Expression of ethylene biosynthetic and receptor genes in rose floral tissues during ethylene-enhanced flower opening

Ethylene production, as well as the expression of ethylene biosynthetic (Rh-ACS1–4 and Rh-ACO1) and receptor (Rh-ETR1–5) genes, was determined in five different floral tissues (sepals, petals, stamens, gynoecia, and receptacles) of cut rose (Rosa hybrida cv. Samantha upon treatment with ethylene or the ethylene inhibitor 1-methylcyclopropene (1-MCP). Ethylene-enhanced ethylene production occurred only in gynoecia, petals, and receptacles, with gynoecia showing the greatest enhancement in the early stage of ethylene treatment. However, 1-MCP did not suppress ethylene production in these three tissues. In sepals, ethylene production was highly decreased by ethylene treatment, and increased dramatically by 1-MCP. Ethylene production in stamens remained unchanged after ethylene or 1-MCP treatment. Induction of certain ethylene biosynthetic genes by ethylene in different floral tissues was positively correlated with the ethylene production, and this induction was also not suppressed by 1-MCP. The expression of Rh-ACS2 and Rh-ACS3 was quickly induced by ethylene in gynoecia, but neither Rh-ACS1 nor Rh-ACS4 was induced by ethylene in any of the five tissues. In addition, Rh-ACO1 was induced by ethylene in all floral tissues except sepals. The induced expression of ethylene receptor genes by ethylene was much faster in gynoecia than in petals, and the expression of Rh-ETR3 was strongly suppressed by 1-MCP in all floral tissues. These results indicate that ethylene biosynthesis in gynoecia is regulated developmentally, rather than autocatalytically. The response of rose flowers to ethylene occurs initially in gynoecia, and ethylene may regulate flower opening mainly through the Rh-ETR3 gene in gynoecia

Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.Comment: RecSys 202

Equivariant Contrastive Learning for Sequential Recommendation

Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augmentation strategies to generate positive pairs and encourage their representations to be invariant. However, due to the inherent properties of user behavior sequences, some augmentation strategies, such as item substitution, can lead to changes in user intent. Learning indiscriminately invariant representations for all augmentation strategies might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e.g., item substitution) and insensitive to mild augmentations (e.g., featurelevel dropout masking). In detail, we use the conditional discriminator to capture differences in behavior due to item substitution, which encourages the user behavior encoder to be equivariant to invasive augmentations. Comprehensive experiments on four benchmark datasets show that the proposed ECL-SR framework achieves competitive performance compared to state-of-the-art SR models. The source code is available at https://github.com/Tokkiu/ECL.Comment: Accepted by RecSys 202

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community

Burden of child maltreatment in China:A systematic review

Objective To estimate the health and economic burdens of child maltreatment in China. Methods We did a systematic review for studies on child maltreatment in China using PubMed, Embase, PsycInfo, CINAHL-EBSCO, ERIC and the Chinese National Knowledge Infrastructure databases. We did meta-analyses of studies that met inclusion criteria to estimate the prevalence of child neglect and child physical, emotional and sexual abuse. We used data from the 2010 global burden of disease estimates to calculate disability-adjusted life-years (DALYs) lost as a result of child maltreatment. Findings From 68 studies we estimated that 26.6% of children under 18 years of age have suffered physical abuse, 19.6% emotional abuse, 8.7% sexual abuse and 26.0% neglect. We estimate that emotional abuse in childhood accounts for 26.3% of the DALYs lost because of mental disorders and 18.0% of those lost because of self-harm. Physical abuse in childhood accounts for 12.2% of DALYs lost because of depression, 17.0% of those lost to anxiety, 20.7% of those lost to problem drinking, 18.8% of those lost to illicit drug use and 18.3% of those lost to self-harm. The consequences of physical abuse of children costs China an estimated 0.84% of its gross domestic product – i.e. 50 billion United States dollars – in 2010. The corresponding losses attributable to emotional and sexual abuse in childhood were 0.47% and 0.39% of the gross domestic product, respectively. Conclusion In China, child maltreatment is common and associated with large economic losses because many maltreated children suffer substantial psychological distress and might adopt behaviours that increase their risk of chronic disease

An Investigation on Chinese Public Acceptance of COVID-19 Prevention Measures

China has basically succeeded in bringing the COVID-19 epidemic under control, thanks to a timely series of effective prevention and control measures taken by the Chinese government. In this study, a public acceptance questionnaire of epidemic prevention measures was designed to investigate the influencing factors of public acceptance. A total of 2062 samples were collected from 8 March 2020 to 9 April 2020, and Independent-Samples T-Test and One-way ANOVA were used to analyze the data collected in the questionnaire in SPSS version 22.0. The results show that age and educational level have a significant influence on public acceptance. With the development of the epidemic, the acceptability grew generally higher. The public acceptance of traffic measures is the highest. This study summarises China’s scientific experience in the fight against COVID-19 and the differences in public acceptance. It can provide a positive reference for the development of epidemic prevention in other countries

Study on Seismic Reduction Measures of a Diaphragm Wall—Underground Structure System

In this paper, the seismic reduction and isolation measures are first proposed by setting a segmented isolation layer between the diaphragm wall and the side wall of the station structure. Although the segmented isolation layer can effectively improve the stress state of the side wall and slabs, the seismic reduction effect of the middle column is not obvious. In order to improve the overall seismic performance of the station structure, the reduction measures by combining the segmented isolation layer and new type bearing of the middle column are then proposed. At the same time of inserting the isolation layer between the diaphragm wall and the structure, the sliding bearing at the top of the column is set up to reduce the vibration. The results show that the segmented isolation layer can significantly reduce the internal force and damage at the top, bottom and side wall joints. In addition, the combined measures of segmented isolation layer and sliding bearing at the top of the column can effectively reduce the seismic damage of the middle column. The damage of the connection between the top plate, the middle plate and the middle column can be significantly decreased

The Mediation Role of Safety Attitude in the Impact of Resilience on the Safety Behavior of Coal Miners in China

Resilience can improve the adaptability of coal miners to high-hazard and high-stress environments. After facing setbacks or adversities, resilience can enable coal miners to recover from bad mental states and have an optimistic safety attitude and positive safety behaviors. However, how resilience affects safety behavior and the role of safety attitude in the relationship have not been clear. This study systematically reviewed previous research on resilience, safety attitude, and safety behavior. By recovering 639 valid questionnaires, the validity and reliability of the resilience scale, safety attitude scale, and safety behavior scale for coal miners were verified. Hierarchical regression analysis explored the relationships between resilience, safety attitude, and safety behavior. Studies have shown that resilience positively affects safety attitude and safe behavior. Safety attitude positively affects safety behaviors and plays a role as a partial mediator in the impact of resilience on safe behavior. The theoretical contribution is that the resilience of miners has a positive impact on safety behavior. Moreover, resilience can also act on safety behaviors through the partial intermediation of safety attitude. The practical contribution is that managers of coal mining companies can promote the resilience and safety attitude of coal miners to improve safety behaviors and prevent accidents

Development of A Safety Climate Scale for Geological Prospecting Projects in China

The geological prospecting industry has developed rapidly in China over the past few years. It has made outstanding contributions to the discovery of new mineral resources, new energy sources, and the excavation and utilization of resources. However, geological prospecting projects do not have effective safety management measures at present. Moreover, the geological prospecting project has its own traits and features that differ from other industries, leading to the fact that safety management measures in other industries cannot be used in geological prospecting projects. Therefore, development of an effective safety management measuring tool is urgent and necessary. In recent years, safety climate has drawn great attention from scholars, and research results have been successfully applied in construction, coal mining and other industries. Based on the extensive literature review on safety climate as well as its organizational structure and employees’ individual behavior characteristics, this paper first extracted the factor structure of the safety climate and then developed a safety climate scale for geological prospecting projects. This paper used the methods of exploratory factor analysis and reliability analysis to ensure the developed safety climate scale was valid and reliable. The safety climate scale developed has four dimensions, i.e., project leader’s safety commitment, safety institutions, risk response, and employee’s safety attitude, containing a total of 17 measurable items. This study contributes to the current literature by exploring the factor structure of the safety climate for geological prospecting projects, and further provides a scientific basis for improvements in the geological prospecting industry. Meanwhile, the findings not only provide technical support for investigating and analyzing the safety management levels of the geological prospecting industry, but also contribute to the benchmarking standards among different enterprises and projects