Rapid Regeneration Offsets Losses from Warming-Induced Tree Mortality in an Aspen-Dominated Broad-Leaved Forest in Northern China

Worldwide tree mortality as induced by climate change presents a challenge to forest managers. To successfully manage vulnerable forests requires the capacity of regeneration to compensate for losses from tree mortality. We observed rapid regeneration and the growth release of young trees after warming-induced mortality in a David aspen-dominated (Populus davidiana) broad-leaved forest in Inner Mongolia, China, as based on individual tree measurements taken in 2012 and 2015 from a 6-ha permanent plot. Warming and drought stress killed large trees 10–15 m tall with a total number of 2881 trees during 2011–2012, and also thinned the upper crowns. David aspen recruitment increased 2 times during 2012–2015 and resulted in a high transition probability of David aspen replacing the same or other species, whereas the recruitment of Mongolian oak (Quercus mongolica) was much lower: it decreased from 2012 to 2015, indicating that rapid regeneration represented a regrowth phase for David aspen, and not succession to Mongolian oak. Further, we found that the recruitment density increased with canopy openness, thus implying that warming-induced mortality enhanced regeneration. Our results suggest that David aspen has a high regrowth ability to offset individual losses from warming-induced mortality. This important insight has implications for managing this vulnerable forest in the semi-arid region of northern China

A Finite Queue Model Analysis of PMRC-based Wireless Sensor networks

In our previous work, a highly scalable and fault- tolerant network architecture, the Progressive Multi-hop Rotational Clustered (PMRC) structure, is proposed for constructing large-scale wireless sensor networks. Further, the overlapped scheme is proposed to solve the bottleneck problem in PMRC-based sensor networks. As buffer space is often scarce in sensor nodes, in this paper, we focus on studying the queuing performance of cluster heads in PMRC-based sensor networks. We develop a finite queuing model to analyze the queuing performance of cluster heads for both non-overlapped and overlapped PMRC-based sensor network. The average queue length and average queue delay of cluster head in different layers are derived. To validate the analysis results, simulations have been conducted with different loads for both non- overlapped and overlapped PMRC-based sensor networks. Simulation results match with the analysis results in general and confirm the advantage of selecting two cluster heads over selecting single cluster head in terms of the improved queuing performance

Survey of Multi-task Recommendation Algorithms

Single-task recommendation algorithms have problems such as sparse data, cold start and unstable recommendation effect. Multi-task recommendation algorithms can jointly model multiple types of user behaviour data and additional information, to better explore the user’s interests and needs in order to improve the recommendation effect and user satisfaction, which provides a new way of thinking to solve a series of problems existing in single-task recommendation algorithms. Firstly, the development background and trend of multi-task recommendation algorithms are sorted out. Secondly, the implementation steps of the multi-task recommendation algorithm and the construction principle are introduced, and the advantages of multi-task learning with data enhancement, feature identification, feature complementation and regularization effect are elaborated. Then, the application of multi-task learning methods in recommendation algorithms with different sharing models is introduced, and the advantages and disadvantages of some classical models and the relationship between tasks are summarized. Then, the commonly used   datasets and evaluation metrics for multi-task recommendation algorithms are introduced, and the differences and connections with other recommendation algorithms in terms of dataset evaluation metrics are elaborated. Finally, it is pointed out that multi-task learning has shortcomings such as negative migration, parameter optimization conflicts, poor interpretability, etc., and an outlook is given to the combination of multi-task recommendation algorithms with reinforcement learning, convex function optimization methods, and heterogeneous information networks

Genome-Wide Association Study for Plant Height and Grain Yield in Rice under Contrasting Moisture Regimes

Drought is one of the vitally critical environmental stresses affecting both growth and yield potential in rice. Drought resistance is a complicated quantitative trait that is regulated by numerous small effect loci and hundreds of genes controlling various morphological and physiological responses to drought. For this study, 270 rice landraces and cultivars were analyzed for their drought resistance. This was done via determination of changes in plant height and grain yield under contrasting water regimes, followed by detailed identification of the underlying genetic architecture via genome-wide association study (GWAS). We controlled population structure by setting top two eigenvectors and combining kinship matrix for GWAS in this study. Eighteen, five, and six associated loci were identified for plant height, grain yield per plant, and drought resistant coefficient, respectively. Nine known functional genes were identified, including five for plant height (OsGA2ox3, OsGH3-2, sd-1, OsGNA1 and OsSAP11/OsDOG), two for grain yield per plant (OsCYP51G3 and OsRRMh) and two for drought resistant coefficient (OsPYL2 and OsGA2ox9), implying very reliable results. A previous study reported OsGNA1 to regulate root development, but this study reports additional controlling of both plant height and root length. Moreover, OsRLK5 is a new drought resistant candidate gene discovered in this study. OsRLK5 mutants showed faster water loss rates in detached leaves. This gene plays an important role in the positive regulation of yield-related traits under drought conditions. We furthermore discovered several new loci contributing to the three investigated traits (plant height, grain yield, and drought resistance). These associated loci and genes significantly improve our knowledge of the genetic control of these traits in rice. In addition, many drought resistant cultivars screened in this study can be used as parental genotypes to improve drought resistance of rice by molecular breeding

Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice

Annotation of candidate genes anchored by associated SNPs. (XLSX 34 kb

Effect of solution-focused approach on anxiety and depression in patients with rheumatoid arthritis: A quasi-experimental study

IntroductionAnxiety and depression are common psychological problems in rheumatoid arthritis (RA) patients. However, few effective nursing intervention models have been designed specifically to improve anxiety and depression in RA patients. Solution-focused approach (SFA) is an effective intervention method for psychosocial issues. There have been no studies involving SFA yet in RA patients. This study investigated the effects of SFA-based nursing intervention on anxiety and depression in RA patients.MethodsA quasi-experimental study using a convenience sampling of RA patients was conducted. The 48 RA patients were divided into the control group (n = 24) and the experimental group (n = 24). The control group received routine nursing intervention, while the experimental group received SFA-based nursing intervention. The scores on the self-rating anxiety scale (SAS), self-rating depression scale (SDS), arthritis self-efficacy scale-8 (ASES-8), and questionnaire on patient satisfaction with nursing care were collected before and after nursing interventions.ResultsBetween-Group Comparison: Before the nursing intervention, there was no statistically significant difference in the SDS, SAS, and ASES-8 scores between the two groups (p > 0.05). However, after the nursing intervention, the SDS and SAS scores of the experimental group were statistically significantly lower than those of the control group (p < 0.05). In contrast, the ASES-8 score of the experimental group was statistically significantly higher than that of the control group (p < 0.05). In addition, patient satisfaction with nursing care of the experimental group was better than that of the control group (p > 0.05). Within-Group Comparison: There was no statistically significant difference in the SDS, SAS, and ASES-8 scores in the control group before and after routine nursing intervention (p > 0.05). However, in the experimental group, the SDS and SAS scores before SFA-based nursing intervention were statistically significantly higher than those after SFA nursing intervention (p < 0.05), and the ASES-8 score before SFA-based nursing intervention was considerably lower than that after SFA nursing intervention (p < 0.05).DiscussionSFA-based nursing intervention can effectively improve anxiety, depression, and arthritis self-efficacy of RA patients. This study broadens clinical psychological nursing intervention models for RA patients. SFA may be an effective nursing model for various psychosocial problems in the current medical context