Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm.

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.

Sex-based differences in fairness norm compliance and neural circuitry.

Human behavior often aligns with fairness norms, either voluntarily or under external pressure, like sanctions. Prior research has identified distinct neural activation patterns associated with voluntary and sanction-based compliance or non-compliance with fairness norms. However, an investigation gap exists into potential neural connectivity patterns and sex-based differences. To address this, we conducted a study using a monetary allocation game and functional magnetic resonance imaging to examine how neural activity and connectivity differ between sexes across three norm compliance conditions: voluntary, sanction-based, and voluntary post-sanctions. Fifty-five adults (27 females) participated, revealing that punishment influenced decisions, leading to strategic calculations and reduced generosity in voluntary compliance post-sanctions. Moreover, there were sex-based differences in neural activation and connectivity across the different compliance conditions. Specifically, the connectivity between the right dorsolateral prefrontal cortex and right dorsal anterior insular appeared to mediate intuitive preferences, with variations across norm compliance conditions and sexes. These findings imply potential sex-based differences in intuitive motivation for diverse norm compliance conditions. Our insights contribute to a better understanding of the neural pathways involved in fairness norm compliance and clarify sex-based differences, offering implications for future investigations into psychiatric and neurological disorders characterized by atypical socialization and mentalizing.

Norm Dynamics: Interdisciplinary Perspectives on Social Norm Emergence, Persistence, and Change.

Social norms are the glue that holds society together, yet our knowledge of them remains heavily intellectually siloed. This article provides an interdisciplinary review of the emerging field of norm dynamics by integrating research across the social sciences through a cultural-evolutionary lens. After reviewing key distinctions in theory and method, we discuss research on norm psychology-the neural and cognitive underpinnings of social norm learning and acquisition. We then overview how norms emerge and spread through intergenerational transmission, social networks, and group-level ecological and historical factors. Next, we discuss multilevel factors that lead norms to persist, change, or erode over time. We also consider cultural mismatches that can arise when a changing environment leads once-beneficial norms to become maladaptive. Finally, we discuss potential future research directions and the implications of norm dynamics for theory and policy.

Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms.

Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms and L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.

SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations.

MicroRNAs (miRNAs) have been demonstrated to be closely related to human diseases. Studying the potential associations between miRNAs and diseases contributes to our understanding of disease pathogenic mechanisms. As traditional biological experiments are costly and time-consuming, computational models can be considered as effective complementary tools. In this study, we propose a novel model of robust orthogonal non-negative matrix tri-factorization (NMTF) with self-paced learning and dual hypergraph regularization, named SPLHRNMTF, to predict miRNA-disease associations. More specifically, SPLHRNMTF first uses a non-linear fusion method to obtain miRNA and disease comprehensive similarity. Subsequently, the improved miRNA-disease association matrix is reformulated based on weighted k-nearest neighbor profiles to correct false-negative associations. In addition, we utilize L 2 , 1 norm to replace Frobenius norm to calculate residual error, alleviating the impact of noise and outliers on prediction performance. Then, we integrate self-paced learning into NMTF to alleviate the model from falling into bad local optimal solutions by gradually including samples from easy to complex. Finally, hypergraph regularization is introduced to capture high-order complex relations from hypergraphs related to miRNAs and diseases. In 5-fold cross-validation five times experiments, SPLHRNMTF obtains higher average AUC values than other baseline models. Moreover, the case studies on breast neoplasms and lung neoplasms further demonstrate the accuracy of SPLHRNMTF. Meanwhile, the potential associations discovered are of biological significance.

Biological Links between Personality and Plasticity: Testing Some Alternative Hypotheses.

AbstractWhen organisms respond behaviorally to a stimulus, they exhibit plasticity, but some individuals respond to the same stimulus consistently differently than others, thereby also exhibiting personality differences. Parent house sparrows express individual differences in how often they feed offspring and how that feeding rate changes with nestling age. Mean feeding rate and its slope with respect to nestling age were positively correlated at median nestling ages but not at hatching, indicating that individuality is primarily in plasticity. Individual differences could arise because of (1) interactions between environmental variables, (2) differences in underlying state or "quality," or (3) differences in the ability to update cues of changing nestling demand. Individual slopes were modestly repeatable across breeding attempts, hinting at the likely action of additional environmental variables, but only brood size was important. I also found few correlates suggesting quality differences. I used short-term brood size manipulations at two nestling ages to test divergent predictions between the three hypotheses. The pattern of correlations between response to the manipulation and individual slope did not fit any single hypothesis. Patterns of sparrow parental care reveal that personality and plasticity are not cleanly separable, and their biology is likely intertwined. New thinking may be needed about the factors parents use in decisions about care and the relevant fitness consequences.

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.

PURPOSE: This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. METHODS: Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. RESULT: Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. CONCLUSION: Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.

Genetic and Environmental Patterns Underlying Phenotypic Plasticity in Flowering Time and Plant Height in Sorghum.

Phenotypic plasticity is the property of a genotype to produce different phenotypes under different environmental conditions. Understanding genetic and environmental factors behind phenotypic plasticity helps answer some longstanding biology questions and improve phenotype prediction. In this study, we investigated the phenotypic plasticity of flowering time and plant height with a set of diverse sorghum lines evaluated across 14 natural field environments. An environmental index was identified to quantitatively connect the environments. Reaction norms were then obtained with the identified indices for genetic dissection of phenotypic plasticity and performance prediction. Genome-wide association studies (GWAS) detected different sets of loci for reaction-norm parameters (intercept and slope), including 10 new genomic regions in addition to known maturity (Ma1) and dwarfing genes (Dw1, Dw2, Dw3, Dw4 and qHT7.1). Cross-validations under multiple scenarios showed promising results in predicting diverse germplasm in dynamic environments. Additional experiments conducted at four new environments, including one from a site outside of the geographical region of the initial environments, further validated the predictions. Our findings indicate that identifying the environmental index enriches our understanding of gene-environmental interplay underlying phenotypic plasticity, and that genomic prediction with the environmental dimension facilitates prediction-guided breeding for future environments.

Environmental context of phenotypic plasticity in flowering time in sorghum and rice.

Phenotypic plasticity is an important topic in biology and evolution. However, how to generate broadly applicable insights from individual studies remains a challenge. Here, with flowering time observed from a large geographical region for sorghum and rice genetic populations, we examine the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments and searched for potential strategies to inform the study design. Both sample size and environmental mean range of the subset affected the consistency. The subset with either a large range of environmental mean or a large sample size resulted in genetic parameters consistent with the overall pattern. Furthermore, high accuracy through genomic prediction was obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under the subsets of environments with either a large range or a large sample size. With 1428 and 1674 simulated settings, our analyses suggested that the distribution of environmental index values of a site should be considered in designing experiments. Overall, we showed that environmental context was critical, and considerations should be given to better cover the intended range of the environmental variable. Our findings have implications for the genetic architecture of complex traits, plant-environment interaction, and climate adaptation.

Within-population plastic responses to combined thermal-nutritional stress differ from those in response to single stressors, and are genetically independent across traits in both males and females.

Phenotypic plasticity helps animals to buffer the effects of increasing thermal and nutritional stress created by climate change. Plastic responses to single and combined stressors can vary among genetically diverged populations. However, less is known about how plasticity in response to combined stress varies among individuals within a population or whether such variation changes across life-history traits. This is important because individual variation within populations shapes population-level responses to environmental change. Here, we used isogenic lines of Drosophila melanogaster to assess the plasticity of egg-to-adult viability and sex-specific body size for combinations of 2 temperatures (25 °C or 28 °C) and 3 diets (standard diet, low caloric diet, or low protein:carbohydrate ratio diet). Our results reveal substantial within-population genetic variation in plasticity for egg-to-adult viability and wing size in response to combined thermal-nutritional stress. This genetic variation in plasticity was a result of cross-environment genetic correlations that were often < 1 for both traits, as well as changes in the expression of genetic variation across environments for egg-to-adult viability. Cross-sex genetic correlations for body size were weaker when the sexes were reared in different conditions, suggesting that the genetic basis of traits may change with the environment. Furthermore, our results suggest that plasticity in egg-to-adult viability is genetically independent from plasticity in body size. Importantly, plasticity in response to diet and temperature individually differed from plastic shifts in response to diet and temperature in combination. By quantifying plasticity and the expression of genetic variance in response to combined stress across traits, our study reveals the complexity of animal responses to environmental change, and the need for a more nuanced understanding of the potential for populations to adapt to ongoing climate change.

The incubation environment does not explain significant variation in heart rate plasticity among avian embryos.

The conditions an organism experiences during development can modify how they plastically respond to short-term changes in their environment later in life. This can be adaptive because the optimal average trait value and the optimal plastic change in trait value in response to the environment may differ across different environments. For example, early developmental temperatures can adaptively modify how reptiles, fish and invertebrates metabolically respond to temperature. However, whether individuals within populations respond differently (a prerequisite to adaptive evolution), and whether this occurs in birds, which are only ectothermic for part of their life cycle, is not known. We experimentally tested these possibilities by artificially incubating the embryos of Pekin ducks (Anas platyrhynchos domesticus) at constant or variable temperatures. We measured their consequent heart rate reaction norms to short-term changes in egg temperature and tracked their growth. Contrary to expectations, the early thermal environment did not modify heart rate reaction norms, but regardless, these reaction norms differed among individuals. Embryos with higher average heart rates were smaller upon hatching, but heart rate reaction norms did not predict subsequent growth. Our data also suggests that the thermal environment may affect both the variance in heart rate reaction norms and their covariance with growth. Thus, individual avian embryos can vary in their plasticity to temperature, and in contrast to fully ectothermic taxa, the early thermal environment does not explain this variance. Because among-individual variation is one precondition to adaptive evolution, the factors that do contribute to such variability may be important.

Post-traumatic growth in parents of long-term childhood cancer survivors compared to the general population: A report from the Swiss childhood cancer survivor study-Parents.

OBJECTIVE: Post-traumatic growth (PTG) describes perceived positive changes following a traumatic event. We describe (i) PTG in parents of long-term childhood cancer survivors (CCS-parents) compared to parents of similar-aged children of the general population (comparison-parents), (ii) normative data for the Swiss population, and (iii) psychological, socio-economic, and event-related characteristics associated with PTG. METHODS: CCS-parents (aged ≤16 years at diagnosis, ≥20 years old at study, registered in the Childhood Cancer Registry Switzerland (ChCR), and the Swiss population responded to a paper-based survey, including the PTG-Inventory (total score 0-105). We carried out (i) t-tests, (ii) descriptive statistics, and (iii) multilevel regression models with survivor/household as the cluster variable. RESULTS: In total, 746 CCS-parents (41.7% fathers, response-rate = 42.3%) of 494 survivors (median time since diagnosis 24 (7-40) years), 411 comparison-parents (42.8% fathers, 312 households), and 1069 individuals of the Swiss population (40.7% male, response-rate = 20.1%) participated. Mean [M] total PTG was in CCS-parents M = 52.3 versus comparison-parents M = 50.4, p = 0.078; and in the Swiss population M = 44.5). CCS-parents showed higher 'relating-to-others' (18.4 vs. 17.3, p = 0.010), 'spiritual-change' (3.3 vs. 3.0, p = 0.038) and 'appreciation-of-life' (9.3 vs. 8.4, p = 0.027) than comparison-parents, but not in 'new-possibilities' and 'personal-strength'. Female gender, older age, higher post-traumatic stress, and higher resilience were positively associated with PTG. Individuals reporting events not typically classified as traumatic also reported growth. CONCLUSIONS: Our findings highlight that mothers and fathers can experience heightened growth many years after their child's illness. Being able to sensitively foreshadow the potential for new-possibilities and personal development may help support parents in developing a sense of hope.

Multiple augmented reduced rank regression for pan-cancer analysis.

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types.

Detecting context dependence in the expression of life history trade-offs.

Life history trade-offs are one of the central tenets of evolutionary demography. Trade-offs, depicting negative covariances between individuals' life history traits, can arise from genetic constraints, or from a finite amount of resources that each individual has to allocate in a zero-sum game between somatic and reproductive functions. While theory predicts that trade-offs are ubiquitous, empirical studies have often failed to detect such negative covariances in wild populations. One way to improve the detection of trade-offs is by accounting for the environmental context, as trade-off expression may depend on environmental conditions. However, current methodologies usually search for fixed covariances between traits, thereby ignoring their context dependence. Here, we present a hierarchical multivariate 'covariance reaction norm' model, adapted from Martin (2023), to help detect context dependence in the expression of life-history trade-offs using demographic data. The method allows continuous variation in the phenotypic correlation between traits. We validate the model on simulated data for both intraindividual and intergenerational trade-offs. We then apply it to empirical datasets of yellow-bellied marmots (Marmota flaviventer) and Soay sheep (Ovis aries) as a proof-of-concept showing that new insights can be gained by applying our methodology, such as detecting trade-offs only in specific environments. We discuss its potential for application to many of the existing long-term demographic datasets and how it could improve our understanding of trade-off expression in particular, and life history theory in general.

Facilitating clinical use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative data and a diagnostic cutoff value.

OBJECTIVE: The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) is well validated and commonly used to assess difficulties in everyday functioning regarding dementia. To facilitate interpretation and clinical implementation across different European countries, we aim to provide normative data and a diagnostic cutoff for dementia. METHODS: Cross-sectional data from Dutch Brain Research Registry (N = 1,064; mean (M) age = 62 ± 11 year; 69.5% female), European Medial Information Framework-Alzheimer's Disease 90 + (N = 63; Mage = 92 ± 2 year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (N = 247; Mage = 63 ± 7 year; 72.1% female) were used. The generalized additive models for location, scale, and shape framework were used to obtain normative values (Z-scores). The beta distribution was applied, and combinations of age, sex, and educational attainment were modeled. The optimal cutoff for dementia was calculated using area under receiver operating curves (AUC-ROC) and Youden Index, using data from Amsterdam Dementia Cohort (N = 2,511, Mage = 64 ± 8 year, 44.4% female). RESULTS: The best normative model accounted for a cubic-like decrease of IADL performance with age that was more pronounced in low compared to medium/high educational attainment. The cutoff for dementia was 1.85 standard deviation below the population mean (AUC = 0.97; 95% CI [0.97-0.98]). CONCLUSION: We provide regression-based norms for A-IADL-Q and a diagnostic cutoff for dementia, which help improve clinical assessment of IADL performance across European countries.

Impacts of ecosystem service message framing and dynamic social norms on public support for tropical forest restoration.

The effectiveness of strategic psychology-based marketing techniques for increasing public support for conservation is poorly understood. We assessed how such techniques affect support for tropical rainforest restoration with a controlled online experiment with 1166 nationally representative residents of the United Kingdom. We tested whether support increased when adding ecosystem service (ES) framings to typical nongovernmental organizations' (NGOs) biodiversity-focused messages that emphasize benefits to UK residents or people living near the tropical restoration site and a dynamic social norm nudge that emphasized increasing popularity of environmental restoration. We considered how respondents' psychological traits (nature connection, self-efficacy, psychological benefits of supporting charities, awareness of environmental degradation in the Global South, and climate change skepticism) influenced responses. Outcomes included respondents' reported advertisement sufficiency, sympathetic attitudes, behavioral support, and financial support. The study population typically found advertisements sufficient and exhibited sympathetic attitudes and financial, but not behavioral, support. Younger people exhibited greater conservation support than older respondents. Messages framed solely on biodiversity conservation were as effective as those highlighting additional ES benefits received by UK residents and people near the tropical restoration site. This suggests that framing around ESs, rather than nature's intrinsic value, may not strengthen public support for conservation. The dynamic social norm nudge had perverse effects. It reduced perceived social norms and most outcome variables. Alternative dynamic norm nudges warrant testing, but our results support research suggesting dynamic norm nudges can be ineffective when associated with activism, challenging their use by conservation NGOs. Psychological benefits of supporting charities and perceived self-efficacy increased support for advertisements, highlighting the benefits of including impact statements relating respondents' support to specific outcomes. Climate change skepticism decreased support, whereas nature connection and perceived static social norms increased it, highlighting the need to increase nature connection and pro-environmental social norms to elevate public support for conservation.

Impactos del encuadre de los mensajes sobre servicios ambientales y las normas sociales dinámicas sobre el apoyo público hacia la restauración de bosques tropicales Resumen Sabemos poco sobre la eficiencia de las técnicas de mercadotecnia basadas en la psicología estratégica para aumentar el apoyo público a la conservación. Evaluamos cómo afectan dichas técnicas al apoyo a la restauración de la selva tropical mediante un experimento controlado en línea con 1,166 residentes del Reino Unido representativos a nivel nacional. Comprobamos si el apoyo aumentaba al añadir marcos de servicios ambientales a los mensajes típicos de las organizaciones no gubernamentales (ONG) centrados en la biodiversidad, que hacen hincapié en los beneficios para los residentes del Reino Unido o las personas que viven cerca del lugar de restauración tropical y un empuje dinámico de normas sociales que hacía hincapié en la creciente popularidad de la restauración ecológica. Analizamos la influencia de los rasgos psicológicos de los encuestados (conexión con la naturaleza, autoeficacia, beneficios psicológicos de apoyar a organizaciones benéficas, experiencia de degradación ambiental en el Sur Global y escepticismo ante el cambio climático) sobre las respuestas. Los resultados fueron la suficiencia de los anuncios, las actitudes de simpatía, el apoyo conductual y el apoyo económico. En general, la población del estudio consideró que los anuncios eran suficientes y mostró actitudes de simpatía y apoyo económico, pero no conductuales. La población más joven mostró un mayor apoyo a la conservación que los encuestados de más edad. Los mensajes centrados únicamente en la conservación de la biodiversidad fueron tan eficaces como los que destacaban los beneficios adicionales de los servicios ambientales recibidos por los residentes del Reino Unido y las personas cercanas al lugar de restauración tropical. Esto sugiere que el encuadre en torno a los servicios ambientales, en lugar del valor intrínseco de la naturaleza, puede no reforzar el apoyo público a la conservación. El empuje dinámico de la norma social tuvo efectos perversos ya que redujo las normas sociales percibidas y la mayoría de las variables de resultado. Es necesario probar otros incentivos dinámicos, pero nuestros resultados corroboran las investigaciones que sugieren que los incentivos dinámicos pueden ser ineficaces cuando se asocian con el activismo, lo que cuestiona su uso por parte de las ONG de la conservación. Los beneficios psicológicos por apoyar a organizaciones benéficas y la autoeficacia percibida aumentaron el apoyo a los anuncios, lo que resalta las ventajas de incluir declaraciones de impacto que relacionen el apoyo de los encuestados con resultados específicos. El escepticismo ante el cambio climático redujo el apoyo, mientras que la conexión con la naturaleza y las normas sociales estáticas percibidas lo aumentaron, lo que destaca la necesidad de aumentar la conexión con la naturaleza y las normas sociales proambientales para elevar el apoyo público a la conservación.

Act generously when others do so: Majority influence on young children's sharing behavior.

Children's sharing behavior is profoundly shaped by social norms within their society, and they can learn these norms by directly observing how most others share in their immediate environment. Here we systematically investigated the impact of majority influence on the sharing behavior of young Chinese children through three studies (N = 336, 168 girls). Four- and 6-year-olds were allowed to choose 10 favorite stickers and had an opportunity to engage in anonymous sharing. Before making the sharing decision, children were assigned to one of two conditions: watching a video in which three peers all shared 8 out of 10 stickers (i.e., the majority sharing condition) or making their decisions without watching the video (i.e., the control condition). Results showed that both the 4- and 6-year-old children shared more stickers in the majority sharing condition than in the control condition (Studies 1 & 2). Moreover, the influence of the majority had a stronger effect compared to the influence of a single role model. Children shared more stickers after observing three peers sharing, compared to watching one peer sharing three times (Study 2). Furthermore, children were less likely to copy the majority's non-sharing behavior when it came to giving away stickers without prosocial outcomes, which was particularly evident among 4-year-olds (Study 3). The results reveal that majority influence uniquely shapes children's sharing behavior and that children selectively follow the majority based on whether the behavior exhibits prosocial attributes. A video abstract of this article can be viewed at https://youtu.be/8qNNhf9754I?si=7YfpaFpcD_IjlXjJ RESEARCH HIGHLIGHTS: Observing a majority of three peers' unanimous generous sharing promoted sharing behavior in both 4- and 6-year-olds. The influence of three peers on children's sharing was stronger than that of one peer sharing three times. Four-year-olds, but not 6-year-olds, did not copy the non-sharing behavior of the majority as it did not lead to prosocial outcomes.

Pornography Use and the Acceptance of Gender Norm Violation in a School Context.

Although media effect studies have quite extensively investigated the association between pornography use and gendered attitudes, some questions remain. The present study aimed to address two of these questions by exploring how gendered attitudes and gender beliefs may be influenced by gender typicality and pornography use. First, the literature has not yet accounted for individual differences based on gender typicality. Second, the influence of pornography use on gender beliefs going beyond pornography's script application is understudied. This online cross-sectional study (N = 1,440, Mage = 23.86, SD = 4.79) contributes to the field by investigating the indirect association between pornography use and acceptance of gender norm violation through gendered attitudes and the moderating role of gender typicality. Acceptance of gender norm violation was measured via vignettes describing a school context in which a teacher and a student violated gender norms. Findings indicated that gendered attitudes negatively relate to the acceptance of gender norm violation. Moreover, compared to women, men's pornography use indirectly relates to lower acceptance rates through gendered attitudes. Additionally, for men, specific levels of gender typicality and atypicality form a strengthening and buffering role, respectively. This applies to the association between pornography use and gendered attitudes as well as to the indirect relationship of pornography use with acceptance of gender norm violation. These findings suggest that pornography use may also affect gender beliefs that are unrelated to the scripts present in pornography. Future studies should take into account the type of preferred pornography and unravel the specific impact of women's pornography use.

How big is too big? A qualitative study of discretionary food portion size norms among Australian consumers.

OBJECTIVE: The high availability of energy-dense nutrient-poor discretionary foods in large serving and package sizes may have shifted portion size norms (described as a typical perception of how much people choose to eat from a given food at a single eating occasion) towards larger sizes. Few public health recommendations exist around appropriate discretionary food portion sizes. This qualitative study aimed to explore the underlying rationale of portion size norms of discretionary foods among Australian adults 18-65 years. DESIGN: Four focus group sessions were conducted. Collected data were analysed using inductive thematic analysis. SETTING: Focus groups were held online via Zoom between September and October 2023. PARTICIPANTS: Thirty-four participants were recruited in the study (mean age 38 years, 19 females). RESULTS: The key themes raised from inductive analysis were personal factors, eating context factors, and food environment factors relevant to the portion size norms. A framework was established to illustrate the interaction across these themes during the conceptualisation of the norms. For serving size availability, consumers found that there were limited serving size choices when making portion size selections and lacked the knowledge and skills in portion control. CONCLUSIONS: These findings highlight the need to make positive changes to the current food environment and develop relevant public health guidelines around appropriate portion sizes to promote healthier portion size norms and enable better portion control.

Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.

OBJECTIVE: Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM. MATERIALS AND METHODS: This work proposes a Schatten p -norm-driven model-based deep learning framework for QSM with a learnable norm parameter p to adapt to the data. In contrast to other model-based architectures that enforce the l 2 -norm or l 1 -norm for the denoiser, the proposed approach can enforce any p -norm ( 0 < p ≤ 2 ) on a trainable regulariser. RESULTS: The proposed method was compared with deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits. CONCLUSION: The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.