Electro-Chemical Modelling of Laser Structured Electrodes

A simulation study performed in the scope of the project RealLi! is presented. One of the project’s main goals is to improve NMC811 and graphite electrode cycling capacities at high C-rates. The rapid charging and discharging capability of batteries is improved using laser ablation to introduce structures into the surface of the electrode composite layers. Due to improved transport kinetics, this not only improves the electrochemical properties in the high-current range, but also homogenizes and accelerates the electrolyte wetting during production as a side effect. This is particularly advantageous in thick-film electrodes for providing high energy densities. This study supports the laser structuring process of battery electrodes [1][2] via a virtual optimisation, based on electro-chemical battery models. The electrodes are structured by ultrafast laser ablation, with parallel channels being introduced along the electrode surface. This modification enables an easier electrolyte penetration, a reduced charge transfer resistance, and shortened lithium-ion transport pathways which finally leads to a reduced diffusion overpotential at high C-rates. The geometrical parameters of this process (pitch distance, width, and cross-sectional shape of laser-generated micro-channels) and their impact on cell performance are virtually optimised by simulations. The simulations are based on a homogenised multi-scale model, applied in 2D/3D macroscopic cuts, coupled with 1D microscopic particle cuts. The 2D/3D macroscopic electrolyte transport equations are common concentrated electrolyte equations. The microscopic particle transport equations are either a set of non-linear Fick’s Diffusion equations [3] that are used to describe spherical symmetric NMC811 materials or a set of Cahn-Hilliard equations [4] that consistently describe the phase separating nature of graphite anodes in cylindrically symmetric particles. The underlying numerical method is an implicit-multi-scale finite-element-method [3] that allows for a flexible implementation of such models. The first results of this ongoing project will be presented along with the overall structure of the method and its implementation. The results include geometrical as well as electro-chemical parameter variations and their respective sensitivity analysis. Furthermore, in the discussed electrode geometry the possible anisotropic structure of an electrode (due to particle shape and distribution) has a bigger impact than in unstructured electrodes. The improved transport pathways along the channels, therefore, imply the necessity of a more thorough homogenisation than it is usually done, for example in a Newman-Model approach. A long-term goal of this work is to enable a significant increase in areal energy density, i.e., the use of thicker electrode films and the use of advanced high energy materials in battery electrodes. [1]3D silicon/graphite composite electrodes for high-energy lithium-ion batteries, W. Pfleging et.al., Electrochimica Acta, Volume 317, 2019, Pages 502-508, J Power Sources 145 (5), 2345-2356 [2]Recent progress in laser texturing of battery materials: a review of tuning electrochemical performances, related material development, and prospects for large-scale manufacturing,W. Pfleging,International Journal of Extreme Manufacturing, Vol 3, 2020 [3]Derivation of a multi-scale battery model and its high-performance computing implementation, F. Pichler, Doctoral Thesis, Graz, 2018 [4]Phase Transformation Dynamics in Porous Battery Electrodes, R. Ferguson, M. Z. Bazant, Electrochimica Acta, Volume 146, Pages 89-97, 2014 Figure

CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions

Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI). We propose the Cascading Refinement CNN (CaRe-CNN), which is a fully 3D, end-to-end trained, 3-stage CNN cascade that exploits the hierarchical structure of such labeled cardiac data. Throughout the three stages of the cascade, the label definition changes and CaRe-CNN learns to gradually refine its intermediate predictions accordingly. Furthermore, to obtain more consistent qualitative predictions, we propose a series of post-processing steps that take anatomical constraints into account. Our CaRe-CNN was submitted to the FIMH 2023 MYOSAIQ challenge, where it ranked second out of 18 participating teams. CaRe-CNN showed great improvements most notably when segmenting the difficult but clinically most relevant myocardial infarct tissue (MIT) as well as microvascular obstructions (MVO). When computing the average scores over all labels, our method obtained the best score in eight out of ten metrics. Thus, accurate cardiac segmentation after acute MI via our CaRe-CNN allows generating patient-specific models of the heart serving as an important step towards personalized medicine.Comment: Accepted at VISIGRAPP 2024, 12 page

Reinforcement learning for safety-critical control of an automated vehicle

We present our approach for the development, validation and deployment of a data-driven decision-making function for the automated control of a vehicle. The decisionmaking function, based on an artificial neural network is trained to steer the mobile robot SPIDER towards a predefined, static path to a target point while avoiding collisions with obstacles along the path. The training is conducted by means of proximal policy optimisation (PPO), a state of the art algorithm from the field of reinforcement learning. The resulting controller is validated using KPIs quantifying its capability to follow a given path and its reactivity on perceived obstacles along the path. The corresponding tests are carried out in the training environment. Additionally, the tests shall be performed as well in the robotics situation Gazebo and in real world scenarios. For the latter the controller is deployed on a FPGA-based development platform, the FRACTAL platform, and integrated into the SPIDER software stack

Inhibition of the mevalonate pathway affects epigenetic regulation in cancer cells

The mevalonate pathway provides metabolites for post-translational modifications such as farnesylation, which are critical for the activity of RAS downstream signaling. Subsequently occurring regulatory processes can induce an aberrant stimulation of DNA methyltransferase (DNMT1) as well as changes in histone deacetylases (HDACs) and microRNAs in many cancer cell lines. Inhibitors of the mevalonate pathway are increasingly recognized as anticancer drugs. Extensive evidence indicates an intense cross-talk between signaling pathways, which affect growth, differentiation, and apoptosis either directly or indirectly via epigenetic mechanisms. Herein, we show data obtained by novel transcriptomic and corresponding methylomic or proteomic analyses from cell lines treated with pharmacologic doses of respective inhibitors (i.e., simvastatin, ibandronate). Metabolic pathways and their epigenetic consequences appear to be affected by a changed concentration of NADPH. Moreover, since the mevalonate metabolism is part of a signaling network, including vitamin D metabolism or fatty acid synthesis, the epigenetic activity of associated pathways is also presented. This emphasizes the far-reaching epigenetic impact of metabolic therapies on cancer cells and provides some explanation for clinical observations, which indicate the anticancer activity of statins and bisphosphonates

The four-minute approach revisited : accelerating MRI-based multi-factorial age estimation

Objectives: This feasibility study aimed to investigate the reliability of multi-factorial age estimation based on MR data of the hand, wisdom teeth and the clavicles with reduced acquisition time. Methods: The raw MR data of 34 volunteers-acquired on a 3T system and using acquisition times (TA) of 3:46 min (hand), 5:29 min (clavicles) and 10:46 min (teeth)-were retrospectively undersampled applying the commercially available CAIPIRINHA technique. Automatic and radiological age estimation methods were applied to the original image data as well as undersampled data to investigate the reliability of age estimates with decreasing acquisition time. Reliability was investigated determining standard deviation (SSD) and mean (MSD) of signed differences, intra-class correlation (ICC) and by performing Bland-Altman analysis. Results: Automatic age estimation generally showed very high reliability (SSD < 0.90 years) even for very short acquisition times (SSD ≈ 0.20 years for a total TA of 4 min). Radiological age estimation provided highly reliable results for images of the hand (ICC ≥ 0.96) and the teeth (ICC ≥ 0.79) for short acquisition times (TA = 16 s for the hand, TA = 2:21 min for the teeth), imaging data of the clavicles allowed for moderate acceleration (TA = 1:25 min, ICC ≥ 0.71). Conclusions: The results demonstrate that reliable multi-factorial age estimation based on MRI of the hand, wisdom teeth and the clavicles can be performed using images acquired with a total acquisition time of 4 min

Prior Stroke in PFO Patients Is Associated With Both PFO-Related and -Unrelated Factors.

Background and Purpose: To identify factors associated with prior stroke at presentation in patients with cryptogenic stroke (CS) and patent foramen ovale (PFO). Methods: We studied cross-sectional data from the International PFO Consortium Study (NCT00859885). Patients with first-ever stroke and those with prior stroke at baseline were analyzed for an association with PFO-related (right-to-left shunt at rest, atrial septal aneurysm, deep venous thrombosis, pulmonary embolism, and Valsalva maneuver) and PFO-unrelated factors (age, gender, BMI, hypertension, diabetes mellitus, hypercholesterolemia, smoking, migraine, coronary artery disease, aortic plaque). A multivariable analysis was used to adjust effect estimation for confounding, e.g., owing to the age-dependent definition of study groups in this cross-sectional study design. Results: We identified 635 patients with first-ever and 53 patients with prior stroke. Age, BMI, hypertension, diabetes mellitus, hypercholesterolemia, coronary artery disease, and right-to-left shunt (RLS) at rest were significantly associated with prior stroke. Using a pre-specified multivariable logistic regression model, age (Odds Ratio 1.06), BMI (OR 1.06), hypercholesterolemia (OR 1.90) and RLS at rest (OR 1.88) were strongly associated with prior stroke.Based on these factors, we developed a nomogram to illustrate the strength of the relation of individual factors to prior stroke. Conclusion: In patients with CS and PFO, the likelihood of prior stroke is associated with both, PFO-related and PFO-unrelated factors

Climate Change, Foodborne Pathogens, and Illness in Higher Income Countries

Purpose of review: We present a review of the likely consequences of climate change for foodborne pathogens and associated human illness in higher income countries. Recent findings: The relationships between climate and food are complex and hence the impacts of climate change uncertain. This makes it difficult to know which foodborne pathogens will be most affected, what the specific effects will be, and on what timescales changes might occur. Hence, a focus upon current capacity and adaptation potential against foodborne pathogens is essential. We highlight a number of developments that may enhance preparedness for climate change. These include: • Adoption of novel surveillance methods, such as syndromic methods, to speed up detection and increase the fidelity of intervention in foodborne outbreaks • Genotype based approaches to surveillance of food pathogens to enhance spatio-temporal resolution in tracing and tracking of illness • Ever increasing integration of plant, animal and human surveillance systems, one-health, to maximize potential for identifying threats • Increased commitment to cross-border (global) information initiatives (including big data) • Improved clarity regarding the governance of complex societal issues such as the conflict between food safety and food waste • Strong user centric (social) communications strategies to engage diverse stakeholder groups Summary: The impact of climate change upon foodborne pathogens and associated illness is uncertain. This emphasises the need to enhance current capacity and adaptation potential against foodborne illness. A range of developments are explored in this paper to enhance preparedness

Suppression of intratumoral CCL22 by type I interferon inhibits migration of regulatory T cells and blocks cancer progression

The chemokine CCL22 is abundantly expressed in many types of cancer and is instrumental for intratumoral recruitment of regulatory T cells (Treg), an important subset of immunosuppressive and tumor-promoting lymphocytes. In this study, we offer evidence for a generalized strategy to blunt Treg activity that can limit immune escape and promote tumor rejection. Activation of innate immunity with Toll-like receptor (TLR) or RIG-I-like receptor (RLR) ligands prevented accumulation of Treg in tumors by blocking their immigration. Mechanistic investigations indicated Treg blockade was a consequence of reduced intratumoral CCL22 levels caused by type I interferon. Notably, stable expression of CCL22 abrogated the antitumor effects of treatment with RLR or TLR ligands. Taken together, our findings argue that type I interferon blocks the Treg-attracting chemokine CCL22 and thus helps limit the recruitment of Treg to tumors, a finding with implications for cancer immunotherapy