Browse Articles

Inequality aversion and prosocial punishment: Evidence from a one-shot public goods game

The willingness to engage in costly punishment of free riders (prosocial punishment) is crucial to foster group cooperation and understand public goods provision. While prosocial punishment is common across societies, its motivations remain unclear. Scholars have suggested that people resist inequitable outcomes and willingly bear costs to sanction free riders, seeking a fairer distribution of payoffs. This study tests a key implication of such fairness-driven arguments: if inequality aversion drives prosocial punishment, individuals should punish less when redistribution occurs, as equality concerns would be already satisfied. We conducted a pre-registered 2x2 between-subjects lab experiment (N=320), where participants completed a Social Value Orientation (SVO) task and played a one-shot Public Goods Game (PGG) with a Punishment Stage. We manipulated endowment inequality and the presence of redistributive taxation. Pre-registered analyses show that (1) inequality aversion does not predict prosocial punishment; (2) punishment levels do not significantly differ across treatments. However, exploratory results suggest that under high inequality, redistribution reduces the intensity of punishment towards richer individuals. This could indicate that inequality aversion triggers prosocial punishment only at acute inequality levels.

Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

Integration of multi-modal monitoring for dynamic control of large-scale 3D tissue bioreactors

Unbiased inference for echocardiogram urgency prediction using double machine learning

The increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography tests, challenges persist in efficiently prioritizing patients for echocardiographic assessments. In this study, we develop a model to assess the urgency of appointments by considering both clinical and administrative variables extracted from Electronic Health Record data. We use double machine learning techniques to analyze these variables and improve our predictions of patient urgency. Traditional methods for estimating variable effects have limitations, particularly in our research context, where clinical and administrative variables may influence one another while also directly impacting the outcome (i.e., the urgency of appointments). In this work, we address this issue by developing an urgency stratification model using double machine learning, which disentangles the complex relationships between variables. Our evaluations demonstrate that the proposed model not only outperforms traditional machine learning methods in predicting appointment urgency but also provides robust estimations of variable effects. Specifically, our results underscore the critical roles of administrative variables and cancer-related comorbidity variables in patient prioritization and appointment urgency prediction. By leveraging double machine learning techniques, our method can enhance the efficiency and effectiveness of echocardiography utilization in clinical practice. It provides clinicians with actionable insights for patient prioritization, facilitating the timely identification of urgent cases and the optimal allocation of resources. Our work contributes to the advancement of healthcare practices by leveraging sophisticated analytics to improve patient care delivery and streamline clinical workflows in echocardiography laboratories. A similar research design can also be extended to other advanced yet limited laboratory tests to help prioritize medical resources.

Computational design of dynamic biosensors for emerging synthetic opioids

Generative artificial intelligence heuristic cues, trust and continuous intention of CBeC platform and the moderating role of information overload

Omnichannel pricing and inventory strategies considering live streaming selling: A data-driven distributionally robust optimization approach

Recently, Live Streaming Selling (LSS) has become increasingly prevalent. Numerous omnichannel retailers are striving to introduce live streaming channel to absorb additional demand. However, it is challenging to investigate robust pricing and inventory strategies that consider the characteristics of omnichannel operations and LSS with uncertain demand. We consider a joint optimization of ordering, replenishment, order fulfillment, and pricing, where customers are sensitive to prices and delivery times. LSS can influence demand and benefit other channels to take free-riding. Furthermore, service level requirements are formulated as joint chance constraints to guarantee adequate performance. The Worst-case Mean Quantile-Deviation (WMQD) is employed to measure risks. The Wasserstein metric is adopted to design the data-driven ambiguity set. Accordingly, a data-driven Distributionally Robust Joint Chance Constrained Programming (DRJCCP) based on WMQD is constructed. Leveraging the dual theory, Conditional Value-at-Risk (CVaR) approximation, and linearization techniques, the developed model can be transformed into tractable formulations, which can be solved by commercial solvers. We further conduct numerical experiments to demonstrate the efficiency and practicality of our developed model. The comparative results reveal that the DRJCCP model based on WMQD has superior out-of-sample performance and is capable of effectively managing uncertainty, thereby ensuring more robust service levels. Furthermore, the sensitivity analyses are performed to verify the effects of some key parameters on the decision-making. The results indicate that introducing live streaming channel is not always profitable for the retailer and increasing the level of LSS effort can enhance free-riding effect without necessarily improving retailer’s profits.

The dorsal aortic compartment is a developmental source of brown adipose tissue in mice

Abstract White adipose tissue primarily stores energy while brown adipose tissue dissipates energy as heat, holding promise for therapeutic use. Brown adipose tissue in the anterior trunk is believed to derive from the somitic mesoderm, although some depots are of partially unknown origin. Here we show that the subscapular, lateral, cervical and peri-aortic brown adipose depots, but not the interscapular depot, are in part formed by a non-somitic source. Single-cell sequencing along with genetic lineage tracing indicates that at embryonic day 9.5 the dorsal aorta compartment harbors multipotent mesenchymal progenitors expressing the transcription factor Osr1. Spreading laterally from the dorsal aortic midline, these cells contribute to adipose, cartilage and myogenic lineages. This study uncovers an alternative source of brown adipose tissue and suggests that a fraction of dorsal aorta-associated mesenchymal Osr1 + cells may represent the in vivo correlate of a multipotent progenitor cell type so far only characterized in vitro, the mesoangioblast.

Unraveling storm wave populations in the UAE with multivariate and clustering analysis

Retraction: Inhibition of RIP1-RIP3-mediated necroptosis attenuates renal fibrosis via Wnt3α/β-catenin/GSK-3β signaling in unilateral ureteral obstruction

Cerebellar tDCS Modulates Corticocortical Functional Networks in a Regionally Specific Manner

With extensive interconnections with the cerebral cortex, the cerebellum is well positioned to coordinate communication between cortical regions. Because different cerebellar subregions interconnect with distinct cortical networks, the impact of regional cerebellar activity should be network-specific. However, it is unclear whether or how cerebellar modulation impacts the functional connectivity (FC) of cerebral cortical networks. To test this, we randomly assigned adults ( n  = 33; 21.2 ± 3.1 years; 22 M/11 F) to undergo 20 min of 1.5 mA transcranial direct current stimulation (tDCS) targeting either the posterior midline ( n  = 17) or right posterolateral cerebellum ( n  = 16). Each participant received anodal (excitatory), cathodal (inhibitory) or sham tDCS during separate MRI sessions. We analyzed post-tDCS resting-state fMRI data to determine whether modulating different cerebellar subregions impacted resting-state FC of distinct cortical networks. Multivariate pattern analyses revealed that posterior midline tDCS primarily modulated FC in the default mode network (DMN), while posterolateral cerebellar tDCS altered FC in the frontoparietal network (FPN). Seed-based connectivity analyses confirmed that posterior midline modulation increased within-network DMN FC while decreasing FC between DMN, visual, and somatomotor networks. In contrast, posterolateral cerebellar tDCS strengthened frontoparietal and attentional network FC while decoupling FPN–DMN and FPN–visual networks. These results support the hypothesis that the cerebellum modulates corticocortical connectivity and further suggest that the posterior midline modulates the DMN, while the posterolateral cerebellum shifts the brain toward a task-ready cognitive state. These findings provide insight into how the cerebellum influences the cerebral cortex and have clinical implications for targeted interventions in neurological and psychiatric conditions.

Efficacy and safety of low-dose interleukin 2 for Behçet’s syndrome: a randomized, placebo-controlled, double-blind, phase 2 clinical trial

The effect of the design of the irrigation needle used during endodontic treatment on postoperative pain: a randomize clinical trial

Mechanochemical interactions in cancer cells: The role of substrate stiffness in cell behavior and drug response

Cancer cells adhere to the extracellular matrix, where they sense and respond to variations in substrate stiffness, influencing their proliferation and invasive potential. Numerous studies have examined the biological activities of cells in relation to mechanical forces; however, research addressing the combined effects of mechanical and chemical interactions on cancer cell behavior across different metastatic stages remains limited. Moreover, the influence of chemotherapeutic drugs in the context of specific cellular characteristics remains underexplored. Therefore, in this study, synthetic polyacrylamide gels with varying elastic moduli were utilized to effectively mimic the diversity of host tissue environments for prostate cancer cells. Additionally, cellular behavior of prostate cancer cells with differing metastatic potential—low (LNCaP), medium (DU145), and high (PC3)—was evaluated in response to anticancer drugs. Ultimately, effects of drug treatment were comprehensively examined using Docetaxel, Bicalutamide, and Abiraterone Acetate, which target distinct cellular components and activate diverse signaling pathways. The assessments were based on the analysis of actin filament content and organization, size of nucleus, and cellular elastic modulus. The results revealed that a soft substrate improves the medication efficacy, resulting in an enhanced cell death rate of 40–60% compared to 20–30% on a stiff substrate. Cells cultured on soft substrates exhibited lower phalloidin content (8–16%) compared to those on stiff substrates (18–32%). Additionally, drug treatments influenced cell mechanics, with Docetaxel reducing the elastic modulus, while Bicalutamide induced an increase. Based on these findings, a treatment strategy aimed at enhancing therapeutic efficacy can be proposed.

Rapid photocontrollable dopamine polymerization for instant adaptive wet adhesion

Transcriptional response of transposable elements to thermal stress in the Antarctic fish Trematomus bernacchii

Abstract Global change and the associated increase in temperature raise serious concerns for the conservation of Antarctic marine biodiversity, which is particularly vulnerable due to the stenothermal nature and highly specialized adaptations of its fauna. Trematomus bernacchii (commonly named emerald rockcod), a Southern Ocean-endemic benthic fish, serves as a valuable model organism for investigating the molecular and physiological impacts of climate change in polar ecosystems. Transposable elements (TEs) are of particular interest, as they are known to become activated under stress and to influence genome plasticity and gene regulation. In this study, we examined the transcriptional response of TEs and their silencing mechanisms in the gills and liver of T. bernacchii specimens exposed to thermal stress (+ 1 °C and + 3 °C compared to a 0 °C control) for 5 and 15 days. Our results showed that temperature increase triggered a transient activation of TEs, followed by the upregulation of silencing-related genes, including members of the Argonaute family, heterochromatin-associated factors, and components of the NuRD complex. Tissue-specific patterns were observed: the liver exhibited a rapid balance between TE activation and silencing, indicating a coordinated and resilient response, while the gills showed a sustained upregulation of both TEs and silencing genes, likely due to their greater sensitivity to environmental changes. These findings highlighted a complex, dynamic interplay between TEs and their regulatory systems under heat stress, offering new insights into early adaptive responses and potential resilience mechanisms in a cold-adapted species facing climate-induced biodiversity loss.

Physicochemical and microbiome changes in queso Crema de Chiapas during ripening

The dynamic changes in the physicochemical, microbiological, and metagenomic profiles of Crema de Chiapas cheese were evaluated across three ripening stages (2, 29, and 58 days). Although the main physicochemical properties —including fat content— remained remarkably stable, salt and protein levels showed noticeable variation throughout ripening. Protein content had the strongest influence on sample differentiation across ripening stages in unsupervised multivariate models, enabling the clustering of microbial diversity according to maturation time. A clear shift in microbial diversity was detected, marked by a reduction in bacterial genera and a concurrent decline in fungal and yeast populations as ripening advanced. The predominant bacterial genera throughout ripening were Streptococcus, Lactobacillus, and Lactococcus. While Streptococcus and Lactobacillus increased over time, Lactococcus exhibited the opposite trend. Metagenomic analysis revealed a decrease in Candida etchellsii and a concomitant increase in Candida tropicalis as ripening progressed. Quantitative PCR (qPCR) confirmed the presence of C. etchellsii at T1 (Ct = 7.22) and C. tropicalis at T3 (Ct = 9.84). The presence of three additional bacterial genera—Chryseobacterium, Aeromonas, and Enterobacter—identified by next-generation sequencing (NGS), was also assessed by qPCR. Chryseobacterium was detected at T2 (Ct = 3.26), whereas Aeromonas and Enterobacter were absent across all stages. Collectively, these findings suggest that potentially pathogenic microorganisms were not present at biologically relevant levels.

Finite-momentum superconductivity from chiral bands in twisted MoTe2

Interplay between different cytotoxic parameters in Galleria mellonella (Lepidoptera, Pyralidae) larvae fed with polypropylene

Longer chronic cannabis use in humans is associated with impaired implicit motor learning and supranormal resting state cortical activity

Chronic cannabis use is associated with cognitive impairment, but its impact on implicit motor learning is unclear. Implicit learning of movement sequences (i.e., their specific ordinal and temporal structure) is vital for performing complex motor behavior and lays the foundation for performing daily activities and interacting socially. We collected data from 30 individuals who used cannabis regularly and 32 individuals who did not use cannabis. We utilized the serial reaction time task to assess implicit motor sequence learning and the Corsi block-tapping test to assess visuospatial short-term and working memory. We also recorded resting state electroencephalography (EEG) to measure resting cortical activity. While implicit motor learning was evident at the group level, longer cannabis use was associated with a smaller index of motor learning and increased activity in beta and gamma EEG frequencies during resting state. The cannabis group also had a significantly shorter Corsi span (in both forward and backward conditions). These findings indicate that longer chronic cannabis use is associated with impaired implicit motor learning that may be a function of increased resting state neural oscillatory activity, resulting in increased cortical noise, and reduced visuospatial short-term and working memory. These findings suggest that chronic cannabis use may disrupt corticostriatal pathways that underlie implicit motor sequence learning, indicating a more extensive effect of cannabis on the motor system.