Anisotropic interactions for continuum modeling of protein-membrane systems.

In this work, a model for anisotropic interactions between proteins and cellular membranes is proposed for large-scale continuum simulations. The framework of the model is based on dynamic density functional theory, which provides a formalism to describe the lipid densities within the membrane as continuum fields while still maintaining the fidelity of the underlying molecular interactions. Within this framework, we extend recent results to include the anisotropic effects of protein-lipid interactions.

Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today.

An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities.

Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However, achieving low latency and high classification performance remains challenging due to the inherent variability of electroencephalographic (EEG) signals across sessions and the limited onboard resources. This work proposes a comprehensive BMI workflow based on a CNN-based Continual Learning (CL) framework, allowing the system to adapt to inter-session changes.

ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling.

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems.

Building complex membranes with Martini 3.

The Martini model is a popular force field for coarse-grained simulations. Membranes have always been at the center of its development, with the latest version, Martini 3, showing great promise in capturing more and more realistic behavior. In this chapter we provide a step-by-step tutorial on how to construct starting configurations, run initial simulations and perform dedicated analysis for membrane-based systems of increasing complexity, including leaflet asymmetry, curvature gradients and embedding of membrane proteins.

Experimental and Theoretical Study on Electron Ionization and Fragmentation of Propylene Oxide─the First Chiral Molecule Detected in the Interstellar Medium.

Propylene oxide, CHCHOCH, is the first chiral molecule detected in space and the third C3 oxide detected toward the Sagittarius B2 (Sgr B2 (N)) molecular cloud, the others being propanal, CHCHCHO, and acetone, (CH)CO. With homochirality being ubiquitous in the building blocks of living matter, the formation and decay paths of propylene oxide in space are of specific interest. Motivated by the significant role of photo- and secondary electrons in astrochemistry, we have studied electron ionization and fragmentation of propylene oxide.

Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer.

Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs.

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable devices to automatically detect seizures in epileptic patients is a high-potential application field for deep neural networks, and specifically for transformers, which are highly suited for end-to-end time series processing without handcrafted feature extraction. In this work, we propose a small-scale transformer detector, the EEGformer, compatible with unobtrusive acquisition setups that use only the temporal channels. EEGformer is the result of a hardware-oriented design exploration, aiming for efficient execution on tiny low-power micro-controller units (MCUs) and low latency and false alarm rate to increase patient and caregiver acceptance.

A combined gas-phase dissociative ionization, dissociative electron attachment and deposition study on the potential FEBID precursor [Au(CH)Cl].

Motivated by the potential of focused-electron-beam-induced deposition (FEBID) in the fabrication of functional gold nanostructures for application in plasmonic and detector technology, we conducted a comprehensive study on [Au(CH)Cl] as a potential precursor for such depositions. Fundamental electron-induced dissociation processes were studied under single collision conditions, and the composition and morphology of FEBID deposits fabricated in an ultrahigh-vacuum (UHV) chamber were explored on different surfaces and at varied beam currents. In the gas phase, dissociative ionization was found to lead to significant carbon loss from this precursor, and about 50% of the chlorine was on average removed per dissociative ionization incident.

Computationally Designed Molecules Modulate ALS-Related Amyloidogenic TDP-43 Aggregation.

Abnormal cytosolic aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is observed in multiple diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration, and Alzheimer's disease. Previous studies have shown that TDP-43 located at the C-terminal of TDP-43 can form higher-order oligomers and fibrils. Of particular interest are the hexamers that adopt a cylindrin structure that has been strongly correlated to neurotoxicity.

Locating helicopter ambulance bases in Iceland: efficient and fair solutions.

Background: Fixed-wing air ambulances play an important role in healthcare in rural Iceland. More frequent use of helicopter ambulances has been suggested to shorten response times and increase equity in access to advanced emergency care. In finding optimal base locations, the objective is often efficiency-maximizing the number of individuals who can be reached within a given time.

Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation.

The RNA-binding protein TDP-43 is associated with mRNA processing and transport from the nucleus to the cytoplasm. TDP-43 localizes in the nucleus as well as accumulating in cytoplasmic condensates such as stress granules. Aggregation and formation of amyloid-like fibrils of cytoplasmic TDP-43 are hallmarks of numerous neurodegenerative diseases, most strikingly present in >90% of amyotrophic lateral sclerosis (ALS) patients.

Martini 3 Coarse-Grained Force Field for Cholesterol.

Cholesterol plays a crucial role in biomembranes by regulating various properties, such as fluidity, rigidity, permeability, and organization of lipid bilayers. The latest version of the Martini model, Martini 3, offers significant improvements in interaction balance, molecular packing, and inclusion of new bead types and sizes. However, the release of the new model resulted in the need to reparameterize many core molecules, including cholesterol.

An anchor-tether 'hindered' HCN1 inhibitor is antihyperalgesic in a rat spared nerve injury neuropathic pain model.

Background: Neuropathic pain impairs quality of life, is widely prevalent, and incurs significant costs. Current pharmacological therapies have poor/no efficacy and significant adverse effects; safe and effective alternatives are needed. Hyperpolarisation-activated cyclic nucleotide-regulated (HCN) channels are causally implicated in some forms of peripherally mediated neuropathic pain.

Low-energy electron interaction with 2-(trifluoromethyl)acrylic acid, a potential component for EUVL resist material.

Motivated by the current introduction of extreme ultraviolet lithography (EUVL) into chip manufacturing processes, and thus the transition to electron-induced chemistry within the respective resist materials, we have studied low energy electron-induced fragmentation of 2-(trifluoromethyl)acrylic acid (TFMAA). This compound is chosen as a potential resist component, whereby fluorination enhances the EUV adsorption and may at the same time promote electron-induced dissociation. Dissociative ionization and dissociative electron attachment are studied, and to aid the interpretation of the observed fragmentation channels, the respective threshold values are calculated at the DFT and coupled cluster level of theory.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials.

Inferring fish behaviour at the trawl mouth from escape location.

In this study, we used escape location underneath the trawl to understand groundfish herding behaviour at the trawl mouth. Three collecting bags (port, center, starboard) were mounted under the trawl and behind the footgear to collect escapees. The escape-at-length of species that escaped into the center bag were compared to the two wing bags to infer fish response behaviour, herding behaviour, and swimming capacity at the trawl mouth.

Energy-Efficient Tree-Based EEG Artifact Detection.

In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological simi-larity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform. The analyses are based on the TUH EEG Artifact Corpus dataset and focus on the temporal electrodes.

On the Electron-Induced Reactions of (CH)AuP(CH): A Combined UHV Surface Science and Gas-Phase Study.

Focused-electron-beam-induced deposition (FEBID) is a powerful nanopatterning technique where electrons trigger the local dissociation of precursor molecules, leaving a deposit of non-volatile dissociation products. The fabrication of high-purity gold deposits via FEBID has significant potential to expand the scope of this method. For this, gold precursors that are stable under ambient conditions but fragment selectively under electron exposure are essential.