Measurement invariance of the Coparenting Relationship Scale (CRS) across 10 countries.

The purpose of this study was to assess the factor structure and the measurement invariance of the Coparenting Relationship Scale (CRS) across 10 countries based on the seven-factor coparenting model (i.e., Coparenting Agreement, Coparenting Closeness, Exposure to Conflict, Coparenting Support, Endorsement of Partner's Parenting; Division of Labor) proposed by Feinberg (2003). The results of research on coparenting from numerous countries have documented its foundational importance for parent mental health, family relationship quality, child development, and psychopathology. Yet, a cross-country perspective is still lacking. Such a perspective can provide insight into which dimensions of coparenting are universally recognized and which are especially prone to variation. A unique multinational data set, comprised of 15 individual studies collected across 10 countries (Belgium, Brazil, China, Israel, Italy, Japan, Portugal, Switzerland, Turkey, USA) in nine languages was established (N = 9,292; 51.1% mothers). Measurement invariance analyses were conducted. A six-factor structure (original seven factors minus Division of Labor) of the measure was consistent across the different contexts and measurement invariance was achieved at the configural level. There was no support for metric or scalar invariance. These findings provide a basis for the CRS to be used across countries and should inspire future quantitative and qualitative research in cross-country coparenting research to understand what aspects are universal and what aspects of coparenting are linked to specific material, relational, or ideational conditions that underlie high-quality coparenting. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Distinct patterns of grandparental involvement in childrearing and Chinese preschoolers' social-emotional development.

Grandparental involvement in childrearing has been associated with children's social-emotional development, yet findings are mixed. Grandparental involvement is a multidimensional concept that includes both quantity (i.e., the degree of grandparental involvement) and quality aspects (i.e., the quality of parent-grandparent coparenting and the quality of grandparenting practices). This study included both quantity and quality aspects to identify heterogeneous patterns of grandparental involvement and examined the associations between distinct patterns of grandparental involvement and children's social-emotional outcomes. Participants were 428 families with Chinese preschoolers (Mage = 53.75 months, SD = 10.32; 51.4% boys). Primary parental and grandparental caregivers completed the questionnaires. Four patterns of grandparental involvement emerged: the low-involvement mediocre-quality, the median, the high-involvement uneven-quality, and the high-involvement high-quality groups. Heterogeneous patterns of grandparental involvement exist and are differentially associated with children's social-emotional development. Children with grandparents in the high-involvement high-quality group demonstrated higher levels of social skills than those in the low-involvement mediocre-quality group and the high-involvement uneven-quality group. They also showed the lowest level of problem behaviors. This study highlights variations in grandparental involvement and helps to clarify previous inconsistent findings regarding the role of grandparental involvement in child development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Chimeric deletion mutation of rpoC2 underlies the leaf-patterning of Clivia miniata var. variegata.

KEY MESSAGE: The deletion mutated rpoC2 leads to yellow stripes of Clivia miniata var. variegata by down regulating the transcription of 28 chloroplast genes and disturbing chloroplast biogenesis and thylakoid membrane development. Clivia miniata var. variegata (Cmvv) is a common mutant of Clivia miniata but its genetic basis is unclear. Here, we found that a 425 bp deletion mutation of chloroplast rpoC2 underlies the yellow stripes (YSs) of Cmvv. Both RNA polymerase PEP and NEP coexist in seed-plant chloroplasts and the ß″ subunit of PEP is encoded by rpoC2. The rpoC2 mutation changed the discontinuous cleft domain required to form the PEP central cleft for DNA binding from 1103 to 59 aa. RNA-Seq revealed that 28 chloroplast genes (cpDEGs) were all down-regulated in YSs, of which, four involved in chloroplast protein translation and 21 of photosynthesis system (PS)I, PSII, cytochrome b6/f complex and ATP synthase are crucial for chloroplast biogenesis/development. The accuracy and reliability of RNA-Seq was verified by qRT-PCR. Moreover, the chlorophyll (Chl) a/b content, ratio of Chla/Chlb and photosynthetic rate (Pn) of YS decreased significantly. Meanwhile, chloroplasts of the YS mesophyll cells were smaller, irregular in shape, contain almost no thylakoid membrane, and even proplastid was found in YS. These findings indicate that the rpoC2 mutation down-regulated expression of the 28 cpDEGs, which disturb chloroplast biogenesis and its thylakoid membrane development. Thus, there are not enough PSI and II components to bind Chl, so that the corresponding areas of the leaf are yellow and show a low Pn. In this study, the molecular mechanism of three phenotypes of F1 (Cmvv ♀ × C. miniata ♂) was revealed, which lays a foundation for the breeding of variegated plants.

Sucrose promotes branch-thorn occurrence of Lycium ruthenicum through dual effects of energy and signal.

Lycium ruthenicum is an important ecoeconomic thorny shrub. In this study, the L. ruthenicum plants of a clone showed two types of 'fewer leaves without thorn' and 'more leaves with thorns' under the same condition after transplanting. Microscopic observation revealed that the apical buds of the thornless (Thless) and thorny (Thorny) branches should be selected as materials for further study. RNA-Seq analysis showed that the KEGG pathway of starch and sucrose metabolism and differentially expressed genes of sugar transport protein 13 (SUT13), sucrose synthase (SUS), trehalose-phosphate phosphatase (TPP) and trehalose-phosphate synthase (TPS) were significantly up-regulated in Thorny. The results of qRT-PCR confirmed the accuracy and credibility of the RNA-Seq. The content of sucrose in Thorny was significantly higher than that in Thless, but the content of trehalose-6-phosphate (T6P) was opposite. Leaf-clipping treatments reduced sucrose content and inhibited the occurrence/development of branch-thorns; exogenous sucrose of 16 g l-1 significantly promoted the occurrence and growth of branch-thorns, and the promotion effects were significantly higher than those treated with non-metabolizable sucrose analogs (isomaltolose and melitose). These findings suggested that sucrose might play a dual role of energy and signal in the occurrence of branch-thorns. Higher sucrose supply in apical buds from more leaves promoted the occurrence of branch-thorns via a lower content of T6P and higher expression levels of SUS, TPP and TPS, whereas fewer leaves inhibited the occurrence. The molecular hypothesis model of the leaf number/sucrose supply regulating the occurrence of branch-thorns in L. ruthenicum was established in the study, which provides foundation for breeding both Thless L. ruthenicum and Thless types of other species.

Self-feedback LSTM regression model for real-time particle source apportionment.

Atmospheric particulate matter pollution has attracted much wider attention globally. In recent years, the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques. Such demands are summarized, in this paper, as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances, such as the existence of secondary source and similar source. In this study, we firstly analyze the possible and potential restraints in single particle source apportionment, then propose a novel three-step self-feedback long short-term memory (SF-LSTM) network for approximating the source contribution. The proposed deep learning neural network includes three modules, as generation, scoring and refining, and regeneration modules. Benefited from the scoring modules, SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment, meanwhile, the regeneration module calculates the source contribution in a non-linear way. The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators (residual sum of squares, stability, sparsity, negativity) for the restraints. Additionally, in short time-resolution analyzing, SF-LSTM provides better results under the restraint of stability.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution.

PM2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 µg/m3) and 33% (19.7 µg/m3) to variation in PM2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NOX. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM2.5 pollution.

Distinct patterns of organized activity participation and their associations with school readiness among Chinese preschoolers.

Organized extracurricular activities (EAs) constitute an important part of many young children's lives. However, the role of EAs in children's development during early childhood is poorly understood. The current study examined the associations between EA participation and a range of school readiness outcomes in a sample of 345 urban Chinese preschoolers. Using three waves of data collected on EA participation and applying growth mixture modeling, we discovered two distinct trajectory classes with respect to the breadth of EA participation as well as two classes for EA attendance intensity. With a series of covariates controlled for, children's greater initial levels of or rates of increase in EA breadth were related to better early math skills. Greater probabilities of belonging to the higher-intensity class were also associated with better early math skills in children. However, EA participation did not predict other aspects of children's school readiness, including Chinese reading, receptive vocabulary, expressive language, social skills, and problem behaviors. This study extends existing EA literature, which has primarily focused on school-aged populations in Western contexts, by demonstrating substantial individual variations in Chinese preschoolers' trajectories of EA participation. Our findings suggest that EAs seem to have little unique contribution to children's school readiness except for early math skills.