Shock structures in time averaged patterns for the Kuramoto-Sivashinsky equation

The Kuramoto-Sivashinsky equation with fixed boundary conditions is numerically studied. Shocklike structures appear in the time-averaged patterns for some parameter range of the boundary values. Effective diffusion constant is estimated from the relation of the width and the height of the shock structures.Comment: 6 pages, 7 figure

Topological augmentation to infer hidden processes in biological systems

Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Success in the sciences: potential influences of sex role conflict, self-efficacy, and role modeling on women\u27s career aspirations

A discrepancy exists between the number of men and women pursuing careers in science, technology, engineering, and mathematics (STEM). To investigate this discrepancy, this study examined a model assessing the potential influence of multiple variables on the career aspirations of women pursuing these degrees. Two hundred thirty-two first and second-year women majoring in STEM fields completed measures assessing quality and quantity of role modeling relationships, STEM self-efficacy, STEM interests, sex role conflict, and career aspirations. Quality of role modeling relationships and STEM self-efficacy significantly predicted career aspirations. STEM self-efficacy did not mediate the relation between quality of role modeling relationships and career aspirations. Findings highlight the importance of role modeling relationships and STEM self-efficacy for women pursuing careers in STEM fields

Direct observation of twist mode in electroconvection in I52

I report on the direct observation of a uniform twist mode of the director field in electroconvection in I52. Recent theoretical work suggests that such a uniform twist mode of the director field is responsible for a number of secondary bifurcations in both electroconvection and thermal convection in nematics. I show here evidence that the proposed mechanisms are consistent with being the source of the previously reported SO2 state of electroconvection in I52. The same mechanisms also contribute to a tertiary Hopf bifurcation that I observe in electroconvection in I52. There are quantitative differences between the experiment and calculations that only include the twist mode. These differences suggest that a complete description must include effects described by the weak-electrolyte model of electroconvection

Modulated structures in electroconvection in nematic liquid crystals

Motivated by experiments in electroconvection in nematic liquid crystals with homeotropic alignment we study the coupled amplitude equations describing the formation of a stationary roll pattern in the presence of a weakly-damped mode that breaks isotropy. The equations can be generalized to describe the planarly aligned case if the orienting effect of the boundaries is small, which can be achieved by a destabilizing magnetic field. The slow mode represents the in-plane director at the center of the cell. The simplest uniform states are normal rolls which may undergo a pitchfork bifurcation to abnormal rolls with a misaligned in-plane director.We present a new class of defect-free solutions with spatial modulations perpendicular to the rolls. In a parameter range where the zig-zag instability is not relevant these solutions are stable attractors, as observed in experiments. We also present two-dimensionally modulated states with and without defects which result from the destabilization of the one-dimensionally modulated structures. Finally, for no (or very small) damping, and away from the rotationally symmetric case, we find static chevrons made up of a periodic arrangement of defect chains (or bands of defects) separating homogeneous regions of oblique rolls with very small amplitude. These states may provide a model for a class of poorly understood stationary structures observed in various highly-conducting materials ("prechevrons" or "broad domains").Comment: 13 pages, 13 figure

AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field

Objectives: The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. Methods: We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. Results: Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. Conclusion: AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.Comment: 15 pages, 2 table

Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution

ObjectivesThis narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration.MethodsA narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014–2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019–2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges.ResultsRecent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5 mm, with stable recordings over 15 months; 2) Deep learning decoders show 40 % improvement in information transfer rates compared to traditional methods; 3) Adaptive algorithms maintain >90 % success rates in motor control tasks over 200-day periods without recalibration; 4) Novel closed-loop optimization frameworks reduce user training time by 55 % while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges.ConclusionsBCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology’s growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization frameworks, suggest accelerating progress toward widespread therapeutic use and human augmentation applications.</div

Enzymatic reactions towards aldehydes: An overview

Many aldehydes are volatile compounds with distinct and characteristic olfactory properties. The aldehydic functional group is reactive and, as such, an invaluable chemical multi-tool to make all sorts of products. Owing to the reactivity, the selective synthesis of aldehydic is a challenging task. Nature has evolved a number of enzymatic reactions to produce aldehydes, and this review provides an overview of aldehyde-forming reactions in biological systems and beyond. Whereas some of these biotransformations are still in their infancy in terms of synthetic applicability, others are developed to an extent that allows their implementation as industrial biocatalysts