Fast and nondestructive approaches of measuring plant species diversity have been a subject of excessive scientific curiosity and disquiet to environmentalists and field ecologists worldwide. In this study, we measured the hyperspectral reflectances and plant species diversity indices at a fine scale (0.8 meter) in central Hunshandak Sandland of Inner Mongolia, China. The first-order derivative value (FD) at each waveband and 37 hyperspectral indices were used to assess plant species diversity. Results demonstrated that the stepwise linear regression of FD can accurately estimate the Simpson (R2 = 0.83), Pielou (R2 = 0.87) and Shannon-Wiener index (R2 = 0.88). Stepwise linear regression of FD (R2 = 0.81, R2 = 0.82) and spectral vegetation indices (R2 = 0.51, R2 = 0.58) significantly predicted the Margalef and Gleason index. It was proposed that the Simpson, Pielou and Shannon-Wiener indices, which are widely used as plant species diversity indicators, can be precisely estimated through hyperspectral indices at a fine scale. This research promotes the development of methods for assessment of plant diversity using hyperspectral data.
Gastric carcinoma is one of the most lethal malignancy at present with leading cause of cancer-related deaths worldwide. Aquaporins (AQPs) are a family of small, integral membrane proteins, which have been evidenced to play a crucial role in cell migration and proliferation of different cancer cells including gastric cancers. However, the aberrant expression of specific AQPs and its correlation to detect predictive and prognostic significance in gastric cancer remains elusive. In the present study, we comprehensively explored immunohistochemistry based map of protein expression profiles in normal tissues, cancer and cell lines from publicly available Human Protein Atlas (HPA) database. Moreover, to improve our understanding of general gastric biology and guide to find novel predictive prognostic gastric cancer biomarker, we also retrieved ‘The Kaplan–Meier plotter’ (KM plotter) online database with specific AQPs mRNA to overall survival (OS) in different clinicopathological features. We revealed that ubiquitous expression of AQPs protein can be effective tools to generate gastric cancer biomarker. Furthermore, high level AQP3, AQP9, and AQP11 mRNA expression were correlated with better OS in all gastric patients, whereas AQP0, AQP1, AQP4, AQP5, AQP6, AQP8, and AQP10 mRNA expression were associated with poor OS. With regard to the clinicopathological features including Laurens classification, clinical stage, human epidermal growth factor receptor 2 (HER2) status, and different treatment strategy, we could illustrate significant role of individual AQP mRNA expression in the prognosis of gastric cancer patients. Thus, our results indicated that AQP’s protein and mRNA expression in gastric cancer patients provide effective role to predict prognosis and act as an essential agent to therapeutic strategy.
Timely monitoring of global plant biogeochemical processes demands fast and highly accurate estimation of plant nutrition status, which is often estimated based on hyperspectral data. However, few such studies have been conducted on degraded vegetation. In this study, complete combinations of either original reflectance or first-order derivative spectra have been developed to quantify leaf nitrogen (N), phosphorus (P), and potassium (K) contents of tree, shrub, and grass species using hyperspectral datasets from light, moderate, and severely degraded vegetation sites in Helin County, China. Leaf N, P, and K contents were correlated to identify suitable combinations. The most effective combinations were those of reflectance difference (Dij), normalized differences (ND), first-order derivative (FD), and first-order derivative difference (FD(D)). Linear regression analysis was used to further optimize sensitive band-based combinations, which were compared with 43 frequently used empirical spectral indices. The proposed hyperspectral indices were shown to effectively quantify leaf N, P, and K content (R2 > 0.5, p < 0.05), confirming that hyperspectral data can be potentially used for fine scale monitoring of degraded vegetation.employed in biomes such as grasslands and savannas 1,18-20 , crops 21 , and trees 22 . A study of maize leaf P content found that 540, 720, 740, and 850 nm are the most sensitive bands for detection of P in both the vegetation production stage and the flowering stage 21 .The spectral region which most closely relates to leaf P content has been found to overlay the spectral region which demonstrates water absorbing traits (1000-2500 nm) 19 . Bands which are indicative of leaf P also lie in the region of 580-710 nm, although this varies among different case studies. The confusion between water absorption and sensitivity to sampling sites makes it difficult to identify the most suitable bands for leaf P estimation. In this study, we aimed to select several sensitive bands from the 500 available and develop a complete combination of reflectance and its first-order derivative (FD) from tree, shrub, and grass species in various degraded vegetation sites, with the objective of developing more general hyperspectral indices for the estimation of leaf P content.Finally, potassium (K) is also a key plant requirement, present mostly as K + ions in vacuoles. K provides regulatory control over processes such as transpiration, starch synthesis, sucrose translocation, respiration, and lipid synthesis 23 . Plants deficient in K exhibit limited growth, metabolism, and stress defense 24 , leading to lower overall biomass and coverage and changes to leaf color. If a K deficiency occurs at the vegetation level, this can accelerate the degradation process. Soils in many broad-acre semiarid areas have become deficient in K, resulting in a decrease of K in the canopy and stem 25,26 .Remote sensing of leaf N, P, or K contents is a challenging task due to the lack of direct absorption features that can be observed in t...
BackgroundPatients with frequent premature ventricular contractions (PVCs) are often symptomatic. Catheter ablation was usually indicated to eliminate symptoms in patients with PVCs-induced cardiomyopathy. Currently, PVCs-ablation is also applied for patients with PVCs and no structural heart diseases (SHD); however, the safety and efficacy of ablation in these patients remains unclear.MethodsIn this retrospective study, data from patients who underwent ablation for PVCs from January 2010 to December 2016 at our hospital was retrieved. Predictors of complications and acute procedural success were evaluated.ResultsA total of 1231 patients (mean age 47.8 ± 16.8 years, 59% female) were included. The overall complication rate was 2.7%, and the most common complication was hydropericardium. Two ablation-related mortalities occurred. One patient died of coronary artery injury during the procedure and the other died from infectious endocarditis. Location (left ventricle and epicardium) was the main predictor of complications, with right ventricular outflow tract (RVOT) predicting fewer complications. The acute procedural success rate was 94.1% in all patients. The main predictor of acute procedural success was RVOT origin, while an epicardial origin was a predictor of procedural failure.ConclusionLocations of left ventricle and epicardium were predictors of procedural complications for patients with PVCs. Therefore, ablation is not recommended in these patients. For other origins of PVCs, particularly RVOT origin, ablation is a safety and effective treatment.
Abstract:The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R c )=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.
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