Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs.

The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image.

Automated model discovery for tensional homeostasis: Constitutive machine learning in growth and remodeling.

We present a built-in physics neural network architecture, known as inelastic Constitutive Artificial Neural Network (iCANN), to discover the inelastic phenomenon of tensional homeostasis. In this course, identifying the optimal model and material parameters to accurately capture the macroscopic behavior of inelastic materials can only be accomplished with significant expertise, is often time-consuming, and prone to error, regardless of the specific inelastic phenomenon. To address this challenge, built-in physics machine learning algorithms offer significant potential.

Molecular Understanding of Activity Changes of Alcohol Dehydrogenase in Deep Eutectic Solvents.

Deep eutectic solvents (DESs) have emerged as promising solvents for biocatalysis. While their impact on enzyme solvation and stabilization has been studied for several enzyme classes, their role in substrate binding is yet to be investigated. Herein, molecular dynamics (MD) simulations of horse-liver alcohol dehydrogenase (HLADH) are performed in choline chloride-ethylene glycol (ChCl-EG) and choline chloride-glycerol (ChCl-Gly) at varying water concentrations.

Maximal mouth opening in infants and toddlers with spinal muscular atrophy: a prospective controlled study.

Background: Bulbar function is frequently impaired in patients with spinal muscular atrophy (SMA). Although extremely important for the patient's quality of life, it is difficult to address therapeutically. Due to bulbar dysfunction, maximum mouth opening (MMO) is suspected to be reduced in children with SMA.

Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping.

Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. In particular, efficient energy estimation may be crucial in achieving a quantum advantage for a practically relevant problem. For instance, the measurement effort poses a critical bottleneck for variational quantum algorithms.

Size-dependent phase change in energy storage materials: Comparing the impact of solid-state wetting and of coherency stress.

Coherent phase transformations in interstitial solid solutions or intercalation compounds with a miscibility gap are of practical relevance for energy storage materials and specifically for metal hydride or lithium-ion compound nanoparticles. Different conclusions on the size-dependence of the transformation conditions are reached by modeling or theory focusing on the impact of either one (internal, solid-state-) critical-point wetting of the nanoparticle surface or coherency constraints from solute-saturated surface layers. We report a hybrid numerical approach, combining atomistic grand canonical Monte Carlo simulation with a continuum mechanics analysis of coherency stress and modeling simultaneously wetting and mechanical constraints.

Unpacking Performance Factors of Innovation Systems and Studying Germany's Attempt to Foster the Role of the Patient Through a Market Access Pathway for Digital Health Applications (DiGAs): Exploratory Mixed Methods Study.

Background: Health care innovation faces significant challenges, including system inertia and diverse stakeholders, making regulated market access pathways essential for facilitating the adoption of new technologies. The German Digital Healthcare Act, introduced in 2019, offers a model by enabling digital health applications (DiGAs) to be reimbursed by statutory health insurance, improving market access and patient empowerment. However, the factors influencing the success of these pathways in driving innovation remain unclear.

Structures of Gas-Phase Hydrated Phosphotyrosine Revealed by Soft X-ray Action Spectroscopy.

Gas-phase near-edge X-ray absorption mass spectrometry (NEXAMS) was employed at the carbon and oxygen K-edges to probe the influence of a single water molecule on the protonated phosphotyrosine molecule. The results of the photodissociation experiments revealed that the water molecule forms two bonds, with the phosphate group and another chemical group. By comparing the NEXAMS spectra at the carbon and oxygen K-edges with density functional theory calculations, we attributed the electronic transitions responsible for the observed resonances, especially the transitions due to the presence of the water molecule.

Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.

Objective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.

Large language models for human-machine collaborative particle accelerator tuning through natural language.

Autonomous tuning of particle accelerators is an active and challenging research field with the goal of enabling advanced accelerator technologies and cutting-edge high-impact applications, such as physics discovery, cancer research, and material sciences. A challenge with autonomous accelerator tuning remains that the most capable algorithms require experts in optimization and machine learning to implement them for every new tuning task. Here, we propose the use of large language models (LLMs) to tune particle accelerators.

Lignin Polyurethane Aerogels: Influence of Solvent on Textural Properties.

This study explores the innovative potential of native lignin as a sustainable biopolyol for synthesizing polyurethane aerogels with variable microstructures, significant specific surface areas, and high mechanical stability. Three types of lignin-Organosolv, Aquasolv, and Soda lignin-were evaluated based on structural characteristics, Klason lignin content, and particle size, with Organosolv lignin being identified as the optimal candidate. The microstructure of lignin polyurethane samples was adjustable by solvent choice: Gelation in DMSO and pyridine, with high affinity to lignin, resulted in dense materials with low specific surface areas, while the use of the low-affinity solvent e.

Overlooked Impact of Moisture on the Stability of Printing Ink and Its Impact on Recycled Low-Density Polyethylene (LDPE) Quality.

Printing inks, composed of binders, pigments, and additives, are essential components in plastic packaging but complicate recycling due to plastic contamination and degradation. While polyolefins are resistant to hydrolytic degradation, moisture generated from upstream cleaning processes, which is often ignored, can accelerate the degradation of ink binders, affecting the recyclate quality. This study has examined the impact of 3 wt.

Efficiency and process development for microbial biomass production using oxic bioelectrosynthesis.

Autotrophic microbial electrosynthesis (MES) processes are mainly based on organisms that rely on carbon dioxide (CO) as an electron acceptor and typically have low biomass yields. However, there are few data on the process and efficiencies of oxic MES (OMES). In this study, we used the knallgas bacterium Kyrpidia spormannii to investigate biomass formation and energy efficiency of cathode-dependent growth.

Structuring of electrorheological fluids in polymer matrices for miniature actuators.

Miniature actuators are utilized in various application fields, from robotics to medical devices, where compact dimensions, precise movements, and cost-effectiveness are crucial factors. Particularly for applications like braille displays, there is a critical demand for lightweight, portable, and affordable actuators to integrate into daily life for visually impaired people. However, existing actuation technologies such as electroactive polymers, electrorheological materials, and piezoelectric elements often do not meet the specific requirements of miniature actuators, especially for braille displays.

Contactless radar-based heart rate estimation in palliative care - a feasibility study and possible use in symptom management.

Background: Heart rate (HR) monitoring is a medical standard to provide information about a patient's health status. In palliative care, relationship and social engagement are crucial therapeutic concepts. For fear of disrupting communication, social contact, and care, continuous HR monitoring is underutilised despite its potential to inform on symptom burden and therapeutic effects.

Strategic improvement of Shewanella oneidensis for biocatalysis: Approach to media refinement and scalable application in a microbial electrochemical system.

Microbial electrochemical systems offer a sustainable method for the conversion of chemical energy into electrical energy or hydrogen and the production of valuable compounds, contributing to the development of a bio-based economy. This study aimed to enhance the performance of anodic bioelectrochemical systems by improving the current density of Shewanella oneidensis as a biocatalyst through strain modification and medium refinement. The genetic modification, combining the prophage deletion and overexpression of the speC gene, resulted in a 4.

Spatially Localized Visual Perception Estimation by Means of Prosthetic Vision Simulation.

Retinal prosthetic devices aim to repair some vision in visually impaired patients by electrically stimulating neural cells in the visual system. Although there have been several notable advancements in the creation of electrically stimulated small dot-like perceptions, a deeper comprehension of the physical properties of phosphenes is still necessary. This study analyzes the influence of two independent electrode array topologies to achieve single-localized stimulation while the retina is electrically stimulated: a two-dimensional (2D) hexagon-shaped array reported in clinical studies and a patented three-dimensional (3D) linear electrode carrier.

Illuminating bioprocess responses to metal-based nanoparticles addition along hydrogen and methane production pathways: A review.

Recent research has discussed the positive impacts of metal-based nanoparticles (NPs) on bioprocesses producing either hydrogen (H) or methane (CH). The enhancement has been explained by mechanisms such as direct interspecies electron transfer (DIET), metal corrosion, and dissimilatory reduction. Such interactions could induce further benefits, such as controlling oxidation-reduction potential (ORP), mitigating toxicants, promoting enzymatic activity, and altering the microbiome, which have not yet been comprehensively discussed.

Role of slice thickness quantification in the 3D reconstruction of FIB tomography data of nanoporous materials.

In focused ion beam (FIB) tomography, a combination of FIB with a scanning electron microscope (SEM) is used for collecting a series of planar images of the microstructure of nanoporous materials. These planar images serve as the basis for reconstructing the three-dimensional microstructure through segmentation algorithms. However, the assumption of a constant distance between consecutively imaged sections is generally invalid due to random variations in the FIB milling process.

Machine learning reveals correlations between brain age and mechanics.

Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and diagnostic tools. Here we systematically probed the mechanical behavior of n=439 brain tissue samples in tension and compression, in different anatomical regions, for different axon orientations, across five age groups.

Information Causality as a Tool for Bounding the Set of Quantum Correlations.

Information causality was initially proposed as a physical principle aimed at deriving the predictions of quantum mechanics on the type of correlations observed in the Bell experiment. In the same work, information causality was famously shown to imply the Uffink inequality that approximates the set of quantum correlations and rederives Tsirelson's bound of the Clauser-Horne-Shimony-Holt inequality. This result found limited generalizations due to the difficulty of deducing implications of the information causality principle on the set of nonlocal correlations.

Redox Biocatalysis in Lidocaine-Based Hydrophobic Deep Eutectic Solvents: Non-Conventional Media Outperform Aqueous Conditions.

Redox biocatalysis is an essential pillar of the chemical industry. Yet, the enzymes' nature restricts most reactions to aqueous conditions, where the limited substrate solubility leads to unsustainable diluted biotranformations. Non-aqueous media represent a strategic solution to conduct intensified biocatalytic routes.

Multiscale homogenized constrained mixture model of the bio-chemo-mechanics of soft tissue growth and remodeling.

Constrained mixture models have successfully simulated many cases of growth and remodeling in soft biological tissues. So far, extensions of these models have been proposed to include either intracellular signaling or chemo-mechanical coupling on the organ-scale. However, no version of constrained mixture models currently exists that includes both aspects.