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The distribution of self-incompatibility systems in angiosperms: the relationship between mating system diversity, life span, growth habit and latitude in a changing global environment.

There is ample theoretical and experimental evidence that angiosperms harbouring self-incompatibility (SI) systems are likely to respond to global changes in unique ways relative to taxa with other mating systems. In this paper, we present an updated database on the prevalence of SI systems across angiosperms and examine the relationship between the presence of SI and latitude, biomes, life-history traits and management conditions to evaluate the potential vulnerability of SI taxa to climate change and habitat disturbance. We performed literature searches to identify studies that employed controlled crosses, microscopic analyses and/or genetic data to classify taxa as having SI, self-compatibility (SC), partial self-compatibility (PSC) or self-sterility (SS). Where described, the site of the SI reaction and the presence of dimorphic versus monomorphic flowers were also recorded. We then combined this database on the distribution of mating systems with information about the life span, growth habit, management conditions and geographic distribution of taxa. Information about the geographic distribution of taxa was obtained from a manually curated version of the Global Biodiversity Information Facility database, and from vegetation surveys encompassing 9 biomes. We employed multinomial logit regression to assess the relationship between mating system and life-history traits, management condition, latitude and latitude-squared using self-compatible taxa as the baseline. Additionally, we employed LOESS regression to examine the relationship between the probability of SI and latitude. Finally, by summarizing information at the family level, we plotted the distribution of SI systems across angiosperms including information about the presence of SI or dioecy, the inferred reaction site of the SI system when known, as well as the proportion of taxa in a family for which information is available. We obtained information about the SI status of 5686 hermaphroditic taxa, of which 55% exhibited SC, and the remaining 45% harbour SI, self-sterility (SS), or PSC. Highlights of the multinomial logit regression include that taxa with PSC have a greater odds of being short- (OR=1.3) or long- (OR=1.57) lived perennials relative to SC ones, and that SS/SI taxa (pooled) are less likely to be annuals (OR=0.64) and more likely to be long-lived perennials (OR=1.32). SS/SI taxa had a greater odds of being succulent (OR=2.4) or a tree (OR=2.05), and were less likely to be weeds (OR=0.34). Further, we find a quadratic relationship between the probability of being SI with latitude: SI taxa were more common in the tropics, a finding that was further supported by the vegetation surveys which showed fewer species with SS/SI in temperate and northern latitudes compared to mediterranean and tropical biomes. We conclude that in the short-term habitat fragmentation, pollinator loss and temperature increases may negatively impact plants with SI systems, particularly long-lived perennial and woody species dominant in tropical forests. In the longer term, these and other global changes are likely to select for self-compatible or partially self-compatible taxa which, due to the apparent importance of SI as a driver of plant diversification across the angiosperm tree of life, may globally influence plant species richness.

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Impact of Duchenne and non-Duchenne smiles on perceived trustworthiness of Black and White faces: A Black perspective.

In five experiments, we investigated how Black participants perceive Duchenne and non-Duchenne smiles on Black and White targets. Results consistently demonstrated that when assessing happiness, faces with Duchenne compared to non-Duchenne smiles were rated as happier on both Black and White targets. However, when assessing a more socially evaluative dimension, trustworthiness, perceptions of Black and White targets diverged. Whereas White targets with Duchenne compared to non-Duchenne smiles were rated as more trustworthy, ratings of Black targets with Duchenne and non-Duchenne smiles did not differ, with both appraised as highly trustworthy. Although the degree to which Black participants identified with their race did not moderate these effects, the perceived genuineness of targets did mediate the relationship. One reason why Duchenne compared to non-Duchenne smiles on White but not Black targets were perceived as more trustworthy is because Duchenne compared to non-Duchenne smiles on White but not Black targets were perceived as more genuine. A final study extended these findings by exploring the impact of target race and smile type on partner choice. In accordance with the results related to trustworthiness ratings, Black participants selected White partners with Duchenne compared to non-Duchenne smiles more often but did not differentiate in their choice of Black partners with Duchenne versus non-Duchenne smiles. These findings underscore the importance of investigating not only diverse targets but also diverse perceivers. Our results suggest that Black perceivers use facial cues differently when rating the trustworthiness of Black and White targets and that these perceptions have important downstream consequences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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Release of phosphorus and metal(loid)s from manured soils to floodwater during a laboratory simulation of snowmelt flooding.

Phosphorus (P) and metal accumulation in manured agricultural soils and subsequent losses to waterways have been extensively studied; however, the magnitudes and the factors governing their losses during spring snowmelt flooding are less known. We examined the P and metal release from long-term manured soil to floodwater under simulated snowmelt flooding with recent manure additions. Intact soil columns collected from field plots located in Randolph, Southern Manitoba, 2 weeks after liquid swine manure treatments (surface-applied, injected, or control with no recent manure addition) were flooded and incubated for 8 weeks at 4± 1°C to simulate snowmelt conditions. Floodwater (syringe filtered through 0.45µm) and soil porewater (extracted using Rhizon-Mom samplers) samples were periodically extracted and analyzed for dissolved reactive phosphorus (DRP), pH, zinc (Zn), manganese (Mn), iron (Fe), magnesium (Mg), calcium (Ca), and arsenic (As). Mean floodwater DRP concentrations (mg L-1) for manure injected (2.0±0.26), surface-applied (2.6±0.26), and control (2.2±0.26) treatments did not differ significantly. Despite manure application, DRP loss to floodwater did not significantly increase compared to the control, possibly due to the elevated residual soil P at this site from the long-term manure use. At the end of simulated flooding, the DRP concentrations increased by 1.5-fold and 5-fold in porewater and floodwater, respectively. Metal(loid) concentrations were not affected by manure treatments in general, except for Zn and Mg on certain days. Unlike DRP, where porewater and floodwater concentrations increased with time, metalloid concentration in porewater and floodwater did not show consistent trends with flooding time.

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Data Analysis of Student Academic Performance and Prediction of Student Academic Performance Based on Machine Learning Algorithms

With the development and popularization of education, the quality of education has become one of the key factors in the development of a country. And students' academic performance, as one of the important indicators of education quality, has been attracting much attention. This paper mines and analyzes the data affecting students' academic performance, and also conducts a predictive study of students' academic performance using logistic regression model. In this study, 30 indicators such as gender, age, family size, parental education, parental occupation, family relationship, health, and the number of drinks per this paperek and per month this paperre used as input variables, and students' academic performance was categorized into SUCCESS and FAIL, and the training and test sets this paperre divided according to the ratio of 7:3, and the logistic regression model was used for training and prediction. The results show that the logistic regression model has high prediction accuracy in predicting students' academic performance (whether they fail or not), with an accuracy of 95.8%, precision of 96.7%, recall of 95.1%, and F1 of 95.8%. This indicates that the logistic regression model has high accuracy and reliability in predicting students' academic performance. The results of this study are important for schools and educational organizations. Through the prediction of students' academic performance, schools can identify students' learning problems in time and take targeted measures to help students improve their academic performance. Meanwhile, this study also provides some useful reference information for individual students to help them better understand their learning situation, adjust their learning strategies in time and improve their learning efficiency. In the future, the method can be further explored and improved to enhance the accuracy and reliability of the prediction and to provide better support and assistance for students' learning and development.

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