Trajectories of charged particles trapped in Earth's magnetic field

I outline the theory of relativistic charged-particle motion in the magnetosphere in a way suitable for undergraduate courses. I discuss particle and guiding center motion, derive the three adiabatic invariants associated with them, and present particle trajectories in a dipolar field. I provide twelve computational exercises that can be used as classroom assignments or for self-study. Two of the exercises, drift-shell bifurcation and Speiser orbits, are adapted from active magnetospheric research. The Python code provided in the supplement can be used to replicate the trajectories and can be easily extended for different field geometries.Comment: 10 pages, 7 figures. Submitted to American Journal of Physic

Citizen Science Time Domain Astronomy with Astro-COLIBRI

Astro-COLIBRI is an innovative tool designed for professional astronomers to facilitate the study of transient astronomical events. Transient events - such as supernovae, gamma-ray bursts and stellar mergers - are fleeting cataclysmic phenomena that can offer profound insights into the most violent processes in the universe. Revealing their secrets requires rapid and precise observations: Astro-COLIBRI alerts its users of new transient discoveries from observatories all over the world in real-time. The platform also provides observers the details they need to make follow-up observations. Some of the transient phenomena available through Astro-COLIBRI are accessible by amateur astronomers and citizen scientists. A subset of the features dedicated to this growing group of users are highlighted here. They include the possibility of receiving only alerts on very bright events, the possibility of defining custom observer locations, as well as the calculation of optimized observation plans for searches for optical counterparts to gravitational wave events.Comment: Proceedings Atelier Pro-AM Gemini, Journ\'ees SF2A 2023. arXiv admin note: text overlap with arXiv:2308.0704

Self-consistent local-equilibrium model for density profile and distribution of dissipative currents in a Hall bar under strong magnetic fields

Recent spatially resolved measurements of the electrostatic-potential variation across a Hall bar in strong magnetic fields, which revealed a clear correlation between current-carrying strips and incompressible strips expected near the edges of the Hall bar, cannot be understood on the basis of existing equilibrium theories. To explain these experiments, we generalize the Thomas-Fermi--Poisson approach for the self-consistent calculation of electrostatic potential and electron density in {\em total} thermal equilibrium to a {\em local equilibrium} theory that allows to treat finite gradients of the electrochemical potential as driving forces of currents in the presence of dissipation. A conventional conductivity model with small values of the longitudinal conductivity for integer values of the (local) Landau-level filling factor shows that, in apparent agreement with experiment, the current density is localized near incompressible strips, whose location and width in turn depend on the applied current.Comment: 9 pages, 7 figure

Parameter estimation for biochemical reaction networks using Wasserstein distances

We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian Dynamics.Comment: 22 pages, 8 figures. Slight modifications/additions to the text; added new section (Section 4.4) and Appendi

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Prime movers : mechanochemistry of mitotic kinesins

Mitotic spindles are self-organizing protein machines that harness teams of multiple force generators to drive chromosome segregation. Kinesins are key members of these force-generating teams. Different kinesins walk directionally along dynamic microtubules, anchor, crosslink, align and sort microtubules into polarized bundles, and influence microtubule dynamics by interacting with microtubule tips. The mechanochemical mechanisms of these kinesins are specialized to enable each type to make a specific contribution to spindle self-organization and chromosome segregation

Multilevel factors are associated with immunosuppressant nonadherence in heart transplant recipients: The international BRIGHT study

Factors at the level of family/healthcare worker, organization, and system are neglected in medication nonadherence research in heart transplantation (HTx). The 4-continent, 11-country cross-sectional Building Research Initiative Group: Chronic Illness Management and Adherence in Transplantation (BRIGHT) study used multistaged sampling to examine 36 HTx centers, including 36 HTx directors, 100 clinicians, and 1397 patients. Nonadherence to immunosuppressants\u2014defined as any deviation in taking or timing adherence and/or dose reduction\u2014was assessed using the Basel Assessment of Adherence to Immunosuppressive Medications Scale \ua9 (BAASIS \ua9 ) interview. Guided by the Integrative Model of Behavioral Prediction and Bronfenbrenner's ecological model, we analyzed factors at these multiple levels using sequential logistic regression analysis (6 blocks). The nonadherence prevalence was 34.1%. Six multilevel factors were associated independently (either positively or negatively) with nonadherence: patient level: barriers to taking immunosuppressants (odds ratio [OR]: 11.48); smoking (OR: 2.19); family/healthcare provider level: frequency of having someone to help patients read health-related materials (OR: 0.85); organization level: clinicians reporting nonadherent patients were targeted with adherence interventions (OR: 0.66); pickup of medications at physician's office (OR: 2.31); and policy level: monthly out-of-pocket costs for medication (OR: 1.16). Factors associated with nonadherence are evident at multiple levels. Improving medication nonadherence requires addressing not only the patient, but also family/healthcare provider, organization, and policy levels

Action of MK‐7264 (Gefapixant) at human P2X3 and P2X2/3 receptors and in vivo efficacy in models of sensitisation

Background & Purpose The P2X3 receptor is an ATP‐gated ion channel expressed by sensory afferent neurons, and is as a target to treat chronic sensitisation conditions. The first‐in‐class, selective P2X3 and P2X2/3 receptor antagonist, the diaminopyrimidine MK‐7264 (Gefapixant), has progressed to Phase III trials for refractory or unexplained chronic cough. We have used patch‐clamp to elucidate the pharmacology and kinetics of MK‐7264 and rat models of hypersensitivity and hyperalgesia to test efficacy in these conditions. Experimental Approach Whole‐cell patch‐clamp of 1321N1 cells expressing human P2X3 and P2X2/3 receptors was used to determine mode of MK‐7264 action, potency and kinetics. The analgesic efficacy was assessed using paw withdrawal threshold and limb weight distribution in rat models of inflammatory, osteoarthritic and neuropathic sensitisation. Key Results MK‐7264 is a reversible allosteric antagonist at human P2X3 and P2X2/3 receptors with IC50 values of 153 and 220nM, respectively. Experiments with the slowly desensitising P2X2/3 heteromer revealed concentration and state‐dependency to wash‐on, with faster rates and greater inhibition when applied before agonist compared to during agonist application. Wash‐on rate (τ value) for MK‐7264 at maximal concentrations was 19s and 146s when applied before and during agonist application, respectively. In vivo, MK‐7264 (30 mg/kg) displayed efficacy comparable to naproxen (20 mg/kg) in inflammatory and osteoarthritic sensitisation models, and gabapentin (100 mg/kg) in neuropathic sensitisation models, increasing paw withdrawal threshold and decreasing weight bearing discomfort. Conclusions and Implications MK‐7264 is a reversible and selective P2X3 and P2X2/3 antagonist, exerting allosteric antagonism via preferential activity at closed channels. Efficacy in rat models supports clinical investigation of chronic sensitisation conditions

The Human Phenotype Ontology in 2024: phenotypes around the world

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs