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.
OptOrch is a transparent and modular optimization toolkit for forest tree seed orchard (SO) deployment. It implements SO-specific optimal contribution models in AMPL (A Mathematical Programming Language), separating biological assumptions, input data, and numerical solvers. Unlike existing optimal-contribution or mate-allocation software, OptOrch exposes the objective function and constraints directly, allowing breeders to modify SO-specific requirements, including status-number thresholds, proportional contribution bounds, graft availability, pairwise coancestry restrictions, female and male gametic contributions, and external pollen flow. When the declared formulation is supported by the selected solver, solutions can be evaluated using solver-reported feasibility status, objective bounds, and optimality gaps. The algebraic framework also allows alternative formulations to be tested when biological constraints create mixed-integer, nonlinear, or nonconvex instances. We demonstrate OptOrch with simulation-based scenarios involving alternative status-number thresholds and pollen contamination. The framework quantifies gain--diversity trade-offs, evaluates external pollen effects, and compares feasible deployment strategies under realistic biological and operational constraints.
The action potential constitutes the digital component of the signaling dynamics of neurons. But the biophysical nature of the full-time course of the action potential associated with changes in membrane potential is mathematically distinct from its representation as a discrete set of events that encode when action potentials are triggered in a collection of spike trains. In this letter, we develop from first principles a unified functional-analytic framework for neuronal spike trains, grounded in Schwartz distribution theory. We show how this representation provides an exact operational calculus for convolution, distributional differentiation, and distributional support, which enables closed-form analysis of spike train dynamics without discretization, rate approximation, or smoothing. We then analyze the framework in the context of a two-neuron reciprocal circuit with propagation latencies and refractoriness, deriving exact results for synaptic drive, spike timing sensitivity, and causal admissibility of inputs, quantities that are either ill-defined or require approximation in conventional treatments.
Preoperative anxiety is common in children and is associated with emergence delirium-a distressing postoperative complication characterized by agitation and disorientation. Midazolam has long been the drug of choice for preoperative anxiolysis. More recently, α2 adrenoceptor agonists, such as clonidine, have gained popularity for their sedative and analgesic properties. The effect of these agents on the incidence of emergence delirium remains uncertain. This study evaluated whether premedication with clonidine or midazolam is associated with emergence delirium in children undergoing general anesthesia. We performed a post hoc analysis of prospectively collected data pooled from 10 clinical studies, comprising data from 4796 general anesthetics in children (0-16 years). The studies were carried out at a tertiary pediatric hospital in Perth, Australia. We assessed the impact of premedication with clonidine or midazolam on the incidence of emergence delirium using validated scales. In this cohort, with a mean age of 6.8 (range: 0.03-16.97), 331 children received clonidine and 731 received midazolam. Overall, the incidence of emergence delirium was 6%. Primary propensity score-matched analyses found no significant association between clonidine (OR 1.19, 95% CI 0.65-2.16) or midazolam (OR 1.23, 95% CI 0.79-1.92) and emergence delirium. In our secondary adjusted logistic regression models, midazolam was associated with increased odds of emergence delirium, whereas clonidine showed no statistically significant association overall. The overall incidence of emergence delirium was low. While clonidine showed no statistically significant association with emergence delirium, our secondary multivariate analysis suggests midazolam might be associated with an increased risk of emergence delirium.
The preBötzinger Complex (preBötC) 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 preBötC 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) preBötC 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 preBötC 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 preBötC 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.
Spectrum-sparse chaotic signals offer both a large detection bandwidth and low spectral occupancy. However, existing photonic schemes have difficulty in reconfiguring their sparsity characteristics. A microwave photonic scheme based on an optical frequency shift loop (OFSL) is proposed to generate a broadband chaotic signal with sparse spectrum. Feeding a narrowband chaotic seed signal into the OFSL generates the spectrum-sparse broadband chaotic signal, whose sparsity depends on the ratio of the seed's bandwidth to the frequency shift. A mathematical model is developed, demonstrating the principle of broadband chaotic signal generation based on the OFSL. Experimental results present autocorrelation and time-frequency characteristics of chaotic signals with sparse factors from 5% to 25%, covering a range from 0 to 8 GHz. Reconstructed phase spaces verify that the chaotic attractor structure is unchanged in the OFSL.
The advent of single-molecule nanopore sequencing established a powerful platform for modern genomics by using static biological pores to report the translocation of canonical nucleic acids, enabling rapid, accessible nucleic acid analysis. However, extending this strategy to single-molecule proteomics has stalled against a fundamental biophysical bottleneck. Current efforts in nanopore proteomics attempt to retrofit these static, spatial "caliper" biological nanopores (e.g., α-hemolysin, MspA, CsgG, aerolysin) for protein sequencing despite the immense steric, charge, and conformational heterogeneity of proteins. Unlike the chemically uniform, polyanionic phosphodiester backbone of DNA, the proteome contains isosteric and isobaric variants that confound purely volumetric measurements made by static pores. To address this bottleneck, we propose the application of dynamical translocases - naturally evolved, protein-handling nanomachines (e.g., the anthrax toxin protective antigen). Unlike static pores that rely on passive diffusion, dynamical translocases employ target-docking clamp architectures that achieve low nanomolar sensitivity. Active-site conformational dynamics generate high-dimensional kinetic fingerprints that enable molecular discrimination during translocation. By coupling dynamical translocases with Physics-Informed Machine Learning (PIML), we demonstrate that amino-acid side-chain-dependent thermodynamic friction can be mathematically decoded, enabling >90% accurate classification of chemically distinct amino acid classes and doing so label-free without the artificial DNA-handles required by legacy platforms.
Spatial proteomics measures multiple proteins in situ, capturing tissue complexity. However, cell classification in densely packed tissues remains challenging because of the lack of efficient classification algorithms, annotation tools and high-quality labeled datasets to benchmark computational methods. We introduce CellTune, an integrated software for analysis of large spatial proteomics datasets, which streamlines precise cell classification through an optimized human-in-the-loop active learning workflow. It advances core capabilities for analysis of large datasets with an intuitive and code-free interface. To evaluate CellTune, we created CellTuneDepot, a resource of 40,000 manually annotated cells and 3.5 million high-quality labeled cells across 60 cell types. CellTune outperforms alternative methods, achieving accuracy comparable to human performance while enabling increased classification resolution and discovery of novel cell types. Together, CellTune and CellTuneDepot provide researchers with a tool for state-of-the-art classification accuracy and resolution at scale to drive biological insights.
How animals perform odor-guided search across the broad range of dynamic flow conditions they encounter in nature is not well understood. Prior work treats established strategies such as circling and zigzagging as discrete, context-dependent behaviors. Here, we propose a parsimonious mathematical framework that unites these motifs into a continuum. We show that free flying Drosophila exhibit this continuum by smoothly reshaping search behavior between circling and zigzagging across unsteady flow, still air, and a range of wind speeds. Strikingly, a flow-invariant rhythmic course progression underlies this behavioral spectrum. Available data from moths and sharks across different flow regimes suggest a similarly preserved rhythm, while the rate of this rhythm varies across taxa. Together, these results support a common conceptual basis for olfactory search behavior and explain how search geometry can adapt automatically to local flow.
In clinical trials, a treatment rarely benefits every patient, underscoring the need to identify subgroups that are more likely to respond. Traditional subgroup analysis approaches, including finite mixture and threshold models, often rely on stringent distributional assumptions and prespecified subgroup structures that may be unrealistic in practice. Moreover, the resulting subgroups can be difficult to interpret and may not generalize well to new patients. To address these challenges, we propose a new least-squares regression framework that accommodates flexible subgroup structure in heterogeneous data. Our model is distribution-free and allows subgroup membership to depend on covariates, while permitting both the number and the organization of coefficient groups to vary across covariates. Building on regularization, we develop a computationally efficient procedure to detect subgroup structure in linear regression coefficients and then use a support vector machine to recover the corresponding partitions, enabling subgroup membership prediction for future individuals. Relative to pairwise fused regularization, our approach substantially reduces computational complexity. We also establish theoretical guarantees for estimation of group-specific parameters and recovery of the underlying partitions. Simulation studies and a real-data application illustrate the practical effectiveness of the proposed method.
Safeguarding crop genetic resistance is essential for limiting disease impacts and maintaining agricultural productivity. The economic impacts of genetic resistance loss driven by pathogen adaptation have rarely been quantified. While developing improved varieties is an option in the event of resistance loss, this is a lengthy, uncertain and costly process, leaving few viable alternatives during epidemics. Furthermore, when disease resistance traits are overcome, growers rely more heavily on chemical inputs. This study develops a scalable analytical and Monte Carlo simulation-based framework to evaluate the economic impacts of a one-step change, either improvement or decline, in the rating of host genetic resistance to disease using the wheat-Pyrenophora tritici repentis pathosystem as a case study. The simulation considered disease resistance rating transitions, epidemic risk, proportion of yield lost to disease, quality downgrade risk and grower risk aversion under different yield potential scenarios. Results demonstrate that a one-step improvement in genetic resistance rating increased the expected discounted net benefits by 5.17 to 7.18%, whereas a one-step decline reduced net benefits by 5.57 to 8.10% depending on yield potential and epidemic conditions. Our findings highlight that the cost of genetic resistance loss outweighs the benefits from incremental genetic resistance gains, particularly under epidemic conditions, indicating the need to consider resistance as a core agricultural capital in genetic innovation. Failure to protect existing resistance increases reliance on chemical measures of crop protection, the risk of chemical resistance development and vulnerability to epidemics, with significant implications for long-term farm viability.
Multimorbidity in older adults is common, heterogeneous, and highly dynamic, and it is strongly associated with disability and increased healthcare utilization. However, existing approaches to studying multimorbidity trajectories are largely descriptive or rely on discrete-time models, which struggle to handle irregular observation intervals and right-censoring. We developed a continuous-time hidden multistate modeling framework to capture transitions among latent multimorbidity patterns while accounting for interval censoring and misclassification. A simulation study compared alternative model specifications under varying sample sizes and follow-up schemes, and the best-performing specification was applied to longitudinal data from the Swedish National study on Aging and Care-Kungsholmen (SNAC-K), including 2716 multimorbid participants followed for up to 18 years. Simulation results showed that hidden multistate models substantially reduced bias in transition hazard estimates compared to non-hidden models, with fully time-inhomogeneous models outperforming piecewise approximations. Application to SNAC-K confirmed the feasibility and practical utility of this framework, enabling identification of risk factors for accelerated progression toward complex multimorbidity and revealing a gradient of mortality risk across patterns. Continuous-time hidden multistate models provide a robust alternative to traditional approaches, supporting individualized predictions and informing targeted interventions and secondary prevention strategies for multimorbidity in aging populations.
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).
Heterogeneous adapting populations, whether in laboratory evolution experiments or global-scale pandemics, experience complex evolutionary dynamics due to the interplay of selection, mutation, and stochasticity. Inference of individual genotypes' fitnesses therefore becomes difficult, especially when many lineages are competing and data are noisy. Existing fitness inference methods tend to rely on assumptions on the fitness landscape's maximum order of epistasis, or they require complicated iterative optimization algorithms to converge on fitness estimates. Here, we show that fitness landscapes can be computed from time series data, without any restrictions on epistatic order or iterative optimization, using a simple, closed-form mathematical expression that is easily implemented with standard matrix operations used commonly in linear algebra. We demonstrate successful fitness inference from noisy in silico evolutionary dynamics from four different noisy microscopic processes, including Wright-Fisher, Moran, ProSeD (serial dilution), and barcoded passage simulations. Then, we illustrate the broad applicability of the equation to five experimental time series datasets, including barcoded yeast evolution experiments, murine norovirus-1 serial passage experiments, and SARS-CoV-2 global genomic prevalence data. Our formula successfully infers fitnesses for even for rare genotypes several orders of magnitude less prevalent than top lineages, works with both laboratory evolution and epidemiological data, and can be implemented in most modern scientific programming languages.
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.
We show that the canonical formulation of the semiclassical Einstein equation, where the matter terms in the constraints are replaced by expectation values of the corresponding operators in quantum states, is inconsistent due to the nonclosure of the resulting constraint algebra.
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 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.
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.
This study presents the first mathematical model of pulsatile hemodynamics that encompasses the complete pulmonary circulation, explicitly linking the large arteries, arterioles, capillaries, venules, and large veins. To overcome the limitations of previous models that exclude explicit capillary dynamics, we incorporate a one-dimensional structured-tree model of the pulmonary arteries and veins with a dynamic capillary sheet model. This approach establishes a recursive method for coupling the capillary sheets to the structured trees, connecting arterioles and venules in a ladder-like architecture. To evaluate the impact of incorporating this capillary structure, we compare simulated hemodynamics in a healthy control subject and a pulmonary hypertension (PH) patient. Results illustrate that including capillaries in the model significantly alters hemodynamic predictions by introducing downstream damping. In the healthy control subject, the inclusion of the capillary network attenuates pulsatile energy, yielding the expected steady venous pressure and flow profiles, whereas omitting the capillaries results in an unphysiological high pulsatility transmitting into the venous system. The structural impact of the capillaries is even more pronounced in the PH patient, where explicitly modeling the capillary bed corrects an over-prediction in peak systolic pressure in the main pulmonary artery. Furthermore, unlike the healthy control subject, the remodeled PH microvasculature fails to completely isolate the venous system from arterial pulsations. Finally, we employ parametric sensitivity analysis to investigate how specific biomechanical factors drive vascular remodeling, demonstrating the framework's capability to quantify disease progression and severity.
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