Roots of six Cucurbitaceae species were exposed to low (14 °C), middle (24 °C), and high (34 °C) temperatures while aerial parts of plants were maintained at ambient temperatures between 23 and 33 °C. The highest dry mass (DM), photon-saturated rate of net photosynthesis (PNsat), and stomatal conductance (gs) were found at 14 °C in figleaf gourd and turban squash plants, at 24 °C in cucumber and melon plants, while bitter melon and wax gourd plants had lower DM, PNsat, and gs at 14 °C than at 24 or 34 °C. Sub-or supra-optimum root temperatures did not induce photoinhibition but induced slight changes in the quantum efficiency of photosystem 2, PS2 (ΦPS2) and photochemical quenching (qp). Meanwhile, xylem sap abscisic acid (ABA) concentration followed a contrasting change pattern to that of gs. Thus the change in PNsat was mainly due to the change in gs and roots played an important role in the regulation of stomatal behaviour by delivering increased amount of ABA to shoots at sub-or supra-optimum root temperatures. and Y. P. Zhang ... [et al.].
Chlorophyll (Chl) content, dry mass, relative water content (RWC), leaf mass per area (LMA), proline (Pro) content, malondialdehyde (MDA) content, superoxide dismutase (SOD) and peroxidase (POD) activity, PN-PAR response curves and gas exchange were studied to determine the effects of water stress on photosynthetic activity, dry mass partitioning and metabolic changes in four provenances of neem (Azadirachta indica A. Juss). The results indicated that provenance differences existed in the adaptation response to water stress that included changes to growth strategies coupled with ecophysiological and metabolic adjustments. As water stress increased, stomatal conductance (gs), net photosynthetic rate (PN), transpiration rate (E), and leaf RWC decreased while LMA increased in all provenances. Dry mass was reduced in droughted plants and the percentage increased in dry mass allocated to roots, and enzyme activities of SOD and POD were highest in neem originating from Kalyani (KA) provenance and lowest in neem originating from New Dehli (ND) provenance. In contrast, water stress increased MDA content least in KA and most in ND. Furthermore, neem originating from ND also had the greatest decrease in Chl a/b ratio while the ratio was least affected in neem originating from KA. These findings suggest neem originating from KA may have more drought resistance than neem originating from ND. The data from PN-PAR response curves are less clear. While these curves showed that drought stress increased compensation irradiance (Ic) and dark respiration (RD) and decreased saturation irradiance (Is) and maximum net photosynthetic rate (Pmax), the extent of decline in P max was provenance dependent. P max under non-waterlimiting conditions was higher in neem originating from Jodhpur (MA) (about 14 μmol m-2 s-1) than in the other three provenances (all about 10 μmol m-2 s-1), but mild water stress had minimal effect on Pmax of these three provenances whereas Pmax of MA provenance declined to 10 μmol m-2 s-1, i.e. a similar value. However, under severe water stress P max of MA and KA provenances had declined to 40% of non-stressed values (about 6 and 4 μmol m-2 s-1, respectively) whereas the decline in Pmax of neem originating from Kulapachta (KU) and ND provenances was about 50% of nonstressed values (about 5 μmol m-2 s-1). These data suggest the PN responses of KU and ND provenances are most tolerant, and KA and MA least tolerant to increasing water stress, but also suggest MA provenance could be the most desired under both non-water-limiting and water-limiting conditions due to highest Pmax in all conditions. and Y. X. Zheng ... [et al.].
Brassinosteroids (BRs), an important class of plant steroidal hormones, play a significant role in the amelioration of various biotic and abiotic stresses. 24-epibrassinolide (EBR), an active brassinosteroid, was applied exogenously in different concentrations to characterize a role of BRs in tolerance of melon (Cucumis melo L.) to high temperature (HT) stress and to investigate photosynthetic performance of HT-stressed, Honglvzaocui (HT-tolerant) and Baiyuxiang (HTsensitive), melon variety. Under HT, Honglvzaocui showed higher biomass accumulation and a lower index of heat injury compared with the Baiyuxiang. The exogenous application of 1.0 mg L-1 EBR, the most effective concentration, alleviated dramatically the growth suppression caused by HT in both ecotypes. Similarly, EBR pretreatment of HTstressed plants attenuated the decrease in relative chlorophyll content, net photosynthetic rate, stomatal conductance, stomatal limitation, and water-use efficiency (WUE), as well as the maximal quantum yield of PSII photochemistry (Fv/Fm), the efficiency of excitation capture of open PSII center, the effective quantum yield of PSII photochemistry (ΦPSII), photochemical quenching coefficient, and the photon activity distribution coefficients of PSI (α). EBR pretreatment further inhibited the increase in intracellular CO2 concentration, leaf transpiration rate, minimal fluorescence of dark-adapted state, nonphotochemical quenching, thermal dissipation, and photon activity distribution coefficients of PSII. Results obtained here demonstrated that EBR could alleviate the detrimental effects of HT on the plant growth by improving photosynthesis in leaves, mainly reflected as up-regulation of photosynthetic pigment contents and photochemical activity associated with PSI. and Y. P. Zhang ... [et al.].
Feature reduction is an important issue in pattern recognition. Lower feature dimensionality could reduce the complexity and enhance the generalization ability of classifiers. In this paper we propose a new supervised dimensionality reduction method based on Locally Linear Embedding and Distance Metric Learning. First, in order to increase the interclass separability, a linear discriminant transformation learnt from distance metric learning is used to map the original data points to a new space. Then Locally Linear Embedding is adopted to reduce the dimensionality of data points. This process extends the traditional unsupervised Locally Linear Embedding to supervised scenario in a clear and natural way. In addition, it can also be seen as a general framework for developing new supervised dimensionality reduction algorithms by utilizing corresponding unsupervised methods. Extensive classification experiments performed on some real-world and artificial datasets show that the proposed method can achieve comparable to or even better results over other state-of-the-art dimensionality reduction methods.