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IntroductionSoil salinity–alkalinity has emerged as a global problem affecting many ecosystems, including grassland. Plants evolve into different ecotypes to adapt to various environments. Leymus chinensis widely distributed in the eastern Eurasian steppe, has evolved into two main ecotypes: yellow–green(YG) and gray–green (GG). Studies on the adaption mechanisms of both ecotypes in response to saline–alkaline stress are limited.MethodsIn this study, the growth and physiological traits of ecotypes YG and GG in soils with different salinity–alkalinity levels—severe saline–alkaline soil (SS), moderate saline–alkaline soil (MS), and light saline–alkaline soil (LS)—were studied. After exposure to saline–alkaline stress for 15 months, the L. chinensis ecotypes exhibited significant differences in the growth characteristics.ResultsThe specific leaf area, individual tiller biomass, net photosynthetic rate, and potassium content of the two ecotypes under MS conditions were significantly higher than or similar to those under LS conditions. This indicates that L. chinensis showed a certain degree of tolerance to saline–alkaline environments under MS conditions. Saline–alkaline stress increased the tillers by 56% in GG and reduced them by 26% in YG, and did not alter the SLA and the number of individual tiller leaves of GG but reduced that of YG. Moreover, with increasing levels of saline–alkaline stress, ecotype GG exhibited an increase in net photosynthetic rate (Pn), while ecotype YG showed insignificant changes. Under SS, GG exhibited higher Pn and chlorophyll content than YG. Additionally, with the increase in the saline–alkaline stress level, the Na+ content increased, but GG exhibited a significantly lower Na+ content than YG. Conversely, the K+ and Ca2+ contents and the K+/Na+ and Ca2+/Na+ ratios decreased, but GG exhibited higher values than YG.DiscussionGG reduced Na+ by absorption by increasing tillers. Additionally, GG absorbed more K+ and Ca2+ ions, thereby maintaining higher K+/Na+ and Ca2+/Na+ ratios than YG under saline–alkaline stress. These adaptive strategies enable ecotype GG to grow and maintain normal physiological functions under high saline–alkaline stress. The findings of this study hold practical significance for enhancing the economic and ecological value of saline–alkaline grasslands.

Soil salinity is important abiotic stress affecting various ecosystems worldwide such as grassland. Distinct ecotypes often evolve within species by natural selection to facilitate adaptation to different types of environmental stress. Leymus chinensis is a perennial rhizomatous grass that is widely distributed in the eastern Eurasian steppe; it has two main ecotypes, namely, yellow-green (YG) and gray-green (GG), which differ in their strategy for coping with salinity stress. Few studies have examined the seed germination of the two ecotypes under salinity stress. In this study, the seed germination and seedling growth of two ecotypes of L. chinensis in response to different levels of salinity (NaCl) stress [0 (control), 20, 50, 100, and 200 mM] were examined. Then, ungerminated seeds were placed under normal conditions to evaluate seedling growth following exposure to salt stress (i.e., regermination). The germination percentage was significantly higher, and the mean germination time was significantly shorter in the GG ecotype than in the YG ecotype at all NaCl concentrations. As the salinity level increased, the radicle length of the two ecotypes decreased; however, GG had longer radicles and a higher number of radicles, even at 200 mM NaCl when no radicle protruding from the seed coat was detected in YG. The shoot length of GG was significantly longer than that of YG at all NaCl levels. After salinity stress was removed, the seed germination percentage increased as the original concentration of NaCl applied increased, but the total germination percentage did not significantly differ among NaCl concentrations. The total seed germination percentage of GG was approximately 80%, whereas that of the YG was approximately 20%. The seedling length of regerminated seeds for both GG and YG was similar. The thousand-grain weight of GG was significantly higher than that of YG. GG was more salt-tolerant than YG and might be better capable of surviving in harsher environments, suggesting that GG might be particularly useful for saline grassland restoration.

The reproductive characteristics of plants are likely affected by climate change e.g., changes in precipitation patterns and nitrogen deposition, but few studies have examined the effects of these ecological agents of selection on the seed yield and germination characteristics of perennial grasses. Here, we conducted a multiple-year pot experiment with Leymus chinensis, a common perennial grass in the eastern region of the Eurasian steppe zone, which was grown under three water treatments with and without nitrogen addition. The seed yield of L. chinensis increased with precipitation and was highest (7.0 g/pot) under 747 mm of precipitation with nitrogen addition (10.5 g/m2). Seed yield was positively correlated with heading number, tiller number, and grain number per spike, and the heading number was a critical factor affecting seed yield. Seed germination percentage and the time to obtain 50% germination were affected by environmental cues experienced by the mother plants.

Soil temperatures at different depths down the soil profile are important agro-meteorological indicators which are necessary for ecological modeling and precision agricultural activities. In this paper, using time series of soil temperature (ST) measured at different depths (0, 5, 10, 20, and 40 cm) at agro-meteorological stations in northern China as reference data, ST was estimated from land surface temperature (LST) and normalized difference vegetation index (NDVI) derived from AQUA/TERRA MODIS data, and solar declination (Ds) in univariate and multivariate linear regression models. Results showed that when daytime LST is used as predictor, the coefficient of determination (R2) values decrease from the 0 cm layer to the 40 cm layer. Additionally, with the use of nighttime LST as predictor, the R2 values were relatively higher at 5, 10 and 15 cm depths than those at 0, 20 and 40 cm depths. It is further observed that the multiple linear regression models for soil temperature estimation outperform the univariate linear regression models based on the root mean squared errors (RMSEs) and R2. These results have demonstrated the potential of MODIS data in tandem with the Ds parameter for soil temperature estimation at the upper layers of the soil profile where plant roots grow in. To the best of our knowledge, this is the first attempt at the synergistic use of LST, NDVI and Ds for soil temperature estimation at different depths of the upper layers of the soil profile, representing a significant contribution to soil remote sensing.

In order to model the accurate interest preference of microblog users and discover user groups with similar in-terest, a new method was proposed which considered the total amount of retweets, comments and attitudes of each mi-croblog for text feature calculation with utilizing classic analytical hierarchy process method. The proposed method used three indicators to evaluate the importance of the text feature representation and made an improvement on traditional tf-idf feature calculation method to fit for short text. Furthermore, this method was also implemented in the traditional clustering algorithm. Experimental results show that, compared with the traditional tf-idf method, the improved approach has a better clustering effect on the average scattering for clusters and the total separation between clusters.

Alkaline stress (high pH) severely damages root cells, and consequently, inhibits rice (Oryza sativa L.) seedling growth. In this study, we demonstrate the accumulation of reactive oxygen species (ROS) in root cells under alkaline stress. Seedlings of two rice cultivars with different alkaline tolerances, ‘Dongdao-4’ (moderately alkaline-tolerant) and ‘Jiudao-51’ (alkaline-sensitive), were subjected to alkaline stress simulated by 15 mM sodium carbonate (Na2CO3). Alkaline stress greatly reduced seedling survival rate, shoot and root growth, and root vigor. Moreover, severe root cell damage was observed under alkaline stress, as shown by increased membrane injury, malondialdehyde accumulation, and Evan’s Blue staining. The expression of the cell death-related genes OsKOD1, OsHsr203j, OsCP1, and OsNAC4 was consistently upregulated, while that of a cell death-suppressor gene, OsBI1, was downregulated. Analysis of the ROS contents revealed that alkaline stress induced a marked accumulation of superoxide anions (O2•-) and hydrogen peroxide (H2O2) in rice roots. The application of procyanidins (a potent antioxidant) to rice seedlings 24 h prior to alkaline treatment significantly alleviated alkalinity-induced root damage and promoted seedling growth inhibition, which were concomitant with reduced ROS accumulation. These results suggest that root cell damage, and consequently growth inhibition, of rice seedlings under alkaline stress is closely associated with ROS accumulation. The antioxidant activity of superoxide dismutase, catalase, peroxidase, and ascorbate peroxidase increased under alkaline stress in the roots, probably in response to the cellular damage induced by oxidative stress. However, this response mechanism may be overwhelmed by the excess ROS accumulation observed under stress, resulting in oxidative damage to root cells. Our findings provide physiological insights into the molecular mechanisms of alkalinity-induced damage to root cells, and will contribute to the improvement of alkaline stress tolerance in rice plants.

Targeting the problem of traditional sparrow search algorithms being prone to falling into local optima, a new algorithm called the Chaotic Sparrow Search Algorithm with Manta Ray Spiral Foraging (abbreviated as MSSA) is proposed. The Logistic-Sine-Cosine chaotic map and elite Reverse learning strategy are fused to initialize the population. It is experimentally demonstrated that this hybrid strategy outperforms the population after random initialization in reducing ineffective sparrow individuals. In the vigilante update stage, the spiral foraging behaviour of the manta ray population in the integrated manta ray optimization algorithm, the sparrows search around the best food source, which enhances the sparrow search algorithm's ability to explore the optimal solution. To enhance the stability of the algorithm to search for the optimum, a mixed Gaussian variational and logistic perturbation strategy is proposed to further improve the performance of the algorithm. Finally, using 12 commonly used benchmark test functions and the Wilcoxon rank sum test, MSSA was compared with other original algorithms and advanced improved algorithms, it is demonstrated that MSSA has higher accuracy and convergence performance, and the improved MSSA algorithm is applied to three types of engineering optimization problems with constraints, demonstrating its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we propose an end-to-end key-player-based group activity recognition network specially applied to the identification of basketball offensive tactics in limited data scenarios. Our previous studies show that basketball tactics can be better recognized via key player detection with multiple instance learning (MIL) using the support vector machine (SVM). However, the SVM in that work is required to extract features depending on basketball- and tactic-specific knowledge for good performance. Thus, in this study, we develop an end-to-end trainable neural network without prior knowledge and integrate MIL into it. As long as a tactic label is given, MIL can train the network to identify tactic’s key players. For testing, our network can recognize the key players in a video clip and provide a tag of the tactic related to them. Like other neural network models, our network requires a large annotated dataset. At the same time, we could collect only a few labeled data, which is common in dealing with group activity recognition. To overcome such a limitation, we propose a novel data augmentation framework, the tactical-based conditional generative adversarial network (GAN), for generating new labeled trajectories. The experimental results show that our method significantly improves 9.13 % in tactic recognition and 4.965 % in key player detection.

Proline accumulation is a common physiological response of plants to various environmental stresses. However, the physiological importance of proline accumulation in stress tolerance remains controversial. Here, we examined whether proline accumulation in rice (Oryza sativa L.) seedlings is correlated with tolerance to saline-alkaline stress factors, namely, salt, alkaline and osmotic stresses, by using 25 japonica cultivars. The tolerance of seedlings to each stress treatment varied significantly among the cultivars as evaluated by membership degree (MD) of fuzzy set theory on the basis of changes in shoot length (SL), total root length (TRL), total root surface area (RSA), average root diameter (RD), total root volume (RV), and root numbers (RN), after 6 d of stress treatment. The proline levels in all thejaponica cultivars increased significantly in response to the investigated stresses. However, partial correlation analysis showed no significant correlations between the basal or elevated proline levels and tolerance to any of the investigated stress factors. These findings suggest that proline accumulation levels do not serve as reliable parameters for assessing saline-alkaline stress tolerance in rice. On the other hand, several root growth indices of rice seedlings, namely TRL, RSA, RD, RV, and RN, showed good correlations with the investigated stress tolerance, and therefore, represent a useful set of growth parameters for assessing saline-alkaline stress tolerance in rice. [ABSTRACT FROM AUTHOR]

Saline-alkaline (SA) stress is a complex effect of three main factors: ion toxicity from high salinity, alkalinity, and high osmotic pressure. In this study, we dissected response of rice (Oryza sativa L.) to above-mentioned SA stress factors simulated by using NaCl, Na2CO3, and PEG6000, respectively. Seed germination was most strongly affected by osmotic stress, followed by alkaline and salt stresses. Meanwhile, the seedling growth was most reduced under alkaline stress. The alkaline treatment significantly reduced total biomass, total root length (TRL), root surface area (RSA), root numbers (RN), and root volume (RV), while it increased root diameter (RD) and Na+/K+ ratio. Further, it caused severe damages to the root system as shown by a remarkable increase in degree of cell injury and expression of the cell death-related gene OsNAC4. On the other hand, salt stress reduced the total biomass, RSA and RV, and increased the Na+/K+ ratio, while to less extent than those under alkaline stress. These results suggest that the deleterious effect of alkaline stress on rice seedlings is due mainly to high alkalinity that induces cell injury in the root system. In contrast, the response to osmotic stress exhibited the typical adaptive responses to dehydration: reduced relative water content (RWC), RD, RV, and shoot biomass; whereas increased TRL, RSA, RN, and root biomass. In addition, we show that proline was accumulated most abundantly in response to alkaline treatment, and which was corroborated by the transcriptional induction of OsP5CS1 and OsP5CS2, the genes encoding proline carboxylate synthase. [ABSTRACT FROM AUTHOR]