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Background Common spatial pattern (CSP) is most widely used in motor imagery based brain-computer interface (BCI) systems. In conventional CSP algorithm, pairs of the eigenvectors corresponding to both extreme eigenvalues are selected to construct the optimal spatial filter. In addition, an appropriate selection of subject-specific time segments and frequency bands plays an important role in its successful application. New method This study proposes to optimize spatial-frequency-temporal patterns for discriminative feature extraction. Spatial optimization is implemented by channel selection and finding discriminative spatial filters adaptively on each time-frequency segment. A novel Discernibility of Feature Sets (DFS) criteria is designed for spatial filter optimization. Besides, discriminative features located in multiple time-frequency segments are selected automatically by the proposed sparse time-frequency segment common spatial pattern (STFSCSP) method which exploits sparse regression for significant features selection. Finally, a weight determined by the sparse coefficient is assigned for each selected CSP feature and we propose a Weighted Naïve Bayesian Classifier (WNBC) for classification. Results Experimental results on two public EEG datasets demonstrate that optimizing spatial-frequency-temporal patterns in a data-driven manner for discriminative feature extraction greatly improves the classification performance. Comparison with existing methods The proposed method gives significantly better classification accuracies in comparison with several competing methods in the literature. Conclusions The proposed approach is a promising candidate for future BCI systems. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) based emotion recognition has become a hot research issue in the field of cognitive interaction and brain-computer interface (BCI). How to build a deep learning model which can fully learn frequency-spatial–temporal representation from complex emotional EEG data and has good neurological interpretability is still challenging. In this paper, a novel multiple frequency bands parallel spatial–temporal 3D deep residual learning framework (MFBPST-3D-DRLF) is proposed for EEG-based emotion recognition. Firstly, a new optimal frequency bands selection method based on group sparse regression is designed for characteristic analysis on frequency domain. Secondly, spatial–temporal 3D feature representations of multiple frequency bands are generated in the data preparation stage for fully expressing the discriminative local patterns among brain responses of different emotional states. Finally, a novel parallel 3D deep residual networks architecture is elaborately constructed to simultaneously extract high level abstract features and achieve accurate classification. Emotional EEG recognition performance of the proposed method has been evaluated on two benchmark datasets, namely SEED and SEED-IV. The proposed MFBPST-3D-DRLF achieves 96.67% and 88.21% on both datasets, outperforming several state-of-the-art algorithms. In addition, investigations on the intermediate results and model parameters reveal that neural signatures associated with different emotional states are traceable and gamma band is most suitable for EEG based emotion recognition. [Display omitted] • MFBPST-3D-DRLF has good representation learning ability on entire frequency-spatial–temporal domains. • MFBPST-3D-DRLF outperforms state-of-the-art algorithms on both SEED and SEED-IV datasets. • Single gamma band is most suitable for classifying both three-class and four-class emotions. • Neural signatures associated with different emotional states are traceable. [ABSTRACT FROM AUTHOR]

Summary: A cold-tolerant line (NY-1) and a cold-sensitive cultivar (Jinyan 4) of cucumber (Cucumis sativus) were treated with temperatures of 28°C/22°C or 28°C/12°C (day/night) in a 10-h photoperiod. Carbohydrates and related enzymes were assayed from 0 to 4h after the start of the dark period. Compared to the normal night temperature (22°C, control), sucrose, stachyose and galactinol increased in mature leaves under cold-night treatment (12°C) while sucrose, glucose and fructose in fruits remained unchanged. In peduncles, where stachyose is catabolized to sucrose after long-distance transport, cold nights simultaneously induced a significant increase of stachyose (substrate) and a decrease of sucrose (product), indicating that the metabolic step from stachyose to sucrose in peduncles is crucial to translocation inhibition in cold nights. This decrease was more pronounced in the cold-sensitive cultivar. Similar growth rates of fruits on one-fruit and two-fruit plants under cold-night treatment further confirmed that it is sink activity rather than source supply that is limiting the source–sink translocation. No significant genotypic differences in enzyme activities involved in the stachyose–sucrose conversion, including alkaline α-galactosidase, acid α-galactosidase, galactokinase, uridine diphosphate (UDP)-galactose pyrophosphorylase, UDP–glucose-4′-epimerase and sucrose synthase, were observed when assayed in an adenosine triphosphate (ATP)-rich in vitro environment. However, the ATP concentration was much higher in peduncles of the cold-tolerant line, indicating that a limiting ATP supply may be partially responsible for the stronger inhibition of the stachyose–sucrose pathway observed in the cold-sensitive cultivar (Jinyan 4). [Copyright &y& Elsevier]

• We focus on feature optimization of motor imagery EEG in BCI filed. • The significances of spatial channels are measured by random forest (RF) algorithm. • Temporal-frequency feature patterns are investigated via composite kernel learning. • Ant colony optimization (ACO) algorithm is applied for searching the best parameters. • Optimize spatial-temporal-frequency patterns comprehensively and simultaneously. Brain computer interface (BCI) is a novel technology that translates human intention into command to control external device. Common spatial pattern (CSP) algorithm is most frequently applied for feature engineering in motor imagery (MI) based BCI system. How to select the most suitable spatial channels, temporal & frequency parameters for different people before CSP is still a challenging issue which greatly affects the performance of MI based BCI system. In this paper, we introduce an adaptive multi-domain feature joint optimization framework. Specifically, random forest (RF) and composite kernel support vector machine (CKSVM) algorithms are used to measure the significances of different spatial channels and local temporal-frequency segments. An ant colony optimization (ACO) based scheme is proposed to search the most suitable spatial channels and temporal-frequency segments. We evaluated the effectiveness of the proposed algorithm on public BCI competition III data set IVa and two self-collected MI EEG datasets. For BCI competition III data set IVa, our method outperforms some other close related algorithms in the literature. For the two self-collected datasets, compared to the traditional manual parameter setting, the classification performance is proven to significantly improve (more than 15%) adopting our adaptive multi-domain parameters. Since our proposed method can simultaneously and automatically optimize subject-specific features in the entire spatial-temporal-frequency domains, the most discriminative CSP features can be selected and the performance of MI EEG classification is significantly improved. Thus, our research is a useful complement to the BCI field. [ABSTRACT FROM AUTHOR]

In recent years, a lot of excellent multi-view clustering methods have been proposed. Because most of them need to fuse all views at one time, they are infeasible as the number of views increases over time. If the present multi-view clustering methods are employed directly to re-fuse all views at each time, it is too expensive to store all historical views. In this paper, we proposed an efficient incremental multi-view spectral clustering method with sparse and connected graph learning (SCGL). In our method, only one consensus similarity matrix is stored to represent the structural information of all historical views. Once the newly collected view is available, the consensus similarity matrix is reconstructed by learning from its previous version and the current new view. To further improve the incremental multi-view clustering performance, the sparse graph learning and the connected graph learning are integrated into our model, which can not only reduce the noises, but also preserve the correct connections within clusters. Experiments on several multi-view datasets demonstrate that our method is superior to traditional methods in clustering accuracy, and is more suitable to deal with the multi-view clustering with the number of views increasing over time. [ABSTRACT FROM AUTHOR]

Highlights • Shallow groundwater had a considerable contribution on cucumber evapotranspiration. • In this condition 75% of estimated cucumber ETc optimize cucumber yield. • Using fertigation, 75% of the traditionally recommended N optimize cucumber yield. • Frequent irrigation with low water volume is important when water table is high. Abstract The middle and lower reaches of Yangtze River present high rainfall and always have a high water table. Recently drip fertigation has been introduced and widely used for vegetable production in this area. To investigate the effect of shallow groundwater and the newly-developed fertigation on the cucumber water and nitrogen (N) requirement, experiments were carried out in plastic tunnels in Yangzhou, Jiangsu, China, during the 2016 and 2017 growing seasons using a randomized block design with three replicates. Four N rates, 4 irrigation amounts and 2 irrigation intervals (2 d and 4 d) were applied in this experiment. The results showed that irrigation amount of 75% of estimated plant evapotranspiration (ET c) was optimal to obtain the highest yield, indicating shallow groundwater had a considerable contribution to the cucumber plant evapotranspiration in this condition. Fertigation technology improved the irrigation water use efficiency (IWUE) and N use efficiency (NUE) and 75% of the recommended N rate (calculated according to a traditional local nutrient balance method) optimized cucumber yield. Frequent irrigation with low water volume had a beneficial impact on the improvement of cucumber yield, IWUE, NUE and reduced the N leaching. According to this work, the recommended fertigation strategy is 0.75 of recommended N, 0.75 estimated ET c and 2 d irrigation interval in this experiment, in which cucumber yield of 105.4 Mg ha−1, IWUE of 41.1 kg m-3 and NUE of 356.4 kg kg−1 were achieved. [ABSTRACT FROM AUTHOR]

Raffinose family oligosaccharides (RFOs) accumulate under stress conditions in many plants and have been suggested to act as stress protectants. To elucidate the metabolic process of RFOs under cold stress, levels of RFOs, and related carbohydrates, the expression and activities of main metabolic enzymes and their subcellular compartments were investigated during low-temperature treatment and during the recovery period in cucumber leaves. Cold stress induced the accumulation of stachyose in vacuoles, galactinol in vacuoles and cytosol, and sucrose and raffinose in vacuoles, cytosol, and chloroplasts. After cold stress removal, levels of these sugars decreased gradually in the respective compartments. Among four galactinol synthase genes ( CsGS ), CsGS1 was not affected by cold stress, while the other three CsGSs were up-regulated by low temperature. RNA levels of acid-α-galactosidase ( GAL ) 3 and alkaline-α-galactosidase ( AGA) 2 and 3 , and the activities of GAL and AGA, were up-regulated after cold stress removal. GAL3 protein and GAL activity were exclusively located in vacuoles, whereas AGA2 and AGA 3 proteins were found in cytosol and chloroplasts, respectively. The results indicate that RFOs, which accumulated during cold stress in different subcellular compartments in cucumber leaves, could be catabolized in situ by different galactosidases after stress removal. [ABSTRACT FROM AUTHOR]

In cucumber ( Cucumis sativus L.), the preexisting fruits inhibit the growth of subsequent fruits. To study the mechanism underlying this phenomenon, we examined the sink activity, the level of free sugars, and the activity of SNF1-related protein kinase 1 (SnRK1) in the peduncles of two types of fruits. In the two-fruit cucumber plants, the growth rate and sink activity [evaluated by alkaline alpha-galactosidase (CsAGA) activity in the peduncle] of the first fruit were greater than those of the second fruit. The 14 C-labeling experiment revealed that assimilates produced by the leaves closer to the second fruit tended to move to the first fruit. Sucrose and trehalose-6-phosphate (T6P) levels in the peduncle of the first fruit were higher than those in the peduncle of the second fruit. The SnRK1 activity was lower in the peduncle of the first fruit than in that of the second fruit at 0–8 days after anthesis. The growth rate and sink activity of the second fruit were enhanced after the removal of the first fruit or after treatment with 6-benzyl aminopurine, as determined by comparison with an increase in the sucrose and T6P levels and a decrease in the SnRK1 activity in its peduncle. The SnRK1 activity was inhibited by T6P in an in vitro kinase assay, and the mRNA level of CsAGA1 in cucumber calli was up-regulated by exogenous trehalose treatment, confirming that the SnRK1 activity and CsAGA1 expression can be regulated by T6P levels. Our results suggest that the T6P- and SnRK1-mediated signaling functions are involved in the regulation of first-fruit inhibition in cucumber plants. [ABSTRACT FROM AUTHOR]

• Comparing Hubbard. • AG owns larger leaf area, higher net photosynthetic rate and leaf stachyose level. • AG has larger phloem cross area and higher sugar concentration in phloem sap. • AG unloads stachyose directly into fruit without catabolism at fast growing stage. Atlantic Giant (AG), the world largest pumpkin, was bred through continuously crosses and strict selections from the small fruit ancestor. To understand the change caused by the artificial cross and selection in the recent 100 years, differences of morphology, anatomy and physiology between two cultivars with distinct fruit size in Cucurbita maxima , AG and Hubbard were investigated. Comparing with Hubbard, AG owns more fruit cell number and larger fruit cell volume at harvest stage, accompanied by larger leaves, higher net photosynthetic rate, lower leaf total sugar concentration, higher leaf stachyose level, larger peduncle vascular cross area and higher phloem sap sugar concentration. Further theoretical calculation indicates that AG needs more leaves and longer translocation time per day to support its huge fruit growth. In addition, considerable glucose and stachyose were found in the phloem sap and sarcocarp of AG respectively, suggesting different loading and unloading mechanisms are involved in the giant pumpkin growth. These results provide useful cues for improving cultivation and breeding techniques of giant pumpkins. [ABSTRACT FROM AUTHOR]

• Alternative polyadenylation produces three transcript isoforms of the cucumber CsSTSs. • Three isoforms revealed different relative stabilities among different leaves. • CsSTS1 was the most stable isoform in the fruit-node leaf. • Fruit setting induced the CsSTS1 exclusive expression in the leaf at the same node. • APA is a delicate mechanism for CsSTS to regulate cucumber source-sink balance. Alternative polyadenylation (APA) is a pervasive mechanism for gene regulation in eukaryotes. Stachyose is the main assimilate translocated in the cucumber phloem. Stachyose synthase (CsSTS) catalyzes the last step of stachyose biosynthesis in cucumber leaves and plays a key role in the regulation of assimilate partitioning between source and sink. In this study, three CsSTS mRNAs with the same open reading frame and the 5'untranslated region (UTR), but differing in their 3'UTRs, named CsSTS1 (short), CsSTS2 (medium), and CsSTS3 (long), were identified. Southern blot and sequence analysis of the cucumber genome confirmed that these transcripts are regulated through APA from a single gene. No significant difference of in vitro translation efficiency was found among three mRNAs. However, the relative stabilities of three transcripts varied among different tissues and different leaf development stages of cucumber. CsSTS1 expression in cucumber calli was up-regulated by the raffinose (substrate of CsSTS) and down-regulated by stachyose (product of CsSTS), respectively. In cucumber plants, all three isoforms have considerable expression in non-fruit node leaves. However, in fruit-carrying node leaves, the expression of CsSTS2 and CsSTS3 was severely inhibited and only CsSTS1 was highly expressed, indicating fruit setting has a remarkable effect on the relative expression level of three transcripts. This "fruit setting" effect could be observed until at least 36 h after the fruit was removed from the node. Our results suggest that abundant expression of CsSTS1 is beneficial for stachyose loading in source leaves, and APA is a delicate mechanism for CsSTS to regulate cucumber source-sink balance. [ABSTRACT FROM AUTHOR]