Salicylic acid (SA) is a vital plant growth regulator providing promising role in plant development and adopts defense mechanism to abiotic stresses. Salinity is the most limiting abiotic factor for plant development ...Salicylic acid (SA) is a vital plant growth regulator providing promising role in plant development and adopts defense mechanism to abiotic stresses. Salinity is the most limiting abiotic factor for plant development and growth changes in watermelon by producing reactive oxygen species and ultimately oxidative stress. The present study was aimed to investigate the mechanism involved in salt stress alleviation in watermelon (Citrullus lanatus Thanb. Mavs.) through the foliar application of salicylic acid. Watermelon cv. Charleston Gray was grown under moderate saline regime of 3 ds·m-1 NaCl and sprayed with salicylic acid with four level (0.5, 1.0, 2.5 and 5.0 mmol/L) compared along with control. SA @ 5.0 mmol/L was found to be very effective in mitigation of salt stress. SA was found to be very effective in alleviation of salinity stress by produced antioxidants and acted as osmo-regulator.展开更多
For horticultural crops and especially for vegetables, salinity is dilemma. It is the most limiting factor for plant growth and development by producing reactive oxygen species and ultimately oxidative stress. In the ...For horticultural crops and especially for vegetables, salinity is dilemma. It is the most limiting factor for plant growth and development by producing reactive oxygen species and ultimately oxidative stress. In the present study, the screening of watermelon (Citrullus lanatus Thanb. Mavs.) Cultivars was observed for salt tolerance. Four salinity levels (1.5, 3, 4.5, and 6 dS·m-1 NaCl) and six cultivars (Crimson, Charleston Gray, Anarkali, Chairman, Sugar Baby and Champion) tested for screening. It was observed that all morphological attributes and ionic contents were severely affected. But it was revealed by statistical analysis that Charleston Gray was affected least while Champion was most salt sensitive cultivar due to oxidative stress and ionic toxicity. It is concluded that different genotypes under consideration vary in their ability to tolerate salt stress.展开更多
Objective To report our preliminary clinical experience and outcomes of uro-oncology procedures conducted utilizing the SSI Mantra^(TM)surgical robotic system.Methods Data of consecutive patients who underwent various...Objective To report our preliminary clinical experience and outcomes of uro-oncology procedures conducted utilizing the SSI Mantra^(TM)surgical robotic system.Methods Data of consecutive patients who underwent various robot-assisted uro-oncology procedures using the SSI Mantra^(TM)surgical robotic system at our institution between July 2022 and September 2023 were recorded.The specific surgical configurations employed with the SSI Mantra^(TM)for these procedures were duly noted.We assessed the feasibility of these procedures with this novel surgical robotic system and report the outcomes.Results A total of 156 patients were operated with the SSI Mantra^(TM)surgical robotic system.The spectrum of procedures performed comprised robot-assisted laparoscopic radical prostatectomy with bilateral extended pelvic lymph node dissection(n=77),robot-assisted radical cystectomy with bilateral extended pelvic lymph node dissection with extracorporeal urinary diversion(n=39),robot-assisted radical nephrectomy(n=32),robot-assisted partial nephrectomy(n=6),robot-assisted radical nephroureterectomy with bladder cuff excision(n=1),and bilateral robot-assisted video endoscopic inguinal lymph node dissection(n=1).One robot-assisted laparoscopic radical prostatectomy had to be converted to open in view of system malfunction.However,no system-related intraoperative complications or injuries were encountered.Conclusion The SSI Mantra^(TM)surgical robotic system demonstrates significant promise as an innovative robotic platform.In this single-center experience,we have demonstrated the feasibility of a diverse array of surgical procedures using this platform.Further research,involving a larger cohort of patients,is imperative to refine the operative techniques and comprehensively understand the perioperative outcomes of the SSI Mantra^(TM)surgical robotic system,particularly in comparison to other robotic surgical platforms.展开更多
Silicon(Si)has emerged as a potent anode material for lithium-ion batteries(LIBs),but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation,leading to material...Silicon(Si)has emerged as a potent anode material for lithium-ion batteries(LIBs),but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation,leading to material pulverization and capacity degradation.Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance,yet still grapples with issues like pulverization,unstable solid electrolyte interface(SEI)growth,and interparticle resistance.This review delves into innovative strategies for optimizing Si anodes’electrochemical performance via structural engineering,focusing on the synthesis of Si/C composites,engineering multidimensional nanostructures,and applying non-carbonaceous coatings.Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li^(+)transport,thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency.We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss.Our review uniquely provides a detailed examination of these strategies in real-world applications,moving beyond theoretical discussions.It offers a critical analysis of these approaches in terms of performance enhancement,scalability,and commercial feasibility.In conclusion,this review presents a comprehensive view and a forward-looking perspective on designing robust,high-performance Si-based anodes the next generation of LIBs.展开更多
Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteris...Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.展开更多
Inefficient nitrogen(N)utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers,redundant plant growth,greenhouse gases,long-lasting toxicity in ecosystem,and even...Inefficient nitrogen(N)utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers,redundant plant growth,greenhouse gases,long-lasting toxicity in ecosystem,and even effect on human health,indicating the importance to optimize N applications in cropping systems.Here,we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions.Powered by drone-based aerial phenotyping and the AirMeasurer platform,we first quantified 6 N response-related traits as targets using plot-based morphological,spectral,and textural signals collected from 54 winter wheat varieties.Then,we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season,which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes(i.e.,phenotypic changes)during N response.After that,we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores(NECS),based on which we classified the varieties into 4 N responsiveness(i.e.,N-dependent yield increase)groups.The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies.Finally,we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes,helping us explore genetic components underlying N responsiveness in wheat.In summary,we believe that our work demonstrates valuable advances in N response-related plant research,which could have major implications for improving N sustainability in wheat breeding and production.展开更多
文摘Salicylic acid (SA) is a vital plant growth regulator providing promising role in plant development and adopts defense mechanism to abiotic stresses. Salinity is the most limiting abiotic factor for plant development and growth changes in watermelon by producing reactive oxygen species and ultimately oxidative stress. The present study was aimed to investigate the mechanism involved in salt stress alleviation in watermelon (Citrullus lanatus Thanb. Mavs.) through the foliar application of salicylic acid. Watermelon cv. Charleston Gray was grown under moderate saline regime of 3 ds·m-1 NaCl and sprayed with salicylic acid with four level (0.5, 1.0, 2.5 and 5.0 mmol/L) compared along with control. SA @ 5.0 mmol/L was found to be very effective in mitigation of salt stress. SA was found to be very effective in alleviation of salinity stress by produced antioxidants and acted as osmo-regulator.
文摘For horticultural crops and especially for vegetables, salinity is dilemma. It is the most limiting factor for plant growth and development by producing reactive oxygen species and ultimately oxidative stress. In the present study, the screening of watermelon (Citrullus lanatus Thanb. Mavs.) Cultivars was observed for salt tolerance. Four salinity levels (1.5, 3, 4.5, and 6 dS·m-1 NaCl) and six cultivars (Crimson, Charleston Gray, Anarkali, Chairman, Sugar Baby and Champion) tested for screening. It was observed that all morphological attributes and ionic contents were severely affected. But it was revealed by statistical analysis that Charleston Gray was affected least while Champion was most salt sensitive cultivar due to oxidative stress and ionic toxicity. It is concluded that different genotypes under consideration vary in their ability to tolerate salt stress.
文摘Objective To report our preliminary clinical experience and outcomes of uro-oncology procedures conducted utilizing the SSI Mantra^(TM)surgical robotic system.Methods Data of consecutive patients who underwent various robot-assisted uro-oncology procedures using the SSI Mantra^(TM)surgical robotic system at our institution between July 2022 and September 2023 were recorded.The specific surgical configurations employed with the SSI Mantra^(TM)for these procedures were duly noted.We assessed the feasibility of these procedures with this novel surgical robotic system and report the outcomes.Results A total of 156 patients were operated with the SSI Mantra^(TM)surgical robotic system.The spectrum of procedures performed comprised robot-assisted laparoscopic radical prostatectomy with bilateral extended pelvic lymph node dissection(n=77),robot-assisted radical cystectomy with bilateral extended pelvic lymph node dissection with extracorporeal urinary diversion(n=39),robot-assisted radical nephrectomy(n=32),robot-assisted partial nephrectomy(n=6),robot-assisted radical nephroureterectomy with bladder cuff excision(n=1),and bilateral robot-assisted video endoscopic inguinal lymph node dissection(n=1).One robot-assisted laparoscopic radical prostatectomy had to be converted to open in view of system malfunction.However,no system-related intraoperative complications or injuries were encountered.Conclusion The SSI Mantra^(TM)surgical robotic system demonstrates significant promise as an innovative robotic platform.In this single-center experience,we have demonstrated the feasibility of a diverse array of surgical procedures using this platform.Further research,involving a larger cohort of patients,is imperative to refine the operative techniques and comprehensively understand the perioperative outcomes of the SSI Mantra^(TM)surgical robotic system,particularly in comparison to other robotic surgical platforms.
基金financially supported by the Jiangsu Distinguished Professors Project(No.1711510024)the funding for Scientific Research Startup of Jiangsu University(Nos.4111510015,19JDG044)+3 种基金the Jiangsu Provincial Program for High-Level Innovative and Entrepreneurial Talents Introductionthe National Natural Science Foundation of China(No.22008091)Natural Science Foundation of Guangdong Province(2023A1515010894)the Open Project of Luzhou Key Laboratory of Fine Chemical Application Technology(HYJH-2302-A).
文摘Silicon(Si)has emerged as a potent anode material for lithium-ion batteries(LIBs),but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation,leading to material pulverization and capacity degradation.Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance,yet still grapples with issues like pulverization,unstable solid electrolyte interface(SEI)growth,and interparticle resistance.This review delves into innovative strategies for optimizing Si anodes’electrochemical performance via structural engineering,focusing on the synthesis of Si/C composites,engineering multidimensional nanostructures,and applying non-carbonaceous coatings.Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li^(+)transport,thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency.We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss.Our review uniquely provides a detailed examination of these strategies in real-world applications,moving beyond theoretical discussions.It offers a critical analysis of these approaches in terms of performance enhancement,scalability,and commercial feasibility.In conclusion,this review presents a comprehensive view and a forward-looking perspective on designing robust,high-performance Si-based anodes the next generation of LIBs.
文摘Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.
基金supported by the National Natural Science Foundation of China(32070400 to J.Z.)The drone-based phenotyping,field experiments,yield,and N measures were also supported by the Key Project of Modern Agriculture of Jiangsu Province(BE2019383)+1 种基金J.Z.,R.J.,and G.Deakin were partially supported by the Allan&Gill Gray Philanthropies'sustainable productivity for crops improvement(G118688 to the University of Cambridge and Q-20-0370 to NIAB)J.Z.and R.J.were supported by the One CGIAR's Seed Equal Initiative(5507-CGIA-07 to J.Z.),as well as the United Kingdom Research and Innovation's(UKRI)Biotechnology and Biological Sciences Research Council's(BBSRC)International Partnership Grant(BB/X511882/1).
文摘Inefficient nitrogen(N)utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers,redundant plant growth,greenhouse gases,long-lasting toxicity in ecosystem,and even effect on human health,indicating the importance to optimize N applications in cropping systems.Here,we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions.Powered by drone-based aerial phenotyping and the AirMeasurer platform,we first quantified 6 N response-related traits as targets using plot-based morphological,spectral,and textural signals collected from 54 winter wheat varieties.Then,we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season,which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes(i.e.,phenotypic changes)during N response.After that,we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores(NECS),based on which we classified the varieties into 4 N responsiveness(i.e.,N-dependent yield increase)groups.The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies.Finally,we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes,helping us explore genetic components underlying N responsiveness in wheat.In summary,we believe that our work demonstrates valuable advances in N response-related plant research,which could have major implications for improving N sustainability in wheat breeding and production.
