Some patients with severe aortic stenosis (AS), due to restrictive cardiac physiology, paradoxically have relatively low flow and low gradients across stenotic aortic valves despite preserved left ventricular (LV) sys...Some patients with severe aortic stenosis (AS), due to restrictive cardiac physiology, paradoxically have relatively low flow and low gradients across stenotic aortic valves despite preserved left ventricular (LV) systolic function. It results in symptoms and reduced quality of life and carries a high mortality. Whilst this form of severe AS, termed paradoxical low flow low gradient (pLFLG), is well reported, patients with this diagnosis experience inappropriate barriers to aortic valve replacement (AVR), the only efficacious treatment. We present the case of an 88-year-old female with 12 months of exertional dyspnoea on a background of hypothyroidism and hypercholesterolemia. Transthoracic echocardiogram (TTE) revealed LV hypertrophy, with a small LV cavity size and reduced stroke volume, yet normal systolic function. A heavily calcified aortic valve was identified with severe aortic stenosis, based on valve area, yet with incongruous mean transvalvular gradient of 25 mmHg (severe ≥ 50 mmHg). Following exclusion of other differential diagnoses, her symptoms were attributed to paradoxical LFLG severe AS. She was however declined definitive transcatheter aortic valve implantation (TAVI) due to her paradoxically low mean aortic gradient. Following further deterioration in her symptoms and supportive quantification of poor exercise performance, she was ultimately re-referred, accepted, and underwent TAVI. Following her AVR, the patient experiences significant improvement in both symptoms and quality of life after only one month. Paradoxical LFLG severe AS remains a well-documented yet under recognized disease. It carries high morbidity and mortality if untreated, yet is significantly less likely to be referred and accepted for intervention. With its prevalence expected to rise with an ageing population, this case serves as a timely reminder for clinicians to address the under recognition of important pathology.展开更多
Background Aortic valve replacement （AVR） improves survival in severe symptomatic aortic stenosis （AS）. Yet, in many patients with severe AS, the timing of AVR remains poorly defined. In particular, it is challeng...Background Aortic valve replacement （AVR） improves survival in severe symptomatic aortic stenosis （AS）. Yet, in many patients with severe AS, the timing of AVR remains poorly defined. In particular, it is challenging in patients with low mean pressure gradient （〈 40 mmHg） and severe AS （aortic valve area （AVA）≤1.0 cm^2） with preserved left ventricular （LV） ejection fraction.展开更多
Documenting the recovery of hydrologic functions following perturbations of a landscape/watershed is important to address issues associated with land use change and ecosystem restoration. High resolution LiDAR data fo...Documenting the recovery of hydrologic functions following perturbations of a landscape/watershed is important to address issues associated with land use change and ecosystem restoration. High resolution LiDAR data for the USDAForestServiceSanteeExperimentalForestin coastalSouth Carolina,USAwas used to delineate the remnant historical water management structures within the watersheds supporting bottomland hardwood forests that are typical of the re- gion. Hydrologic functions were altered during the early1700’s agricultural use period for rice cultivation, with changes to detention storage, impoundments, and runoff routing. Since late1800’s, the land was left to revert to forests, without direct intervention. The resultant bottomlands, while typical in terms of vegetative structure and composition, still have altered hydrologic pathways and functions due to the historical land use. Furthermore, an accurate estimate of the watershed drainage area (DA) contributing to stream flow is critical for reliable estimates of peak flow rate, runoff depth and coefficient, as well as water and chemical balance. Peak flow rate, a parameter widely used in design of channels and cross drainage structures, is calculated as a function of the DA and other parameters. However, in contrast with the upland watersheds, currently available topographic maps and digital elevation models (DEMs) used to estimate the DA are not adequate for flat, low-gradient Coastal Plain (LCP) landscape. In this paper we explore a case study of a 3rd order watershed (equivalent to 14-digit hydrologic unit code (HUC)) at headwaters of east branch of Cooper River draining to Charleston Harbor, SC to assess the drainage area and corresponding mean annual runoff coefficient based on various DEMs including LiDAR data. These analyses demonstrate a need for application of LiDAR-based DEMs together with field verification to improve the basis for assessments of hydrology, watershed drainage characteristics, and modeling in the LCP.展开更多
随着国家大力推进能源供给侧结构性改革,新能源装机容量不断提升,电力市场竞争愈加激烈。另一方面,全球煤炭市场的复杂多变,导致以煤炭为能量来源的发电企业成本上涨。燃煤发热量是衡量煤质的重要评价标准之一,也是采购煤炭最重要的依据...随着国家大力推进能源供给侧结构性改革,新能源装机容量不断提升,电力市场竞争愈加激烈。另一方面,全球煤炭市场的复杂多变,导致以煤炭为能量来源的发电企业成本上涨。燃煤发热量是衡量煤质的重要评价标准之一,也是采购煤炭最重要的依据,对燃煤发热量进行准确预测能够有效地控制电厂运行采购成本。为了实现燃煤发热量的高效预测,采用Pearson系数对相关变量进行特征选取,采用基于密度的噪点空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法对某电厂自备煤厂近2年1733条化验数据进行去噪,对去噪后数据进行谱聚类(Spectral Clustering,SC)分析。将分类后的子样本集采用极致梯度提升(Extreme Gradient Boosting,XGBoost)算法分别建立预测模型,并与最小二乘法回归(Ordinary Least Squares,OLS)、支持向量机(Support Vector Machines,SVM)模型进行性能比较。结果表明,基于XGBoost的电站燃煤发热量预测模型相较于其他算法准确性有明显提升,泛化能力更强。对经过SC算法分类后的燃煤分别建立预测模型能够进一步提高模型的精细化水平,为燃煤电站发热量预测提供一种可靠高效的方法。展开更多
文摘Some patients with severe aortic stenosis (AS), due to restrictive cardiac physiology, paradoxically have relatively low flow and low gradients across stenotic aortic valves despite preserved left ventricular (LV) systolic function. It results in symptoms and reduced quality of life and carries a high mortality. Whilst this form of severe AS, termed paradoxical low flow low gradient (pLFLG), is well reported, patients with this diagnosis experience inappropriate barriers to aortic valve replacement (AVR), the only efficacious treatment. We present the case of an 88-year-old female with 12 months of exertional dyspnoea on a background of hypothyroidism and hypercholesterolemia. Transthoracic echocardiogram (TTE) revealed LV hypertrophy, with a small LV cavity size and reduced stroke volume, yet normal systolic function. A heavily calcified aortic valve was identified with severe aortic stenosis, based on valve area, yet with incongruous mean transvalvular gradient of 25 mmHg (severe ≥ 50 mmHg). Following exclusion of other differential diagnoses, her symptoms were attributed to paradoxical LFLG severe AS. She was however declined definitive transcatheter aortic valve implantation (TAVI) due to her paradoxically low mean aortic gradient. Following further deterioration in her symptoms and supportive quantification of poor exercise performance, she was ultimately re-referred, accepted, and underwent TAVI. Following her AVR, the patient experiences significant improvement in both symptoms and quality of life after only one month. Paradoxical LFLG severe AS remains a well-documented yet under recognized disease. It carries high morbidity and mortality if untreated, yet is significantly less likely to be referred and accepted for intervention. With its prevalence expected to rise with an ageing population, this case serves as a timely reminder for clinicians to address the under recognition of important pathology.
文摘Background Aortic valve replacement （AVR） improves survival in severe symptomatic aortic stenosis （AS）. Yet, in many patients with severe AS, the timing of AVR remains poorly defined. In particular, it is challenging in patients with low mean pressure gradient （〈 40 mmHg） and severe AS （aortic valve area （AVA）≤1.0 cm^2） with preserved left ventricular （LV） ejection fraction.
文摘Documenting the recovery of hydrologic functions following perturbations of a landscape/watershed is important to address issues associated with land use change and ecosystem restoration. High resolution LiDAR data for the USDAForestServiceSanteeExperimentalForestin coastalSouth Carolina,USAwas used to delineate the remnant historical water management structures within the watersheds supporting bottomland hardwood forests that are typical of the re- gion. Hydrologic functions were altered during the early1700’s agricultural use period for rice cultivation, with changes to detention storage, impoundments, and runoff routing. Since late1800’s, the land was left to revert to forests, without direct intervention. The resultant bottomlands, while typical in terms of vegetative structure and composition, still have altered hydrologic pathways and functions due to the historical land use. Furthermore, an accurate estimate of the watershed drainage area (DA) contributing to stream flow is critical for reliable estimates of peak flow rate, runoff depth and coefficient, as well as water and chemical balance. Peak flow rate, a parameter widely used in design of channels and cross drainage structures, is calculated as a function of the DA and other parameters. However, in contrast with the upland watersheds, currently available topographic maps and digital elevation models (DEMs) used to estimate the DA are not adequate for flat, low-gradient Coastal Plain (LCP) landscape. In this paper we explore a case study of a 3rd order watershed (equivalent to 14-digit hydrologic unit code (HUC)) at headwaters of east branch of Cooper River draining to Charleston Harbor, SC to assess the drainage area and corresponding mean annual runoff coefficient based on various DEMs including LiDAR data. These analyses demonstrate a need for application of LiDAR-based DEMs together with field verification to improve the basis for assessments of hydrology, watershed drainage characteristics, and modeling in the LCP.
文摘随着国家大力推进能源供给侧结构性改革,新能源装机容量不断提升,电力市场竞争愈加激烈。另一方面,全球煤炭市场的复杂多变,导致以煤炭为能量来源的发电企业成本上涨。燃煤发热量是衡量煤质的重要评价标准之一,也是采购煤炭最重要的依据,对燃煤发热量进行准确预测能够有效地控制电厂运行采购成本。为了实现燃煤发热量的高效预测,采用Pearson系数对相关变量进行特征选取,采用基于密度的噪点空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法对某电厂自备煤厂近2年1733条化验数据进行去噪,对去噪后数据进行谱聚类(Spectral Clustering,SC)分析。将分类后的子样本集采用极致梯度提升(Extreme Gradient Boosting,XGBoost)算法分别建立预测模型,并与最小二乘法回归(Ordinary Least Squares,OLS)、支持向量机(Support Vector Machines,SVM)模型进行性能比较。结果表明,基于XGBoost的电站燃煤发热量预测模型相较于其他算法准确性有明显提升,泛化能力更强。对经过SC算法分类后的燃煤分别建立预测模型能够进一步提高模型的精细化水平,为燃煤电站发热量预测提供一种可靠高效的方法。
