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Using national data from Project Implicit, the authors examine state-level variations in implicit and explicit bias against Asian Americans held by non–Asian Americans (n = 196,678) from 2018 to 2022. The authors also explore state-level sociodemographic correlates of both types of bias. The findings reveal considerable heterogeneity in implicit and explicit bias across states. Moreover, Republican and swing states had higher levels of implicit bias against Asian Americans, and states with older median ages and greater percentages of Asian populations were associated with less explicit bias. This study underscores the importance of state-level variation in and structural factors of biases against Asian Americans as contexts for examining attitudes toward Asian Americans.

The authors visualize how implicit racial bias at the collective level varies across European countries and how it changed between 2009 and 2019. To obtain population-level estimates, the authors re-weight implicit association test data from the Project Implicit international dataset (n = 184,745). Implicit bias against Black people is stronger in southern and eastern vis-à-vis northern and western European countries. It decreased until the mid-2010s but started to increase thereafter.

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86–88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

In recent decades, the rapid development of metaheuristic algorithms has outpaced theoretical understanding, with experimental evaluations often overshadowing rigorous analysis. While nature-inspired optimization methods show promise for various applications, their effectiveness is often limited by metaphor-driven design, structural biases, and a lack of sufficient theoretical foundation. This paper systematically examines the challenges in developing robust, generalizable optimization techniques, advocating for a paradigm shift toward modular, transparent frameworks. A comprehensive review of the existing limitations in metaheuristic algorithms is presented, along with actionable strategies to mitigate biases and enhance algorithmic performance. Through emphasis on theoretical rigor, reproducible experimental validation, and open methodological frameworks, this work bridges critical gaps in algorithm design. The findings support adopting scientifically grounded optimization approaches to advance operational applications.

In the evolving cybersecurity landscape, detecting Thai-English code-mixed malicious scripts within high-trust domains such as governmental and academic portals presents a significant defensive challenge. While Transformer-based architectures excel in semantic parsing, they often exhibit ‘Structural Bias,’ misinterpreting the high-entropy syntax of benign legacy HyperText Markup Language (HTML) as malicious obfuscation due to inherent ‘Attention Deficit’ in token-limited models. To address this, we propose an Explainable AI (XAI)-Driven Hybrid Architecture grounded in a ‘Selective Integration’ strategy. Unlike traditional hybrid models, our framework mathematically formalizes the fusion process by synergizing context-aware WangChanBERTa embeddings with orthogonal structural statistics through Dempster-Shafer Theory and Conditional Mutual Information (CMI). The proposed model was validated on a high-fidelity corpus, achieving a state-of-the-art F1-score of 0.9908, significantly outperforming standalone Transformers, Random Forest, and unsupervised baselines. XAI diagnostics revealed a ‘Dual-Validation’ mechanism where structural features act as an epistemic anchor. This mechanism effectively triggers a ‘Semantic Veto’ to filter hallucinations caused by benign complexity, achieving a remarkably low False Positive Rate (FPR) of 0.0116. Our findings demonstrate that hybridization is most effective when engineered features provide mathematical orthogonality to semantic embeddings. This work offers a robust, theoretically grounded framework for securing critical digital infrastructures in low-resource linguistic environments.

Efforts to create a sustainable future require careful and complex thinking, interdisciplinary and cross-organizational collaboration, and effective and ethical communication. However, the structural biases of digital communication technologies foster modes of thought and expression that undermine or impede these necessities. While one possible solution to this problem is digital literacy, the two prevailing paradigms of digital literacy both reproduce the myth of technological neutrality. This myth further inhibits sustainability by wrongly suggesting that digital technologies are appropriate to all communication goals and tasks. As a corrective to these models, I propose a new paradigm of digital literacy, one rooted in media ecology. The adoption of this model, I maintain, allows us to consciously co-create our social world rather than merely inhabit it.

BACKGROUND:Structural selection bias and confounding are key threats to validity of causal effect estimation. Here, we consider M-bias, a type of selection bias, described by Hernán et al as a situation wherein bias is caused by selecting on a variable that is caused by two other variables, one a cause of the exposure, the other a cause of the outcome. Our goals are to derive a bound for (the maximum) M-bias, explore through examples the magnitude of M-bias, illustrate how to apply the bound for other types of selection bias, and provide a program for directly calculating M-bias and the bound. METHODS:We derive a bound for selection bias assuming specific, causal relationships that characterize M-bias and further evaluate it using simulations. RESULTS:Through examples, we show that, in many plausible situations, M-bias will tend to be small. In some examples, the bias is not small–but plausibility of the examples, ultimately to be judged by the researcher, may be low. The examples also show how the M-bias bound yields bounds for other types of selection bias and also for confounding. The latter illustrates how Lee’s bound for confounding can arise as a limiting case of ours. CONCLUSIONS:We have derived a new bound for M-bias. Examples illustrate how to apply it with other types of selection bias. They also show that it can yield tighter bounds in certain situations than a previously published bound for M-bias. Our examples suggest that M-bias may often, but not uniformly, be small.

A recent nationwide cohort study reported an unadjusted Hazard Ratio (HR) of 2.714 for Vitiligo incidence following COVID-19 vaccination, indicating a major safety concern. This finding was based on cohorts with an ≈ 11-year age difference, immediately raising critical concerns regarding extreme structural selection and detection bias. We hypothesize that this extreme association is an artifact of a fatal methodological flaw, challenging the study's internal validity and subsequent external validity. We aim to quantitatively separate the HR attributable to the structural age imbalance (HR Structural) from the residual HR (HR Residual), which quantifies the uncorrected methodological failure and residual confounding. We further perform a plausible recalculation of risk to demonstrate the complete collapse of the risk signal upon correcting the methodological failure in the baseline cohort. We performed a direct age-standardization analysis analysis using the age distribution of the scrutinized study's cohorts (Vaccinated, mean age = 56.32 years vs. Non-Vaccinated, mean age = 45.51 years) and applied established national age-specific Vitiligo incidence rates (IR) from external epidemiology. The HR Structural was calculated to be 1.2104. The remaining HR Residual of 2.2423 quantifies the uncorrected methodological failure. The NV cohort's observed incidence rate (0.67/10,000) was found to be nearly 70% lower than the expected rate (2.2146/10,000), providing quantifiable evidence of profound non-comparability. The subsequent recalculation of risk, correcting for this baseline failure, reduces the observed HR of = 2.714 to an HR Corrected of 1.0025, thus completely annulling the signal of risk due to vaccination. The HR = 2.714 of the scrutinized study is an unstable statistical artifact. The overwhelming majority of the observed association is a consequence of a fatal design flaw. The HR Corrected of almost 1 confirms that correcting the methodological error eliminates the risk signal, demonstrating a severe lack of internal and external validity of the original study.

The study aims to provide data on authors’ gender distribution with special attention on publications from Europe. Articles (October 2019–March 2020) published in three representative neurosurgical journals (Acta Neurochirurgica, Journal of Neurosurgery, Neurosurgery) were analyzed with regard to female participation. Out of 648 publications, 503 original articles were analyzed: 17.5% (n = 670) of the 3.821 authors were female, with 15.7% (n = 79) females as first and 9.5% (n = 48) as last authors. The lowest ratio of female first and last authors was seen in original articles published in the JNS (12.3%/7.7% vs. Neurosurgery 14.9%/10.6% and Acta 23.0/11.5%). Articles originated in Europe made up 29.8% (female author ratio 21.1% (n = 226)). Female first authorship was seen in 20.7% and last authorship in 10.7% (15.3% and 7.3% were affiliated to a neurosurgical department). The percentages of female authorship were lower if non-original articles (n = 145) were analyzed (11.7% first/4.8% last authorships). Female participation in editorial boards was 8.0%. Considering the percentages of European female neurosurgeons, the current data are proportional. However, the lack of female last authors, the discrepancy regarding non-original articles and the composition of the editorial boards indicate that there still is a structural underrepresentation and that females are limited in achieving powerful positions.

The prediction of protein structure from sequence remains a major unsolved problem in biology. The most successful protein structure prediction methods make use of a divide-and-conquer strategy to attack the problem: a conformational sampling method generates plausible candidate structures, which are subsequently accepted or rejected using an energy function. Conceptually, this often corresponds to separating local structural bias from the long-range interactions that stabilize the compact, native state. However, sampling protein conformations that are compatible with the local structural bias encoded in a given protein sequence is a long-standing open problem, especially in continuous space. We describe an elegant and mathematically rigorous method to do this, and show that it readily generates native-like protein conformations simply by enforcing compactness. Our results have far-reaching implications for protein structure prediction, determination, simulation, and design.