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Cooperation is a fundamental aspect of social and biological systems, yet achieving and maintaining high levels of cooperation remains a significant challenge. This study investigates the dynamics of cooperation among players engaged in repeated two-player Prisoner’s Dilemma games, utilizing a novel integration of the Bush–Mosteller reinforcement learning model with adaptive network rewiring mechanisms. Each player updates its probability of cooperation and rewires its connections based on the payoffs received from neighbors. Our results demonstrate that incorporating network rewiring guided by reinforcement learning significantly enhances both the level of cooperation and the average payoff across the population. Players that prioritize rewiring over strategy updates are found to form more stable cooperative structures, while those with heightened sensitivity to payoffs and optimal aspiration levels achieve greater cooperation. By identifying and analyzing key parameters that influence cooperative dynamics, our findings provide deep insights into the mechanisms that drive cooperative behavior. This research not only highlights the transformative potential of adaptive network rewiring in promoting cooperation within complex adaptive systems but also offers a framework for designing resilient cooperative networks across diverse domains.

Abstract Context Atrial fibrillation (AF), cardiac arrhythmias, and related risk factors are common in patients with Cushing’s syndrome, or clinical chronic hypercortisolism. While hypercortisolism may be associated with AF, this association has not yet been ascertained causally. Objective To determine whether plasma cortisol is causally associated with AF using a 2-sample Mendelian randomization (MR) design. Methods Three genetic variants in the SERPINA1/SERPINA6 locus and functionally associated with plasma cortisol were identified in the CORtisol NETwork consortium (12 597 participants). Summary-level genome-wide association study (GWAS) data for the associations between the cortisol-associated variants and AF were obtained from a GWAS meta-analysis of 6 studies (60 620 AF cases and 970 216 noncases) and the FinnGen consortium (17 325 AF cases and 97 214 noncases). The fixed-effects inverse-variance weighted approach accounting for genetic correlations between variants was used for analysis. Multivariable MR analyses were conducted to assess potential mediating effects of systolic blood pressure (SBP) and waist circumference (WC). Summary-level GWAS data for SBP and WC were obtained respectively from the International Consortium of Blood Pressure (757 601 participants) and the Genetic Investigation of ANthropometric Traits consortium (232 101 participants). Results One standard deviation increase in genetically predicted plasma cortisol was associated with greater risk of AF (odds ratio [OR] 1.20, 95% CI 1.06-1.35). The association attenuated when adjusting for genetically predicted SBP and WC (OR 0.99, 95% CI 0.72-1.38). Conclusion Evidence derived from the MR study suggests a positive association between plasma cortisol and risk of AF, likely mediated through SBP and WC.

The iodide/triiodide redox shuttle has limited the efficiencies accessible in dye-sensitized solar cells. Here, we report mesoscopic solar cells that incorporate a Co (II/III) tris(bipyridyl)—based redox electrolyte in conjunction with a custom synthesized donor-π-bridge-acceptor zinc porphyrin dye as sensitizer (designated YD2-o-C8). The specific molecular design of YD2-o-C8 greatly retards the rate of interfacial back electron transfer from the conduction band of the nanocrystalline titanium dioxide film to the oxidized cobalt mediator, which enables attainment of strikingly high photovoltages approaching 1 volt. Because the YD2-o-C8 porphyrin harvests sunlight across the visible spectrum, large photocurrents are generated. Cosensitization of YD2-o-C8 with another organic dye further enhances the performance of the device, leading to a measured power conversion efficiency of 12.3% under simulated air mass 1.5 global sunlight.

Using a 10 week nationwide online survey performed during a time period containing the time ahead, the start, and the peak of a COVID-19 outbreak in Taiwan, we investigated aspects that could affect participants' vaccination intentions. From March to May 2021, we surveyed 1,773 people in Taiwan, aged from 20 to 75 years, to determine potential acceptance rates and factors influencing the acceptance of a COVID-19 vaccine. We used an ordinal logistic regression with a backward selection method to identify factors that affected vaccination intention. Several factors could increase individuals' vaccination intentions including: being male, older, with an openness personality, having a better quality of life in the physical health domain, having better knowledge and personal health behavior, having more trust in the government, and being worried about misinformation. Perceived risks played a crucial role in the vaccine decision-making process. When the pandemic intensified, people's vaccination intentions increased significantly. The findings of the present study could highlight individuals' vaccination attitudes and provide governments with an empirical and dynamic base to design tailored strategies to increase vaccination rates.

Background Cortisol’s immunosuppressive, obesogenic, and hyperglycaemic effects suggest that it may play a role in cancer development. However, whether cortisol increases cancer risk is not known. We investigated the potential causal association between plasma cortisol and risk of overall and common site-specific cancers using Mendelian randomisation. Methods Three genetic variants associated with morning plasma cortisol levels at the genome-wide significance level ( P  < 5 × 10 −8 ) in the Cortisol Network consortium were used as genetic instruments. Summary-level genome-wide association study data for the cancer outcomes were obtained from large-scale cancer consortia, the UK Biobank, and the FinnGen consortium. Two-sample Mendelian randomisation analyses were performed using the fixed-effects inverse-variance weighted method. Estimates across data sources were combined using meta-analysis. Results A standard deviation increase in genetically predicted plasma cortisol was associated with increased risk of endometrial cancer (odds ratio 1.50, 95% confidence interval 1.13–1.99; P  = 0.005). There was no significant association between genetically predicted plasma cortisol and risk of other common site-specific cancers, including breast, ovarian, prostate, colorectal, lung, or malignant skin cancer, or overall cancer. Conclusions These results indicate that elevated plasma cortisol levels may increase the risk of endometrial cancer but not other cancers. The mechanism by which this occurs remains to be investigated.

Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section.

This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving patterns of triadic structures, its effects on overall network dynamics remain underexplored. Existing research often neglects the interaction between micro-level balancing mechanisms and overall network behavior. To bridge this gap, we develop a method for detecting dynamic triadic structures in signed networks, categorizing triangle transformations over two consecutive periods into formation and breakage. We analyze the impact of these structures on temporal network evolution by incorporating them into exponential random graph models across five networks of varying size, density, and directionality. To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. These findings highlight the importance of considering the triadic transformation processes of balance triangles in studying temporal networks, validated across diverse datasets, warranting further research.

This paper introduces a new method for acquiring and interpreting data on cognitive (or perceptual) networks. The proposed method involves the collection of multiple reports on randomly chosen pairs of individuals, and statistical means for aggregating these reports into data of conventional sociometric form. We refer to the method as \"perceptual tomography\" to emphasize that it aggregates multiple 3rd-party data on the perceived presence or absence of individual properties and pairwise relationships. Key features of the method include its low respondent burden, flexible interpretation, as well as its ability to find \"robust intransitive\" ties in the form of perceived non-edges. This latter feature, in turn, allows for the application of conventional balance clustering routines to perceptual tomography data. In what follows, we will describe both the method and an example of the implementation of the method from a recent community study among Alaska Natives. Interview data from 170 community residents is used to ascribe 4446 perceived relationships (2146 perceived edges, 2300 perceived non-edges) among 393 community members, and to assert the perceived presence (or absence) of 16 community-oriented helping behaviors to each individual in the community. Using balance theory-based partitioning of the perceptual network, we show that people in the community perceive distinct helping roles as structural associations among community members. The fact that role classes can be detected in network renderings of \"tomographic\" perceptual information lends support to the suggestion that this method is capable of producing meaningful new kinds of data about perceptual networks.

In observational studies, venous thromboembolism (VTE) has been associated with Cushing's syndrome and with persistent mental stress, two conditions associated with higher cortisol levels. However, it remains unknown whether high cortisol levels within the usual range are causally associated with VTE risk. We aimed to assess the association between plasma cortisol levels and VTE risk using Mendelian randomization. Three genetic variants in the SERPINA1/SERPINA6 locus (rs12589136, rs11621961 and rs2749527) were used to proxy plasma cortisol. The associations of the cortisol-associated genetic variants with VTE were acquired from the INVENT (28 907 cases and 157 243 non-cases) and FinnGen (6913 cases and 169 986 non-cases) consortia. Corresponding data for VTE subtypes were available from the FinnGen consortium and UK Biobank. Two-sample Mendelian randomization analyses (inverse-variance weighted method) were performed. Genetic predisposition to higher plasma cortisol levels was associated with a reduced risk of VTE (odds ratio [OR] per one standard deviation increment 0.73, 95% confidence interval [CI] 0.62-0.87, p<0.001). The association was stronger for deep vein thrombosis (OR 0.69, 95% CI 0.55-0.88, p = 0.003) than for pulmonary embolism which did not achieve statistical significance (OR 0.83, 95% CI 0.63-1.09, p = 0.184). Adjusting for genetically predicted systolic blood pressure inverted the direction of the point estimate for VTE, although the resulting CI was wide (OR 1.06, 95% CI 0.70-1.61, p = 0.780). This study provides evidence that genetically predicted plasma cortisol levels in the high end of the normal range are associated with a decreased risk of VTE and that this association may be mediated by blood pressure. This study has implications for the planning of observational studies of cortisol and VTE, suggesting that blood pressure traits should be measured and accounted for.

Accurately predicting the power conversion efficiency (PCE) in dye‐sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This study presents precise, predictive, and interpretable machine learning (ML) models specifically designed for Zn‐porphyrin‐sensitized solar cells. The model leverages theoretically computable, effective, and reusable molecular descriptors (MDs) to address this challenge. The models achieve excellent performance on a “blind test” of 17 newly designed cells, with a mean absolute error (MAE) of 1.02%. Notably, 10 dyes are predicted within a 1% error margin. These results validate the ML models and their importance in exploring uncharted chemical spaces of Zn‐porphyrins. SHAP analysis identifies crucial MDs that align well with experimental observations, providing valuable chemical guidelines for the rational design of dyes in DSSCs. These predictive ML models enable efficient in silico screening, significantly reducing analysis time for photovoltaic cells. Promising Zn‐porphyrin‐based dyes with exceptional PCE are identified, facilitating high‐throughput virtual screening. The prediction tool is publicly accessible at https://ai‐meta.chem.ncu.edu.tw/dsc‐meta. Innovative High‐Throughput Design of Porphyrin‐Sensitized Solar Cells Through Interpretable Machine Learning