115.
文摘Genomic selection,the application of genomic prediction(GP)models to select candidate individuals,has significantly advanced in the past two decades,effectively accelerating genetic gains in plant breeding.This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period.We delved into the pivotal roles of training population size and genetic diversity,and their relationship with the breeding population,in determining GP accuracy.Special emphasis was placed on optimizing training population size.We explored its benefits and the associated diminishing returns beyond an optimum size.This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms.The density and distribution of single-nucleotide polymorphisms,level of linkage disequilibrium,genetic complexity,trait heritability,statistical machine-learning methods,and non-additive effects are the other vital factors.Using wheat,maize,and potato as examples,we summarize the effect of these factors on the accuracy of GP for various traits.The search for high accuracy in GP—theoretically reaching one when using the Pearson’s correlation as a metric—is an active research area as yet far from optimal for various traits.We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets,effective training population optimization methods and support from other omics approaches(transcriptomics,metabolomics and proteomics)coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy,making genomic selection an effective tool in plant breeding.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFD1100602)the Shaanxi Key Research and Development Program(Grant No.2023-ZDLNY-66)the Shaanxi Provincial Science and Technology Program(Grant No.Z2024-ZYFS-0043).
文摘Extreme nutrient solution temperature significantly affects photosynthetic characteristics of hydroponic vegetables and gives rise to slow plant growth.In this study,a method was proposed to obtain the suitable nutrient solution temperature range of hydroponic crops.Nested experiments of net photosynthetic rates were designed.The experiments considered the impact of nutrient solution temperatures,air temperatures,photon flux densities,and CO_(2) concentrations.Then we established a prediction model of photosynthetic rate based on a regression support vector machine.The results have shown that the coefficient of determination between the measured values and the predicted values of photosynthetic rate is 0.982,and the root mean square error is 0.990μmol/m2·s.Taking the net photosynthetic rate prediction model as the objective function,the maximum photosynthetic rate could be found using multiple population genetic algorithms,and then the nutrient solution temperature response curve could be created.According to the U-chord curvature theory,the suitable nutrient solution temperature range was calculated.After optimization by the multi-population genetic algorithm,the coefficient of determination between measured values and optimized values of maximum photosynthetic rate was 0.989 and the mean square error was 0.003.An analysis of the calculation based on the theory of U-chord curvature indicated that the suitable nutrient solution temperature range to grow hydroponic lettuce is 20.04°C-26.32°C.The proposed method provides a solid foundation to accurately acquire the suitable nutrient solution temperature range for a crop grown in hydroponics.
