US state vaccine mandates did not influence COVID-19 vaccination rates but reduced uptake of COVID-19 boosters and flu vaccines compared to bans on vaccine restrictions.

During the COVID-19 pandemic, some US states mandated vaccination for certain citizens. We used state-level data from the CDC to test whether vaccine mandates predicted changes in COVID-19 vaccine uptake, as well as related voluntary behaviors involving COVID-19 boosters and seasonal influenza vaccines. Results showed that COVID-19 vaccine adoption did not significantly change in the weeks before and after states implemented vaccine mandates, suggesting that mandates did not directly impact COVID-19 vaccination. Compared to states that banned vaccine restrictions, however, states with mandates had lower levels of COVID-19 booster adoption as well as adult and child flu vaccination, especially when residents initially were less likely to vaccinate for COVID-19. This research supports the notion that governmental restrictions in the form of vaccination mandates can have unintended negative consequences, not necessarily by reducing uptake of the mandated vaccine, but by reducing adoption of other voluntary vaccines.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence.

Triadic influence as a proxy for compatibility in social relationships.

Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric-the "triadic influence"-that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks-functions of the triadic influence and the personalities of the students-control the evolution of real social networks, opening an avenue for the quantitative study of these systems.

Cooperation and deception through stigmergic interactions in human groups.

Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here, we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or noncompetitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions.

Adaptive adjustment after conflict with group opinion: evidence from neural electrophysiology.

Individuals inherently seek social consensus when making decisions or judgments. Previous studies have consistently indicated that dissenting group opinions are perceived as social conflict that demands attitude adjustment. However, the neurocognitive processes of attitude adjustment are unclear. In this electrophysiological study, participants were recruited to perform a face attractiveness judgment task. After forming their own judgment of a face, participants were informed of a purported group judgment (either consistent or inconsistent with their judgment), and then, critically, the same face was presented again. The neural responses to the second presented faces were measured. The second presented faces evoked a larger late positive potential after conflict with group opinions than those that did not conflict, suggesting that more motivated attention was allocated to stimulus. Moreover, faces elicited greater midfrontal theta (4-7 Hz) power after conflict with group opinions than after consistency with group opinions, suggesting that cognitive control was initiated to support attitude adjustment. Furthermore, the mixed-effects model revealed that single-trial theta power predicted behavioral change in the Conflict condition, but not in the No-Conflict condition. These findings provide novel insights into the neurocognitive processes underlying attitude adjustment, which is crucial to behavioral change during conformity.

Achievement Goals: A Social Influence Cycle.

Achievement goals have been defined as the purpose of competence-relevant behavior. In this respect they connect one of the basic human needs, i.e., competence, to one of society's core values, i.e., achievement. We propose to look at achievement goals through the lens of social influence. We review both the influence that cultural, structural, and contextual factors have on achievement goal endorsement and the influence that endorsing achievement goals allows people to have within their social space. The review allows us to propose a circular model of the influence on and of achievement goals: The culture, social structures, and contexts that are typical of a certain society shape the specific environments in which individuals develop their achievement goals, which in turn has an influence on the expression and circulation of these achievement goals into society, in a social influence cycle.

Insect herbivory within modern forests is greater than fossil localities.

Fossilized leaves provide the longest running record of hyperdiverse plant-insect herbivore associations. Reconstructions of these relationships over deep time indicate strong links between environmental conditions, herbivore diversity, and feeding damage on leaves. However, herbivory has not been compared between the past and the modern era, which is characterized by intense anthropogenic environmental change. Here, we present estimates for damage frequencies and diversities on fossil leaves from the Late Cretaceous (66.8 Ma) through the Pleistocene (2.06 Ma) and compare these estimates with Recent (post-1955) leaves collected via paleobotanical methods from modern ecosystems: Harvard Forest, United States; the Smithsonian Environmental Research Center, United States; and La Selva, Costa Rica. Total damage frequency, measured as the percentage of leaves with any herbivore damage, within modern ecosystems is greater than any fossil locality within this record. This pattern is driven by increased frequencies across nearly all functional feeding groups within the Recent. Diversities of total, specialized, and mining damage types are elevated within the Recent compared with fossil floras. Our results demonstrate that plants in the modern era are experiencing unprecedented levels of insect damage, despite widespread insect declines. Human influence, such as the rate of global climate warming, influencing insect feeding and timing of life cycle processes along with urbanization and the introduction of invasive plant and insect species may drive elevated herbivory. This research suggests that the strength of human influence on plant-insect interactions is not controlled by climate change alone but rather, the way in which humans interact with terrestrial landscape.

Metabolomics analysis reveals the metabolite profiles of Rheum tanguticum grown under different altitudinal gradients.

BACKGROUND: Plant growth and quality are often affected by environmental factors, including geographical location, climate, and soil. In this study, we describe the effect of altitudinal differences on the growth and active ingredients in Rheum tanguticum Maxim. ex Balf. (R. tanguticum), a traditional Chinese medicinal herb known for its laxative properties. RESULTS: The results showed that plants grown at lower altitudes had better growth performances than those in higher altitude areas. The yield varied by 2.45-23.68 times with altitude, reaching a maximum of 102.01 t/ha. In addition, total anthraquinone and total sennoside contents decreased with increasing altitude, whereas total tannins increased with increasing altitude. The total anthraquinone content of the indicator compound reached 5.15% at five experimental sites, which exceeded the Chinese Pharmacopoeia standard by 70.87%. The content of the other two categories of active ingredients reached a maximum value of 0.94% (total sennosides) and 2.65% (total tannins). Redundancy analysis revealed that annual rainfall, annual average temperature, annual sunshine hours, and pH significantly affected growth and active ingredients. Moreover, key metabolites, such as flavonoids, amino acids and their derivatives, phenolic acids, lipids, and terpenes, were differentially expressed between samples from low- and high-altitude cultivation areas. These metabolites were enriched in the flavonoid and flavonol biosynthetic pathway and the monoterpene biosynthetic pathway. CONCLUSIONS: These results suggest that high anthraquinone content was observed in the lowest-latitude cultivation area due to low rainfall and alkaline soil pH. Key metabolites were significantly upregulated in high-latitude cultivation areas. These results provide a scientific basis for quality control and the systematic cultivation of R. tanguticum.

Mutual influence of non-covalent interactions formed by imidazole: A systematic quantum-chemical study.

Imidazole is a five-membered heterocycle that is part of a number of biologically important molecules such as the amino acid histidine and the hormone histamine. Imidazole has a unique ability to participate in a variety of non-covalent interactions involving the NH group, the pyridine-like nitrogen atom or the π-system. For many biologically active compounds containing the imidazole moiety, its participation in formation of hydrogen bond NH⋯O/N and following proton transfer is the key step of mechanism of their action. In this work a systematic study of the mutual influence of various paired combinations of non-covalent interactions (e.g., hydrogen bonds and π-interactions) involving the imidazole moiety was performed by means of quantum chemistry (PW6B95-GD3/def2-QZVPD) for a series of model systems constructed based on analysis of available x-ray data. It is shown that for considered complexes formation of additional non-covalent interactions can only enhance the proton-donating ability of imidazole. At the same time, its proton-accepting ability can be both enhanced and weakened, depending on what additional interactions are added to a given system. The mutual influence of non-covalent interactions involving imidazole can be classified as weak geometric and strong energetic cooperativity-a small change in the length of non-covalent interaction formed by imidazole can strongly influence its strength. The latter can be used to develop methods for controlling the rate and selectivity of chemical reactions involving the imidazole fragment in larger systems. It is shown that the strong mutual influence of non-covalent interactions involving imidazole is due to the unique ability of the imidazole ring to effectively redistribute electron density in non-covalently bound systems with its participation.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.

IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data.

Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.

Mechanistic Insights into Sc(III)-Catalyzed Asymmetric Homologation of Ketones with Diazo Compounds: How Trans Influence Assists in Controlling Stereochemistry.

Asymmetric one-carbon homologation or ring expansion of ketones with formal insertion of carbene intermediate, is a challenging but useful strategy to construct a complex skeleton. Sc(III) and chiral ligands have been employed in this regard. However, due to flexible conformations and a variety of stereo models, the origin of stereochemistry remains ambiguous. Density functional theory (DFT) calculations were carried out to explore the interactions that control the stereoselectivity of a Sc(III)-catalyzed asymmetric homologation. The trans influence of counterions was found to affect the coordination mode of ketone to Sc(III), and consequently affect the stereoselectivity.

Parental Nurturance Moderates the Etiology of Youth Resilience.

Parenting behaviors are among the most robust predictors of youth resilience to adversity. Critically, however, very few studies examining these effects have been genetically-informed, and none have considered parenting as an etiologic moderator of resilience. What's more, despite the multidimensionality of resilience, extant etiologic literature has largely focused on a single domain. The current study sought to fill these respective gaps in the literature by examining whether and how parental nurturance shapes the etiology of academic, social, and psychological resilience, respectively. We employed a unique sample of twins (N = 426 pairs; ages 6-11) exposed to moderate-to-severe levels of environmental adversity (i.e., family poverty, neighborhood poverty, community violence) from the Twin Study of Behavioral and Emotional Development in Children. As expected, parental nurturance was positively correlated with all forms of resilience. Extended univariate genotype-by-environment interaction models revealed that parental nurturance significantly moderated genetic influences on all three domains of resilience (academic resilience A1= -0.53, psychological resilience A1= -1.22, social resilience A1= -0.63; all p < .05), such that as parental nurturance increased, genetic influences on youth resilience decreased. Put another way, children experiencing high levels of parental nurturance were more resilient to disadvantage, regardless of their genetic predisposition towards resilience. In the absence of nurturing parenting, however, genetic influences played an outsized role in the origins of resilience. Such findings indicate that parental nurturance may serve as a malleable protective factor that increases youth resilience regardless of genetic influences.

From parents to children: associations of traffic risks with impulsivity, family relationships and serotonin transporter genotype.

Road traffic injuries are the leading cause of death for young adults, and parents play a major role in shaping their traffic behaviour. Higher impulsivity (predictor of higher traffic risk) has been shown to be dependent on family relations and the serotonin transporter gene promoter polymorphism (5-HTTLPR). The specific mechanisms for the inheritance of risky traffic behaviour from parents to children are not clear, and the genetic aspect has not been studied before. We used data of Estonian Children Personality Behaviour and Health Study subjects (n = 596, mean age = 25.2 ± 0.6) and their parents (mothers, n = 460, mean age = 52.1 ± 5.8; fathers, n = 339, mean age = 54.1 ± 6.5). Family relationships scale, traffic risk questionnaires and Adaptive and Maladaptive Impulsivity Scale were filled out. The increased risk-taking behaviour of parents and worse quality of family relationship were significant predictors of higher traffic risk among subjects. Family support and impulsivity of fathers significantly predicted the subjects' traffic risk score in interaction with 5-HTTLPR genotype: l'/l' homozygous subjects with adaptively impulsive fathers had higher traffic risk, whereas for s'-allele carrying subjects family support was more significant. Parental role modelling and family relationships are significant predictors of future traffic behaviour of the child. Whether the behavioural example of the father or the influence of family relationships is more important in predicting future risky traffic behaviour, depends on the 5-HTTLPR genotype of the child.

Recent advances in the contribution of glucosinolates degradation products to cruciferous foods odor: factors that influence degradation pathways and odor attributes.

As one of the most important vegetables and oils consumed globally, cruciferous foods are appreciated for their high nutritional value. However, there is no comprehensive knowledge to sufficiently unravel the "flavor mystery" of cruciferous foods. The present review provides a comprehensive literature on the recent advances regarding the contribution of glucosinolates (GSL) degradation products to cruciferous foods odor, which focuses on key GSL degradation products contributing to distinct odor of cruciferous foods (Brassica oleracea, Brassica rapa, Brassica napus, Brassica juncea, Raphanus sativus), and key factors affecting GSL degradation pathways (i.e., enzyme-induced degradation, thermal-induced degradation, chemical-induced degradation, microwave-induced degradation) during different processing and cooking. A total of 93 volatile GSL degradation products (i.e., 36 nitriles, 33 isothiocyanates, 3 thiocyanates, 5 epithionitriles, and 16 sulfides) and 29 GSL (i.e., 20 aliphatic, 5 aromatic, and 4 indolic) were found in generalized cruciferous foods. Remarkably, cruciferous foods have a distinctive pungent, spicy, pickled, sulfur, and vegetable odor. In general, isothiocyanates are mostly present in enzyme-induced degradation of GSL and are therefore often enriched in fresh-cut or low-temperature, short-time cooked cruciferous foods. In contrast, nitriles are mainly derived from thermal-induced degradation of GSL, and are thus often enriched in high-temperature, long-time cooked cruciferous foods.

Processing and cooking can cause degradation of glucosinolates and formation of volatiles.Structure­odor relationship of glucosinolates degradation products is discussed.Nitriles, isothiocyanates, and sulfides play an important role in cruciferous foods odor.Both enzyme- and thermal-induced degradation of glucosinolates is strongly pH-dependent.

Using instrumental variables to address unmeasured confounding in causal mediation analysis.

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding. However, in contrast to the rich literature on the use of IV methods to identify and estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy to identify mediational indirect effects. In response, we define and nonparametrically identify novel estimands-double complier interventional direct and indirect effects-when 2, possibly related, IVs are available, one for the exposure and another for the mediator. We propose nonparametric, robust, efficient estimators for these effects and apply them to a housing voucher experiment.

Doubly robust estimation and sensitivity analysis for marginal structural quantile models.

The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.

Culturable yeast diversity in urban topsoil influenced by various anthropogenic impacts.

In urban ecosystems, processes associated with anthropogenic influences almost always lead to changes in soil micromycete complexes. The taxonomic structure of soil micromycete complexes is an important informative parameter of soil bioindication in the ecological control of urban environments. Unicellular fungi, such as culturable yeasts, are a very suitable and promising object of microbiological research for monitoring urban topsoil. This review aims to give an overview of the yeast communities in urban topsoil in different areas of Moscow (heating main area, household waste storage and disposal area, highway area) and to discuss the changes in the taxonomic structure of culturable yeast complexes depending on the type and intensity of anthropogenic impact.

Using Eye tracking to Examine Young Adults' Visual Attention to E-cigarette Advertising Features and Associated Positive E-cigarette Perceptions.

BACKGROUND: Little is known about the influence of e-cigarette marketing features on the antecedents of e-cigarette use. PURPOSE: Using an eye-tracking experiment, we examined visual attention to common features in e-cigarette ads and its associations with positive e-cigarette perceptions among young adults. METHODS: Young adults (ages 18-29) who smoke cigarettes (n = 40) or do not use tobacco (n = 71) viewed 30 e-cigarette ads on a computer screen. Eye-tracking technology measured dwell time (fixation duration) and entry time (time to first fixation) for 14 pre-defined ad features. Participants then completed a survey about perceptions of e-cigarettes shown in the ads. We used regression models to examine the associations between ad features and standardized attention metrics among all participants and by tobacco-use status and person-aggregated standardized attention for each ad feature and positive e-cigarette perceptions. RESULTS: Dwell time was the longest for smoker-targeted claims, positive experience claims, and price promotions. Entry time was the shortest for multiple flavor descriptions, nicotine warnings, and people. Those who do not use tobacco had a longer dwell time for minor sales restrictions and longer entry time for purchasing information than those who smoke. Longer dwell time for multiple flavor descriptions was associated with e-cigarette appeal. A shorter entry time for fruit flavor description was associated with positive e-cigarette-use expectancies. CONCLUSIONS: Young adults allocated attention differently to various e-cigarette ad features, and such viewing patterns were largely similar by tobacco-use statuses. Multiple or fruit flavors may be the features that contribute to the positive influence of e-cigarette marketing among young adults.

E-cigarette marketing exposure is associated with e-cigarette use among young adults. However, little is known about the influence of e-cigarette marketing features among this population. This study used eye-tracking technology to objectively measure dwell time and entry time for 14 pre-defined e-cigarette ad features. Young adults (ages 18­29) who smoke cigarettes (n = 40) or do not use tobacco (n = 71) viewed 30 e-cigarette ads on a computer screen and completed an online survey about positive e-cigarette perceptions. The study found that dwell time was the longest for smoker-targeted claims, positive experience claims, and price promotions. Entry time was the shortest for multiple flavor descriptions, nicotine warnings, and people. Those who do not use tobacco had a longer dwell time for minor sales restrictions and longer entry time for purchasing information than those who smoke. Longer dwell time for multiple flavor descriptions was associated with e-cigarette appeal. A shorter entry time for fruit flavor description was associated with positive e-cigarette-use expectancies. The results suggest that young adults allocated attention differently to various e-cigarette ad features, and such viewing patterns were largely similar by tobacco-use statuses. Multiple or fruit flavors may be the features that contribute to the positive influence of e-cigarette marketing among young adults.

Countering online marketing and user endorsements with enhanced cannabis warning labels: An online experiment among at-risk youth and young adults.

As cannabis legalization expands and online marketing intensifies, this study examines whether online social cues can amplify youth-targeted cannabis advertising and whether cannabis warning labels (CWLs) can counteract these influences. A U.S. online sample of 970 adolescents and 1776 young adults susceptible to cannabis use were recruited from Qualtrics in summer 2022. Each participant was randomly assigned to one of the 3 (CWLs: none vs. textual vs. pictorial) by 3 (comments: none vs. anti-cannabis vs. pro-cannabis) conditions in an online experiment. Participants were exposed to three online marketing posts promoting marijuana edibles (randomly selected from a large pool, N = 1260), each with either no warning label, a textual warning, or a pictorial warning (text and picture), and with either five comments (pro- or anti-cannabis in valence) or none. Results showed that among adolescents, pro-cannabis comments increased product appeal (vs. anti-cannabis comments: b = 0.18, p = .025; vs. no comments: b = 0.21, p = .021), and did so more than young adults. For adolescents, only pictorial warnings reduced product appeal (b = -0.20, p = .028). For young adults, both pictorial (b = -0.18, p = .002) and textual warnings (b = -0.12, p = .029) reduced product appeal. Furthermore, both textual (adolescents: b = -0.20, p = .004; young adults: b = -0.15, p = .005) and pictorial (adolescents: b = -0.30, p < .001; young adults: b = -0.18, p = .001) warnings reduced cannabis use intentions. Findings support requiring enhanced CWLs accompany online marketing ads.