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Prejudice confrontations can be effective in reducing bias, but few studies have examined them in childhood. This study, in the United States, investigated 8- to 14-year olds' (N = 196; Mage = 11.64; 114 girls, 82 boys; 33.7% White, 27.6% Multiracial/Multiethnic, 12.2% Black, 8.2% Latino, 7.7% Asian, 4.5% Other, and 6.1% not reported) evaluations of, reasoning about, and confrontations of gender-based Science, Technology, Engineering, and Mathematics (STEM) inequalities within peer groups. Data were collected in 2024. Participants who evaluated inequalities more negatively were more likely to confront them. Evaluations of inequalities that disadvantaged boys, but not girls, were more positive with age. Finally, evaluations of inequalities uniquely predicted participants' use of different reasoning categories to justify their evaluations. These findings demonstrate children's and adolescents' capacities to make STEM contexts more equitable for all children. Although children often view gender-based exclusion and inequality as wrong, less is known about how they respond to it. In this study, children and adolescents viewed vignettes of mixed-gender peer groups who competed in science competitions together and only chose either girls or boys to be group leaders. Children evaluated a group’s decision to choose only girls to be leaders as more acceptable with age. Evaluations of a group’s decision to choose only boys to be leaders were negative and did not vary with age. Most children and adolescents directly confronted the groups’ biased choices for leader, and they were more likely to do so when they evaluated the biased choices negatively.

Social and emotional skills (SES) are key predictors of students' academic success and well-being, yet how these skills cluster and vary across student groups remains underexplored in non-Western contexts. This study identified distinct SES profiles among Turkish adolescents and examined which demographic characteristics and school climate factors are associated with these profiles, as well as their links to achievement in reading, mathematics and science. The analyses used nationally representative data from 7250 15-year-old students in Türkiye participating in PISA 2022. Latent profile analysis (LPA) was conducted using students' self-reported SES indicators. Multinomial logistic regression was used to examine predictors of profile membership, and achievement differences across profiles were tested using the Bolck-Croon-Hagenaars (BCH) method with pooled Wald chi-square tests. Five distinct SES profiles were identified: Empathic but Overwhelmed, Withdrawn and At-Risk, Average Majority, Assertive Explorers and Compassionate Collaborators. Gender and socioeconomic status were significant predictors: girls were more likely to belong to empathy- and collaboration-oriented profiles, while students from advantaged families were overrepresented in adaptive groups. Immigration-related variables showed limited significant relations. Strong teacher-student relationships and a sense of belonging were linked to higher SES profiles. The Assertive Explorers and Compassionate Collaborators achieved the highest scores across all subjects, whereas the Withdrawn and At-Risk group had the lowest. The findings demonstrate that adolescents' SES clusters into distinct, context-sensitive configurations that shape their academic performance. Tailored educational strategies that foster self-regulation, perseverance and a sense of belonging may enhance both emotional well-being and learning outcomes.

Digital surveys are increasingly integrated into clinical and public health research to capture patient-reported outcomes. However, concerns about fraudulent or duplicate responses threaten data integrity. Most of the literature on survey fraud focuses on open-access online recruitment, where bot-generated or anonymous entries are common, but far less is known about fraud patterns in clinic-linked, incentive-based surveys. Evaluations of the real-world implementation of fraud-deterrence strategies remain limited. This study evaluated whether implementing enhanced fraud-deterrence procedures in an incentive-based, clinic-linked, postprocedure survey reduced the prevalence of potentially fraudulent responses. Second, the study evaluated which indicators were most frequently triggered before and after implementation. This evaluation was conducted within the ADOPT (Alternatives to Dental Opioid Prescribing After Tooth Extraction) study. Eligible patients aged 12 to 25 years were recruited through QR-coded, clinic-distributed flyers and invitation cards. Participants completed a screening survey followed by an incentivized postprocedure survey between days 6 and 10 after tooth extraction. A midstudy protocol modification introduced enhanced fraud-deterrence measures in the screening process, including a phone number requirement, prohibition of email invitations, date of birth confirmation, and the use of a participant list with SMS text message invitations. For analysis, survey responses were categorized as "control" (before modification) or "intervention" (after modification). A 6-item scoring system assessing completion time outliers, submission time, repeated screeners, duplicated phone numbers, a blank recruitment source, and illogical response patterns was used to classify responses as potentially fraudulent (&#x2265;2 indicators). Sensitivity analyses evaluated thresholds from 1 to 3 indicators. A total of 573 survey responses were included, with 122 in the control cohort and 451 in the intervention cohort. The overall prevalence of potentially fraudulent responses (50/573, 8.7%) was lower than the rates reported in open-access online survey research, and the difference between the control and intervention cohorts was not statistically significant (15/122, 12.3% vs 35/451, 7.8%; P=.12). Fewer surveys in the intervention cohort were flagged for a blank recruitment source (16/451, 3.5% vs 15/122, 12.3%; P<.001) and completion of multiple screeners (29/451, 6.4% vs 16/122, 13.1%; P=.02). The frequency of duplicated phone numbers was higher in the intervention survey (82/451, 18.2% vs 3/122, 2.5%; P<.001), although this difference was not statistically significant when restricted to individuals who provided a phone number (82/451, 18.2% vs 3/46, 6.5%; P=.06). Sensitivity analyses showed consistent patterns across alternative thresholds, and subgroup analyses did not show overall differences in fraud rates based on age, sex, or recruitment location. Enhanced fraud-deterrence procedures did not statistically significantly reduce overall fraud prevalence in a survey setting using clinic-linked, QR-based recruitment with modest incentives. A transparent scoring system provides a replicable approach for assessing survey integrity and may be preferable to reliance on eligibility gating alone.

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Boolean networks are a widely used modeling framework in systems biology for studying gene regulation, signal transduction, and cellular decision-making. Empirical studies indicate that biological Boolean networks exhibit a high degree of canalization, a property of Boolean update rules that stabilizes dynamics and constrains state transitions. Despite its central role, existing software packages provide limited support for the systematic generation of Boolean functions and networks with prescribed canalization properties. We present BoolForge, a Python toolbox for the random generation and analysis of Boolean functions and networks, with a particular focus on canalization. BoolForge enables users to (i) generate random Boolean functions with specified canalizing depth, layer structure, and related constraints; (ii) construct Boolean networks with tunable topological and functional properties; and (iii) analyze structural and dynamical features including canalization measures, robustness, modularity, and attractor structure. By enabling controlled generation alongside analysis, BoolForge facilitates ensemble-based investigations of structure-dynamics relationships, benchmarking of theoretical predictions, and construction of biologically informed null models for Boolean network studies. BoolForge is implemented in Python (&#x2265;3.10) and can be installed via pip install boolforge. Source code and documentation are available at https://github.com/ckadelka/BoolForge. A comprehensive tutorial compendium is available as Supplementary Material at Bioinformatics online.

Single-cell sequencing has transformed our understanding of cellular heterogeneity, enabling the construction of multi-omics atlases through data integration. However, conventional atlas updates require full reintegration of all datasets, creating scalability challenges that limit the timeliness and adaptability of biomedical research. Here we present multimodal integration with continual learning (MIRACLE), an online learning framework for scalable multimodal integration. Using dynamic architecture adaptation and data rehearsal, MIRACLE continually integrates diverse datasets while preserving biological fidelity. Across evaluations, MIRACLE achieves accurate online integration with substantially improved efficiency, refining and expanding atlases with new cross-modal, cross-tissue and cross-disease data. Applied to respiratory infections, it reveals both shared and pathogen-specific immune mechanisms in coronavirus disease 2019, influenza A and tuberculosis. Overall, MIRACLE provides an efficient and collaborative solution for the continual integration, sharing and exploration of biological knowledge.

The preB&#xf6;tzinger Complex (preB&#xf6;tC) within the medulla oblongata contains neuronal circuits critical for generating the mammalian respiratory rhythm, but the functional connectivity among its core excitatory and inhibitory populations remains debated. Defining this connectivity requires disentangling synaptic interactions of functionally identified excitatory and inhibitory preB&#xf6;tC neurons with various electrophysiological phenotypes. We applied a novel synaptic conductance inference method to whole-cell recordings from genetically specified VgluT2-expressing (excitatory) and VGAT-expressing (inhibitory) preB&#xf6;tC neurons active in the rhythmic medullary slice in vitro, which contains core inhibitory-excitatory circuitry with an excitatory rhythmogenic kernel. We found that this circuitry consists of a self-exciting inspiratory VgluT2 population coupled to inspiratory and expiratory VGAT populations that interact reciprocally through inhibition. The functional inhibitory connectome is more complex than previously understood. However, compared with functional synaptic interactions inferred from recordings in the preB&#xf6;tC in situ, the neuronal synaptic conductance profiles in the rhythmic slice reveal a functionally reduced inhibitory connectome, characterized by prominent tonic expiratory inhibition and phasic inspiratory inhibition, without the characteristic multiphasic structure in situ. These results indicate that the functional excitatory and inhibitory circuit interactions within the preB&#xf6;tC isolated in vitro, although reduced relative to more intact states in situ, are intrinsically designed to generate coordinated inspiratory and expiratory population activity. Tonic expiratory phase inhibition together with inspiratory phasic inhibition serves to regulate excitability and phase transitions of the excitatory rhythmogenic kernel.

MRI detection of pituitary microadenomas in Cushing's disease is inconsistent, and its impact on surgical outcomes remains debated. Diagnostic performance may vary by reader expertise and interpretation strategy. The authors sought to assess interrater agreement and compare diagnostic accuracy across expert MRI interpretation conditions. In this retrospective study at a tertiary referral center, preoperative pituitary MRI from 90 patients with biochemically confirmed Cushing's disease who underwent surgery (2012-2022) were independently interpreted by a trained pituitary neuroradiologist and two experienced pituitary neurosurgeons. Interrater agreement (Cohen's &#x3ba;) and diagnostic performance were evaluated across 5 reading conditions: each reader individually and majority and unanimous voting among the 3 readers. The reference standard was adenoma identification at surgery. The authors calculated sensitivity/specificity-derived metrics, including balanced accuracy, area under the receiver operating characteristic curve (AUC-ROC), positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Three-month remission rates were compared between MRI-positive and MRI-negative interpretations within each reading condition using odds ratios (ORs) with 95% confidence intervals. Interrater agreement was fair to moderate (&#x3ba; = 0.34-0.44). Performance differed across reading conditions (global comparison, p < 0.001). The highest diagnostic performance was observed for one neurosurgeon and for unanimous voting (balanced accuracy 0.84 and 0.85; AUC-ROC 0.90 and 0.85, respectively). In these two conditions, an MRI-positive interpretation was associated with improved adenoma identification at surgery and greater 3-month remission compared with MRI-negative interpretations (PLR 4.38 and 15.35; NLR 0.16 and 0.32; OR for remission 12.38 and 10.07, respectively). In a surgically selected cohort with confirmed Cushing's disease, MRI localization of corticotroph microadenomas is strongly reader dependent. Consensus interpretation improves performance, and negative MRI are associated with lower remission rates, supporting optimization of preoperative imaging.

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The UK NHS faces increasing pressure to diminish its carbon footprint, with fluorinated anaesthetic agents representing a significant source of direct greenhouse gas emissions. Several NHS trusts retain substantial quantities of unused stock and waste volatile anaesthetics (VAs), particularly desflurane, requiring appropriate disposal. However, a crucial unquantified aspect is the endpoint destruction routes and associated emissions for accumulated desflurane stock and waste anaesthetics. We developed a mathematical model accompanied by an open-source Python-based application programming interface (API) to estimate equivalent carbon emissions and their climate impacts across disposal pathways. Stochastic modelling also estimates uncertainties of input parameters and their effects on model predictions. User-defined input parameters in the API allow practitioners to model specific destruction pathways. We identified high-temperature incineration in clinical waste streams as the most likely route, which, compared with release, shows reductions of more than 70% in equivalent emissions. Model predictions suggest that plasma destruction has the highest potential for reducing equivalent carbon emissions, to less than 5% from all waste fluorinated VAs by reducing the formation of products of incomplete combustion, with an associated reduction in climate impact. Crucial work still needs to be done to validate theoretical estimates, accurately assess the formation of products of incomplete combustion from fluorinated VAs under varying destruction conditions, and evaluate the feasibility of implementing the assessed waste streams.

Primary mitochondrial disease is a group of genetic disorders caused by pathogenic variants in nuclear or mitochondrial DNA, often resulting in progressive neurodegeneration and cognitive decline. Current management is primarily supportive, though recent research offers hope for disease-modifying treatments in the future. Selecting appropriate therapeutic outcomes for clinical trials in mitochondrial diseases is challenging due to limited sensitivity to changes, small sample sizes, and the burden of study related activities. This study aims to identify an efficient choice of cognitive endpoints for translational research. This study compared digital cognitive assessments with traditional paper-based tools. It included two cohorts: the Newcastle cohort of 45 patients recruited from the mitochondrial clinic Newcastle upon Tyne (UK) and the KHENERGYZE clinical trial cohort of 27 patients recruited from four European countries. Patients in the Newcastle cohort underwent two conventional cognitive assessments (Addenbrooke's Cognitive Examination and Montreal Cognitive Assessment), along with two computerized tests (Cogstate and Test of Attentional Performance). Potential confounding factors were also assessed. Both cohorts showed a high prevalence of moderate to severe perceived fatigue. Over 50% of patients showed reduced reaction times. Strong correlations were found between conventional and digital assessments. Several confounding factors such as education and employment were identified as influencing cognitive performance. The findings support the understanding of mitochondrial disease as a slowly progressive condition, where impaired cognitive function is evident even in patients in the absence of devastating CNS manifestations such as stroke-like episodes. Observed variability in cognitive performance may help detect meaningful changes over time.

We present a Laplace transform approach for explicitly solving linear delay differential systems with multiple discrete delays by applying the Cauchy residue theorem. This method enables direct determination of the stability of the trivial solution when delays are relatively small. Its efficacy is illustrated through two nonlinear models with two and three delays, respectively, for which explicit solutions and stability criteria are obtained. The approach offers two key advantages: (i) analytic solutions are obtained with less effort than the method of steps, and (ii) a small hyper-tetrahedron region in the delay parameter space can be identified in which the trivial solution is asymptotically stable. Furthermore, the results can be combined with existing theory, such as Lemma 3.10 in [1], to establish conditions for Hopf bifurcation. (Dedicated to Professor Shigui Ruan on the occasion of his 60th birthday).

Non-Markovian noise, arising from environmental memory effects, is the most general and challenging form of noise in quantum computing, and is typically difficult to characterize and suppress. Here, we analyze and experimentally demonstrate a non-Markovian noise suppression scheme inspired by quantum purification protocols. We theoretically prove that, even without noise calibration and assumptions on specific noise models, the scheme can exponentially reduce non-Markovian error rates with respect to the ancillary system size. We implement the protocol using nuclear spins, demonstrating that non-Markovian noise can be suppressed for both unitary operations and nonunitary channels. The observed fidelities and process tomography show close agreement with theoretical predictions, confirming the practicality and effectiveness of the scheme.

In this article, the generalized four-dimensional Lotka-Volterra model is studied. The model consists of four units coupled with excitatory or inhibitory couplings. It is shown that in the phase space of the model, there exists a heteroclinic network-a connected union of two or more heteroclinic cycles (see definition in the text). A partition of the plane of coupling's parameters into regions of the existence of various heteroclinic networks is constructed. The presence of a stable heteroclinic cycle in the phase space of neuronal models, including the model under consideration, can be considered as the implementation of the process of switching neuronal activity. It is shown that the system under study can exhibit multistability.

Elastography techniques to date have been limited in their ability to provide repeatable measurements across examiners, preventing the ability to compare within the same patient over time and across patients to define population norms. Innovation to define normal and abnormal biomechanical changes in the cervix during remodeling using methodology that is comparable across large populations of patients would facilitate significant clinical and scientific progress. The aim of this study was to test the ability of a novel quantitative, synchronized stress-strain cervical elastography system (FQ-CES Fully Quantitative Cervical Elastography System) to numerically quantify cervical remodeling in humans longitudinally over pregnancy and to describe patterns in those with term and preterm birth. We performed a prospective longitudinal multicenter study of pregnant individuals with singleton gestations from 2021 to 2022 across three academic centers in the United States. Participants were enrolled at <16 weeks gestation and had up to three quantitative stress-strain cervical elastography exams across pregnancy (11-15 weeks, 18-23 weeks, 28-34 weeks). The device synchronizes quantified pressure applied (stress) and tissue deformation (strain), facilitating direct mathematical calculation of biomechanical cervical softness. Linear mixed effects models were used to describe cervical softening across pregnancy and to compare patterns between preterm and term births. Changes in cervical softness measured with the device were also compared to longitudinal changes in cervical length over pregnancy. Among 372 patients, 1842 imaging exams across pregnancy were included in this analysis. The device demonstrated statistically significant steady cervical softening as pregnancy progressed (approximately 7.4% softening per week). Nulliparous individuals started pregnancy with 33% harder cervices than multiparous individuals (P<.001). Both starting cervical elastography in early pregnancy and trajectory of cervical softening over pregnancy differed in individuals with preterm birth compared to term birth. People with preterm birth started pregnancy with softer cervixes and exhibited a flatter softening slope, whereas people with term birth outcomes started pregnancy firmer and exhibited a steeper softening slope. Starting cervical length and change in cervical length over pregnancy did not differ in people with term vs. preterm birth outcomes. Cervical elastography using synchronized quantified stress and quantified strain can numerically quantify cervical remodeling patterns longitudinally in human pregnancies that are comparable across populations. This device can detect more robust differences in patients with subsequent preterm birth than cervical length. Use of this device provides a significant technological advancement, opening up a critically important opportunity to advance risk prediction for preterm birth.

Thermal operations are quantum channels that play a central role in deriving thermodynamic limitations in quantum systems. However, they were originally defined by implementation procedures rather than by fundamental principles. Alternative models of thermal processes have been proposed, but they obscure the resources required for implementation. Here, we identify the universal principle that separates these models. We show that thermal operations are uniquely characterized by a purely quantum information-theoretic property, which we call informational equilibrium. This formulation does not assume a particular Hamiltonian or even a notion of temperature: a channel is a thermal operation precisely when it leaves its environment invariant whenever the system is prepared in equilibrium. In this way, the usual energetic description of equilibrium (i.e., Gibbs states) emerges as a representation of an underlying informational constraint. Extending this framework, we note that enforcing environment invariance for all system inputs leads to catalytic channels, which idealize perfectly reusable heat baths. This informational equilibrium perspective culminates in a hierarchy for doubly-stochastic quantum channels, with strict separations between the classes. Our refined hierarchy sharpens the failure of the Birkhoff-von Neumann theorem in the quantum regime, and highlights the richer structural landscape of quantum channels compared to classical stochastic processes that cannot be captured by state-convertibility relations.

Ultraviolet (UV) imagers are important for a variety of applications, such as quality inspection in the semiconductor industry, forensics and food quality inspection, but are often costly because they require dedicated semiconductor process flows. Here, an imaging chip is introduced that has been fabricated using standard 40&#xa0;nm complementary metal-oxide-semiconductor (CMOS) technology. Instead of using a conventional charge-based photodetection principle, the imager uses a capacitive operation principle where UV-light causes capacitance changes via the photodielectric effect in a functionalization layer, which are measured by the underlying CMOS circuitry. This spin-coated or inkjet-printed functionalization layer consists of solution-processed, wide-bandgap, semiconducting metal-oxide nanoparticles, such as ZnO, SnO2 and Ga2O3. Owing to their bandgap-dependent optical absorption, these materials exhibit distinct capacitive responses across UV-A, UV-B, and UV-C spectral regions, thereby enabling band-selective detection and multispectral UV imaging. The sensors exhibit low noise-equivalent powers (17-138 fW Hz-1/2) across the UV bands. Unlike conventional silicon CMOS imagers, the present capacitive-CMOS platform is inherently visible-blind, providing selective UV detection. This work positions late-functionalized capacitive-CMOS arrays as a route toward reducing the fabrication complexity of UV imagers, which can lead to their more widespread implementation in consumer and low-volume application-specific products.

Magnons provide a route to ultrafast transport and nondestructive readout of spin-based information transfer. Here, we report magnon transport and its emergent anisotropic nature in BiFeO_{3} layers confined between ultrathin layers of the antiferromagnet LaFeO_{3}. Because of the confined state, BiFeO_{3} serves as an efficient magnon transmission channel as well as a magnetoelectric knob by which to control the stack by means of an electric field. We discuss the mechanism of the anisotropic spin transport based on the interaction between the antiferromagnetic order and the electric field. This allows us to manipulate and amplify the spin transport in such a confined geometry. Furthermore, lower crystal symmetry and suppression of the spin cycloid in ultrathin BiFeO_{3} stabilizes an antiferromagnetic state exhibiting a nontrivial sign inversion of the spin current, which is a characteristic of an altermagnet. This Letter provides an understanding of the anisotropic spin transport in complex antiferromagnetic heterostructures where ferroelectricity and altermagnetism coexist, paving the way for a new route to realize electric-field control of a novel state of magnetism.

Synaptic proteostasis is crucial for neuronal function, yet how synapses adapt to metabolic stress remains unclear. We show that nutrient stress, particularly serum withdrawal, induces autophagy-dependent remodeling of the synaptic proteome, whereas mTORC1 inhibition produces limited effects. Nutrient stress activates synaptic autophagy within 1-2 h and promotes the recruitment of the LC3 lipidation machinery via RAB5B-positive endosomal compartments in a dynein-dependent manner. Live imaging reveals enhanced RAB5B-ATG16L1 co-trafficking and increased ATG5 mobility upon serum withdrawal, indicating spatiotemporally controlled delivery of autophagy precursors to synaptic compartments. Functionally, nutrient deprivation dampens neuronal activity, while a fasting-mimicking diet induces synaptic proteome remodeling overlapping with starvation-associated autophagy cargo. In contrast, restriction of mTORC1-activating amino acids fails to induce comparable remodeling. Together, these findings identify a RAB5B-mediated trafficking pathway that links nutrient sensing to synaptic degradation, revealing how neurons maintain proteostasis under metabolic challenge.