Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (R_P = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.     

In Situ Measurement of Some Soil Properties in Paddy Soil Using Visible and Near-Infrared Spectroscopy

In situ measurements with visible and near-infrared spectroscopy (vis-NIR) provide an efficient way for acquiring soil information of paddy soils in the short time gap between the harvest and following rotation. The aim of this study was to evaluate its feasibility to predict a series of soil properties including organic matter (OM), organic carbon (OC), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH of paddy soils in Zhejiang province, China. Firstly, the linear partial least squares regression (PLSR) was performed on the in situ spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the non-linear least-square support vector machine (LS-SVM) algorithm was carried out aiming to extract more useful information from the in situ spectra and improve predictions. Results show that in terms of OC, OM, TN, AN and pH, (i) the predictions were worse using in situ spectra compared to laboratory-based spectra with PLSR algorithm (ii) the prediction accuracy using LS-SVM (R²>0.75, RPD>1.90) was obviously improved with in situ vis-NIR spectra compared to PLSR algorithm, and comparable or even better than results generated using laboratory-based spectra with PLSR; (iii) in terms of AP and AK, poor predictions were obtained with in situ spectra (R²<0.5, RPD<1.50) either using PLSR or LS-SVM. The results highlight the use of LS-SVM for in situ vis-NIR spectroscopic estimation of soil properties of paddy soils.     

Identification of Different Varieties of Sesame Oil Using Near-Infrared Hyperspectral Imaging and Chemometrics Algorithms

This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874–1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.     

湿地土壤全氮和全磷含量高光谱模型研究

氮磷是湿地生态系统土壤中的重要营养元素,其对湿地植被生长、湿地生态系统生产力、区域富营养化变化、湿地环境生态净化功能等具有重要的影响作用。研究氮磷营养物质在湿地土壤中的分布变化特征,对湿地生态系统评估、恢复和管理具有重要的意义。以中国高纬度地区面积最大的滨海芦苇湿地——盘锦湿地为研究区,采用不同建模方法(再抽样多元逐步回归模型bootstrap SMLR和再抽样偏最小二乘回归模型bootstrap PLSR)和光谱变换技术(包络线去除CR、光谱一阶微分FD和光谱倒数的对数LR),分别建立了湿地土壤全氮和全磷含量的估算模型。基于湿地土壤实测光谱,模拟高光谱Hyperion数据和多光谱TM数据,在此基础上进行湿地土壤营养元素含量估算。对比所建反演模型的估算精度,探讨高光谱遥感技术对湿地土壤营养元素组分的估算能力和适用性。研究结果表明:bootstrap PLSR相比于bootstrap SMLR建模方法,其对研究区湿地土壤全氮和全磷含量的估算获得了较高精度;对盘锦湿地土壤全氮含量的估算,最高估算精度产生于CR光谱变换技术结合bootstrap PLSR建模;对湿地土壤全磷含量的估算,最高估算精度产生于原光谱数据结合bootstrap PLSR建模;模拟高光谱数据Hyperion对湿地土壤全氮和全磷含量的估算精度均高于模拟多光谱数据TM,模拟Hyperion的估算精度更接近于实测光谱的估算精度。     

Effects of Tannic Acid,Green Tea and Red Wine on hERG Channels Expressed in HEK293 Cells

Tannic acid presents in varying concentrations in plant foods, and in relatively high concentrations in green teas and red wines. Human ether-à-go-go-related gene (hERG) channels expressed in multiple tissues (e.g. heart, neurons, smooth muscle and cancer cells), and play important roles in modulating cardiac action potential repolarization and tumor cell biology. The present study investigated the effects of tannic acid, green teas and red wines on hERG currents. The effects of tannic acid, teas and red wines on hERG currents stably transfected in HEK293 cells were studied with a perforated patch clamp technique. In this study, we demonstrated that tannic acid inhibited hERG currents with an IC₅₀ of 3.4 μM and ~100% inhibition at higher concentrations, and significantly shifted the voltage dependent activation to more positive potentials (Δ23.2 mV). Remarkably, a 100-fold dilution of multiple types of tea (green tea, oolong tea and black tea) or red wine inhibited hERG currents by ~90%, and significantly shifted the voltage dependent activation to more positive potentials (Δ30.8 mV and Δ26.0 mV, respectively). Green tea Lung Ching and red wine inhibited hERG currents, with IC₅₀ of 0.04% and 0.19%, respectively. The effects of tannic acid, teas and red wine on hERG currents were irreversible. These results suggest tannic acid is a novel hERG channel blocker and consequently provide a new mechanistic evidence for understanding the effects of tannic acid. They also revealed the potential pharmacological basis of tea- and red wine-induced biology activities.     

Detection of pest species with different ratios in tea plant based on electronic nose

It is highly possible that tea (Camellia sinensis) plant is attacked by more than one pest species at the same time, and the determination of their proportion is of great significance to the management of tea plants. However, there are no literatures focusing on it previously. In this work, two pest species (Ectropis obliqua and Ectropis grisescens) in six different ratios (10:0, 8:2, 6:4, 4:6, 2:8 and 0:10) were applied to attack tea plants and electronic nose (E‐nose) was employed to detect them, labelled as group 10:0, 8:2, 6:4, 4:6, 2:8 and 0:10, respectively. Two prediction methods were applied to predict the ratio of E. obliqua and E. grisescens attacking tea plant and their performances were compared. The first method employed regression algorithm for prediction analysis based on the whole E‐nose data directly. The second method classified tea plants into three main classes (the first class contained group 10:0, the second class contained groups 8:2, 6:4, 4:6 and 2:8, and the third class contained group 0:10) first, then regression algorithm was applied to deal with the second class for prediction analysis. The results showed that the second method had a better performance. Its discrimination results showed 100% of the correct classification rate for training set and 93.75% for testing set. Meanwhile, its prediction results showed 0.0005 of root mean square error (RMSE) for calibration set, 0.0064 for validation set and 99.07% of fitting correlation coefficients (R²) for calibration set, 91.22% for validation set, which were acceptable for prediction analysis and proved that E‐nose was a feasible technique for pests' ratio prediction.     

不同光谱类型对银川平原土壤含盐量反演精度的影响与校正

土壤盐渍化是导致土壤质量下降、耕地减产的重要因素之一。为准确快速评价银川平原土壤含盐量,本研究对野外高光谱数据和室内高光谱数据进行一阶微分(FDR)变换,逐步回归(SR)筛选特征波段,利用偏最小二乘回归(PLSR)与支持向量机(SVM)进行建模,明确适用于本地区土壤含盐量准确反演的光谱类型,并对较差光谱类型进行分段校正与全局校正,尝试提高土壤含盐量反演精度。结果表明: 基于野外光谱的土壤含盐量反演模型精度比室内光谱平均高58.9%;对室内光谱进行分段校正、全局校正后反演精度均有提高,其中,PLSR以分段校正精度更高,建模决定系数(R_c²)、验证决定系数(R_p²)和相对分析误差(RPD)分别为0.790、0.633和1.64,而SVM以全局校正精度更高,R_c²、R_p²和RPD分别为0.927、0.947和3.87;SVM模型的反演精度高于PLSR,其中,野外光谱建模效果最佳,室内全局校正光谱与室内分段校正光谱次之,室内光谱最差。因此,野外高光谱可实现对银川平原土壤表层含盐量的定量反演,经校正的室内光谱对土壤含盐量反演精度显著提升,均可为粮食安全与生态环境高质量发展提供保障。     

Hyperspectral and Thermal Imaging of Oilseed Rape (Brassica napus) Response to Fungal Species of the Genus Alternaria

In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%).     

Resistance to Water Transport in Shoots of Vitis vinifera L. : Relation to Growth at Low Water Potential

Apparent resistances to water transport in the liquid phase were determined from measurements of soil, root, basal shoot internode, shoot apex, and leaf water potentials and water flux in Vitis vinifera (cv White Riesling) during soil drying. Predawn water potential differences (ΔΨ) in the shoots accounted for 20% of the total ΔΨ between the soil and the shoot apex when plants were well-watered but increased to about 90% when shoot growth ceased. The ΔΨ from soil to root was essentially constant during this period. At low water potential, the ΔΨ in the shoot was persistent when transpiration was low (predawn) or completely prevented (plant bagging). The apparent hydraulic resistance between the basal shoot internode and most rapidly expanding leaf (or shoot apex) increased several-fold when water was withheld. Leaf and internode expansion both exhibited high sensitivity to increasing hydraulic resistance. Measurements of pneumatic resistance to air flow through frozen internode segments indicated progressive vapor-filling of vessels as soil drying progressed. From these observations and others in the literature, it was suggested that embolization may be a common occurrence and play an important role in the inhibition of shoot growth at moderate water deficits.     

ΔFBA—Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.     

Symport of proton and sucrose in plasma membrane vesicles isolated from spinach leaves

The mechanism of sucrose transport was investigated in plasma membrane (PM) vesicles isolated from spinach (Spinacia oleracea L.) leaves. PM vesicles were isolated by aqueous two-phase partitioning and were equilibrated in pH 7.8 buffer containing K⁺. The vesicles rapidly accumulated sucrose in the presence of a transmembrane pH gradient (ΔpH) with external pH set at 5.8. The uptake rate was slow at pH 7.8. The K⁺-selective ionophore, valinomycin, stimulated uptake in the presence of a ΔpH, and the protonophore, carbonyl cyanide m-chlorophenylhydrazone (CCCP), greatly inhibited ΔpH-dependent sucrose uptake. Addition of sucrose to the vesicles resulted in immediate alkalization of the medium. Alkalization was stimulated by valinomycin, was abolished by CCCP, and was sucrose-specific. These results demonstrate the presence of a tightly coupled H⁺/sucrose symporter in PM vesicles isolated from spinach leaves.     

Growth Simulation and Discrimination of Botrytis cinerea,Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R²) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03–53.40×10⁻⁴ and 0.011–0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.     

Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance

To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009–2010 and 2010–2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr _{680–760 nm}), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr _{680–760 nm} was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr _{680–760 nm} were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr _{680–760 nm} was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr _{680760 nm} for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance.     

Epidemiology of CKD Regression in Patients under Nephrology Care

Chronic Kidney Disease (CKD) regression is considered as an infrequent renal outcome, limited to early stages, and associated with higher mortality. However, prevalence, prognosis and the clinical correlates of CKD regression remain undefined in the setting of nephrology care. This is a multicenter prospective study in 1418 patients with established CKD (eGFR: 60–15 ml/min/1.73m²) under nephrology care in 47 outpatient clinics in Italy from a least one year. We defined CKD regressors as a ΔGFR ≥0 ml/min/1.73 m²/year. ΔGFR was estimated as the absolute difference between eGFR measured at baseline and at follow up visit after 18–24 months, respectively. Outcomes were End Stage Renal Disease (ESRD) and overall-causes Mortality.391 patients (27.6%) were identified as regressors as they showed an eGFR increase between the baseline visit in the renal clinic and the follow up visit. In multivariate regression analyses the regressor status was not associated with CKD stage. Low proteinuria was the main factor associated with CKD regression, accounting per se for 48% of the likelihood of this outcome. Lower systolic blood pressure, higher BMI and absence of autosomal polycystic disease (PKD) were additional predictors of CKD regression. In regressors, ESRD risk was 72% lower (HR: 0.28; 95% CI 0.14–0.57; p<0.0001) while mortality risk did not differ from that in non-regressors (HR: 1.16; 95% CI 0.73–1.83; p = 0.540). Spline models showed that the reduction of ESRD risk associated with positive ΔGFR was attenuated in advanced CKD stage. CKD regression occurs in about one-fourth patients receiving renal care in nephrology units and correlates with low proteinuria, BP and the absence of PKD. This condition portends better renal prognosis, mostly in earlier CKD stages, with no excess risk for mortality.     

ΔF508 CFTR Surface Stability Is Regulated by DAB2 and CHIP-Mediated Ubiquitination in Post-Endocytic Compartments

The ΔF508 mutant form of the cystic fibrosis transmembrane conductance regulator (ΔF508 CFTR) that is normally degraded by the ER-associated degradative pathway can be rescued to the cell surface through low-temperature (27°C) culture or small molecular corrector treatment. However, it is unstable on the cell surface, and rapidly internalized and targeted to the lysosomal compartment for degradation. To understand the mechanism of this rapid turnover, we examined the role of two adaptor complexes (AP-2 and Dab2) and three E3 ubiquitin ligases (c-Cbl, CHIP, and Nedd4-2) on low-temperature rescued ΔF508 CFTR endocytosis and degradation in human airway epithelial cells. Our results demonstrate that siRNA depletion of either AP-2 or Dab2 inhibits ΔF508 CFTR endocytosis by 69% and 83%, respectively. AP-2 or Dab2 depletion also increases the rescued protein half-life of ΔF508 CFTR by ~18% and ~91%, respectively. In contrast, the depletion of each of the E3 ligases had no effect on ΔF508 CFTR endocytosis, whereas CHIP depletion significantly increased the surface half-life of ΔF508 CFTR. To determine where and when the ubiquitination occurs during ΔF508 CFTR turnover, we monitored the ubiquitination of rescued ΔF508 CFTR during the time course of CFTR endocytosis. Our results indicate that ubiquitination of the surface pool of ΔF508 CFTR begins to increase 15 min after internalization, suggesting that CFTR is ubiquitinated in a post-endocytic compartment. This post-endocytic ubiquination of ΔF508 CFTR could be blocked by either inhibiting endocytosis, by siRNA knockdown of CHIP, or by treating cells with the CFTR corrector, VX-809. Our results indicate that the post-endocytic ubiquitination of CFTR by CHIP is a critical step in the peripheral quality control of cell surface ΔF508 CFTR.     

RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.     

Fine hyperspectral classification of rice varieties based on self-attention mechanism

The accurate identification of rice varieties using rapid and nondestructive hyperspectral technology is of practical significance for rice cultivation and agricultural production. This paper proposes a convolutional neural network classification model based on a self-attention mechanism (self-attention-1D-CNN) to improve accuracy in distinguishing between crop species in fields using canopy spectral information. After experimental materials were planted in the research area, portable equipment was used to collect the canopy hyperspectral data for rice during the booting stage. Five preprocessing methods and three extraction methods were used to process the data. A comparison of the classification accuracy of different classification models showed that the self-attention-1D-CNN proposed in this study achieved the best classification with an accuracy of 99.93%. The research demonstrated the feasibility of using hyperspectral technology for the fine classification of rice varieties, and the feasibility of using the CNN model as a potential classification method for near-ground crop monitoring and classification.     

Osmotic adjustment in sorghum: I. Mechanisms of diurnal osmotic potential changes

Osmotic adjustment, defined as a lowering of osmotic potential (ψ_π) due to net solute accumulation in response to water stress, has been considered to be a beneficial drought tolerance mechanism in some crop species. The objective of this experiment was to determine the relative contribution of passive versus active mechanisms involved in diurnal ψ_π changes in sorghum (Sorghum bicolor L. Moench) leaf tissue in response to water stress. A single sorghum hybrid (cv AT×623 × RT×430) was grown in the field under variable water supplies. Water potential, ψ_π, and relative water content were measured diurnally on expanding and the uppermost fully expanded leaves before flowering and on fully expanded leaves during the grain-filling period. Diurnal changes in total osmotic potential (Δψ_π) in response to water stress was 1.1 megapascals before flowering and 1.4 megapascals during grain filling in comparison with 0.53 megapascal under well-watered conditions. Under water-stressed conditions, passive concentration of solutes associated with dehydration accounted for 50% (0.55 megapascal) of the diurnal Δψ_π before flowering and 47% (0.66 megapascal) of the change during grain filling. Net solute accumulation accounted for 42% (0.46 megapascal) of the diurnal Δψ_π before flowering and 45% (0.63 megapascal) of the change during grain filling in water-stressed leaves. The relative contribution of changes in nonosmotic volume (decreased turgid weight/dry weight) to diurnal Δψ_π was less than 8% at either growth stages. Water stress did not affect leaf tissue elasticity or partitioning of water between the symplasm and apoplasm.     

Phenotypic and Genotypic Characterisation of Burkholderia cenocepacia J2315 Mutants Affected in Homoserine Lactone and Diffusible Signal Factor-Based Quorum Sensing Systems Suggests Interplay between Both Types of Systems

Many putative virulence factors of Burkholderia cenocepacia are controlled by various quorum sensing (QS) circuits. These QS systems either use N-acyl homoserine lactones (AHL) or cis-2-dodecenoic acid (“Burkholderia diffusible signal factor”, BDSF) as signalling molecules. Previous work suggested that there is little cross-talk between both types of systems. We constructed mutants in B. cenocepacia strain J2315, in which genes encoding CepI (BCAM1870), CciI (BCAM0239a) and the BDSF synthase (BCAM0581) were inactivated, and also constructed double (ΔcepIΔBCAM0581, ΔcciIΔBCAM0581 and ΔcepIΔcciI) mutants and a triple (ΔcepIΔcciIΔBCAM0581) mutant. Subsequently we investigated phenotypic properties (antibiotic susceptibility, biofilm formation, production of AHL and BDSF, protease activity and virulence in Caenorhabditis elegans) and measured gene expression in these mutants, and this in the presence and absence of added BDSF, AHL or both. The triple mutant was significantly more affected in biofilm formation, antimicrobial susceptibility, virulence in C. elegans, and protease production than either the single or double mutants. The ΔBCAM0581 mutant and the ΔcepIΔBCAM0581 and ΔcciIΔBCAM0581 double mutants produced significantly less AHL compared to the WT strain and the ΔcepI and ΔcciI single mutant, respectively. The expression of cepI and cciI in ΔBCAM0581, was approximately 3-fold and 7-fold (p<0.05) lower than in the WT, respectively. The observed differences in AHL production, expression of cepI and cciI and QS-controlled phenotypes in the ΔBCAM0581 mutant could (at least partially) be restored by addition of BDSF. Our data suggest that, in B. cenocepacia J2315, AHL and BDSF-based QS systems co-regulate the same set of genes, regulate different sets of genes that are involved in the same phenotypes and/or that the BDSF system controls the AHL-based QS system. As the expression of the gene encoding the C6-HSL synthase CciI (and to a lesser extent the C8-HSL synthase CepI) is partially controlled by BDSF, it seems likely that the BDSF QS systems controls AHL production through this system.     

Differential Metabolic Profiles during the Albescent Stages of ‘Anji Baicha’ (Camellia sinensis)

‘Anji Baicha’ is an albino tea cultivar with white shoots at low air temperature and green shoots at high air temperature in early spring. The metabolite contents in the shoots dynamically vary with the color changes and with shoot development. To investigate the metabolomic variation during the albescent and re-greening stages, gas chromatography–mass spectrometry combined with multivariate analysis were applied to analyze the metabolite profiles in the different color stages during the development of ''Anji Baicha'' leaves. The metabolite profiles of three albescent stages, including the yellow-green stage, the early albescent stage, and the late albescent stage, as well as the re-greening stage were distinguished using principal component analysis, revealing that the distinct developmental stages were likely responsible for the observed metabolic differences. Furthermore, a group classification and pairwise discrimination was revealed among the three albescent stages and re-greening stage by partial least squares discriminant analysis. A total of 65 differential metabolites were identified with a variable influence on projection greater than 1. The main differential metabolic pathways of the albescent stages compared with the re-greening stage included carbon fixation in photosynthetic organisms and the phenylpropanoid and flavonoid biosynthesis pathways. Compared with the re-greening stage, the carbohydrate and amino acid metabolic pathways were disturbed during the albescent stages. During the albescent stages, the sugar (fructofuranose), sugar derivative (glucose-1-phosphate) and epicatechin concentrations decreased, whereas the amino acid (mainly glycine, serine, tryptophan, citrulline, glutamine, proline, and valine) concentrations increased. These results reveal the changes in metabolic profiling that occur during the color changes associated with the development of the albino tea plant leaves.