埃朗根-纽伦堡大学

Nonequilibrium Green’s functions provide a powerful framework for studying quantum many-body dynamics including the laser-induced dynamics in solids. The Non-Equilibrium Systems Simulation package (NESSi) offers an efficient platform for such simulations, ranging from perturbative approaches like nonequilibrium GW to nonequilibrium dynamical mean-field theory. However, simulations based on nonequilibrium Green’s functions become computationally demanding when the dynamics span a large temporal range, such as from sub-femtosecond electron dynamics to the picosecond dynamics of collective modes. Due to the memory integral in the Kadanoff-Baym equations, which serve as equations of motion for nonequilibrium Green’s functions, the computational cost scales as O(Nt3) with the number of timesteps Nt, and the memory requirement scales as O(Nt2). In this work, we extend NESSi by incorporating techniques that aim to overcome this bottleneck: (i) By truncating the memory integrals in the KBE to a maximum of Nc timesteps, the computational complexity is reduced to O(NtNc2), and the memory requirement to O(Nc2). Provided that the results converge with respect to the cutoff Nc, memory truncation allows to extend the simulations to significantly longer times. (ii) We introduce functionalities to describe nonequilibrium steady states, i.e. time-translationally invariant nonequilibrium states. Such states are relevant for transport settings, and they provide an approximate description of slowly evolving (prethermal) nonequilibrium states. New Version Program Summary Program Title: NESSi.CPC Library link to program files: https://doi.org/10.17632/973crf9hgd.2Developer’s repository link: https://github.com/nessi-cntr/nessiLicensing provisions: MPL-2.0.Programming language: C++, python.External routines/libraries: cmake, eigen3, fftw3 (optional), hdf5 (optional), mpi (optional), omp (optional).Journal reference of previous version: Comput. Phys. Commun. 257 (2020) 107484 [1].Does the new version supersede the previous version?: Extension of the existing library.Reasons for the new version: Reduce computational and memory cost to extend simulation times and directly simulate nonequilibrium steady states.Summary of revisions: Extension to memory-truncated and steady-state Kadanoff-Baym equations.Nature of problem: Solves equations of motion of time-dependent Green’s functions on the Kadanoff-Baym contour for a memory-truncated self-energy and in the steady state.Solution method: Higher-order solution methods of integral and integro-differential equations on the Kadanoff-Baym contour with memory-truncation in the integral kernel and evaluation of Fourier integrals in the steady state.References[1] M. Schüler, D. Golez, Y. Murakami, N. Bittner, A. Herrmann, H. U. Strand, P. Werner, M. Eckstein, Nessi: The non- equilibrium systems simulation package, Comput. Phys. Commun. 257 (2020) 107484. 10.1016/j.cpc.2020.107484.

The digital transformation of healthcare is rapidly reshaping neurology, particularly in the field of movement disorders, where continuous monitoring, long disease trajectories, and complex multimodal care create a high demand for innovative solutions. Wearable sensors, digital diagnostics, app-based therapeutics, and integrated hybrid care networks and digital supported care pathways promise earlier diagnosis, personalized treatment and care management, and improved long-term outcomes. However, real-world implementation in managed outpatient care remains fragmented and faces major barriers beyond pure technological feasibility. This position paper critically reviews the current state of digital technologies in movement disorder care, identifies key systemic, ethical, and economic roadblocks, and proposes a pragmatic roadmap toward a realistic and ethically sound digital outpatient clinic. We argue that the future digital clinic will not be defined solely by technical progress, but by how consciously healthcare systems integrate digital tools to hybrid care solutions while preserving human-centered, equitable, and evidence-based care.

Bioreactors in biofabrication and tissue engineering enable dynamic cell and tissue culture under controlled conditions while applying external stimuli such as mechanical or electrical signals. Together, these features mimic the in vivo environment and support functional tissue formation for implantation or model systems. Most current bioreactors focus on medium perfusion and mechanical or electrical stimulation, but often lack real-time monitoring, which is essential for quality control. To address this gap, this work presents an additively manufactured bioreactor that integrates dynamic perfusion, electrical stimulation, and in-line microscopy and sensor monitoring for culturing 3D-bioprinted constructs. Three bioinks containing C2C12 myoblasts were 3D-printed and cultured in the system, with continuous tracking of pH and temperature and comparison to static controls. Laminar perfusion improved cell viability and spreading in GelMA and ADA-GEL bioinks, with faster myoblast alignment observed in ADA-GEL after 3 days. Platinum-coated copper electrodes embedded in the design produced electric fields of at least 10 V & centerdot;cm- 1. As a proof of principle, isolated murine interossei plantares myofibers were placed in the chamber and electrically stimulated to induce twitching. Overall, this bioreactor demonstrates strong potential as a platform for real-time tissue monitoring and studying customized stimuli during tissue development.

Inflammatory bowel diseases present with elevated levels of intestinal epithelial cell (IEC) death, which compromises the gut barrier, activating immune cells and triggering more IEC death. The endogenous signals that prevent IEC death and break this vicious cycle, allowing resolution of intestinal inflammation, remain largely unknown. Here we show that prostaglandin E2 signalling via the E-type prostanoid receptor 4 (EP4) on IECs represses epithelial necroptosis and induces resolution of colitis. We found that EP4 expression correlates with an improved IBD outcome and that EP4 activation induces a transcriptional signature consistent with resolution of intestinal inflammation. We further show that dysregulated necroptosis prevents resolution, and EP4 agonism suppresses necroptosis in human and mouse IECs. Mechanistically, EP4 signalling on IECs converges on receptor-interacting protein kinase 1 to suppress tumour necrosis factor-induced activation and membrane translocation of the necroptosis effector mixed-lineage kinase domain-like pseudokinase. In summary, our study indicates that EP4 promotes the resolution of colitis by suppressing IEC necroptosis.

Moderators are intended to clarify when, where, and for whom interventions work, yet null moderator results are often omitted from the published record. We term this practice “reporting after moderators are significant” (RAMSing) and examine its scope and consequences in STEM education meta-research. Drawing on an umbrella review of 89 meta-analyses comprising 1,786 moderator effect sizes, we found a substantial visibility gap: non-significant moderator effect sizes showed markedly lower relative visibility than the most strongly selected results ( ≈ 0.40). To address this bias, we combined structural mapping with two bias-robust Bayesian frameworks, RoBMA-PSMA and RoBMA-Regression, to estimate bias-adjusted moderator effects, derive field-specific benchmarks, and estimate realistic evidential requirements for moderator testing. The overall bias-adjusted mean moderator effect was g = 0.27 (95 = 0.48). Modeling a five-level moderator-theme classification substantially improved fit (BFInc = 350) and slightly reduced residual heterogeneity ( = 0.44). RAMSing-adjusted benchmarks placed the 25th, 50th, and 75th percentiles of |g| at 0.18, 0.38, and 0.64, respectively, all below both Cohen’s conventional heuristics and the raw empirical quartiles. Prospective power analyses further indicated that, under the assumptions examined here (g = 0.38, ^2 = 0.24, = 0.05), approximately 14 independent effect sizes per moderator level are needed to detect a typical moderator effect. These findings indicate that RAMSing is likely an important source of distortion in moderator-based inference within this corpus. The workflow and openly available toolkit introduced here provide a practical framework for detecting and bias-adjusting RAMSing, and for supporting practices designed to reduce it in STEM education and other fields that rely on meta-analytic moderator evidence.