On learning what to learn: Heterogeneous observations of dynamics and establishing possibly causal relations among them.

Before we attempt to (approximately) learn a function between two sets of observables of a physical process, we must first decide what the and of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding "the right quantities" to relate through such a function, and then proceeding to learn it. This is accomplished by first processing simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple of the system.

Concordance between pharmacy dispensing and electronic monitoring data of direct oral anticoagulants - A secondary analysis of the MAAESTRO study.

Background: Direct oral anticoagulants are the preferred treatment for stroke patients with atrial fibrillation. Pharmacy dispensing data represent a practical method to identify suboptimal medication adherence.

Objective: This study investigates whether pharmacy dispensing data are indicative of real-life adherence behavior, using data from 130 patients in the MAAESTRO study (2018-2022) in Basel, Switzerland.

Fulgide Derivatives as Photo-Switchable Coatings for Cathodes of Lithium Ion Batteries - A DFT Study.

Photo-switchable coatings for lithium ion batteries (LIB) can offer the possibility to control the diffusion processes from the electrode materials to the electrolyte and thus, for example, reducing the energy loss in the fully charged state. Fulgide derivatives, as known photo-switches, are investigated concerning their use as coating for vanadium pentoxide, a potential cathode material for LIB. With the help of Density Functional Theory calculations, two fulgide derivatives are characterized with respect to their photophysics, their aggregation behaviour on the cathode material and the ability to form self-assembled monolayers (SAM).

Distinct evolutionary trajectories following loss of RNA interference in .

While increased mutation rates typically have negative consequences in multicellular organisms, hypermutation can be advantageous for microbes adapting to the environment. Previously, we identified two hypermutator clinical isolates that rapidly develop drug resistance due to transposition of a retrotransposon, Cnl1. Cnl1-mediated hypermutation is caused by a nonsense mutation in the gene encoding a novel RNAi component, Znf3, combined with a tremendous transposon burden.

Intrinsically disordered sequences can tune fungal growth and the cell cycle for specific temperatures.

Temperature can impact every reaction essential to a cell. For organisms that cannot regulate their own temperature, adapting to temperatures that fluctuate unpredictably and on variable timescales is a major challenge. Extremes in the magnitude and frequency of temperature changes are increasing across the planet, raising questions as to how the biosphere will respond.

Transformations establishing equivalence across neural networks: When have two networks learned the same task?

Transformations are a key tool in the qualitative study of dynamical systems: transformations to a normal form, for example, underpin the study of instabilities and bifurcations. In this work, we test, and when possible establish, an equivalence between two different artificial neural networks by attempting to construct a data-driven transformation between them, using diffusion maps with a Mahalanobis-like metric. If the construction succeeds, the two networks can be thought of as belonging to the same equivalence class.

Dislocation Density-Mediated Functionality in Single-Crystal BaTiO.

Unlike metals where dislocations carry strain singularity but no charge, dislocations in oxide ceramics are characterized by both a strain field and a local charge with a compensating charge envelope. Oxide ceramics with their deliberate engineering and manipulation are pivotal in numerous modern technologies such as semiconductors, superconductors, solar cells, and ferroics. Dislocations facilitate plastic deformation in metals and lead to a monotonous increase in the strength of metallic materials in accordance with the widely recognized Taylor hardening law.

Exploring structures of small anionic nickel-ethanol clusters with infrared spectroscopy.

Small anionic nickel clusters with ethanol are investigated with a combination of mass-selective infrared photodissociation spectroscopy in a molecular beam and density functional theory simulations at the BLYP/6-311g(d,p) and TPSSh/def2-TZVPP level. In this context, the O-H stretching vibration of the ethanol is analyzed to obtain information about the structural motif, the geometry of the metal core, and the spin state of the clusters. For the [Ni2(EtOH)]- and [Ni3(EtOH)]- clusters, we assign quartet states of motifs with a hydrogen bond from the ethanol to the linear nickel core.

Relationship between electronically monitored adherence to direct oral anticoagulants and ischemic or hemorrhagic events after an initial ischemic stroke-A case control study.

Background: Patients with atrial fibrillation (AF) have a high risk for recurrent clinical events after an ischemic stroke. Direct oral anticoagulants (DOAC) are prescribed for secondary prevention. Adherence to DOAC is crucial mainly because of their short elimination half-life.

Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.

We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI time series. Our approach is a three stage one. First, we exploit manifold learning and, in particular, diffusion maps (DMs) to discover a set of variables that parametrize the latent space on which the emergent high-dimensional fMRI time series evolve.

Ground-Based Mobile Measurements to Track Urban Methane Emissions from Natural Gas in 12 Cities across Eight Countries.

To mitigate methane emission from urban natural gas distribution systems, it is crucial to understand local leak rates and occurrence rates. To explore urban methane emissions in cities outside the U.S.

Adherence Monitoring Package (AMoPac) in patients suspected of non-response to antihypertensive treatment: perceived usefulness by general practitioners.

Background: Non-adherence to antihypertensive agents is common, mainly because of the low perceived burden of high blood pressure. General practitioners (GPs) are unable to predict whether patients are adhering to a recommended treatment. Knowledge about adherence might be of clinical interest in patients non-responding to antihypertensive treatment.

Biomolecular condensates in fungi are tuned to function at specific temperatures.

Temperature can impact every reaction and molecular interaction essential to a cell. For organisms that cannot regulate their own temperature, a major challenge is how to adapt to temperatures that fluctuate unpredictability and on variable timescales. Biomolecular condensation offers a possible mechanism for encoding temperature-responsiveness and robustness into cell biochemistry and organization.

Investigating cooperative effects in small cobalt and cobalt-nickel alloy clusters with attached ethanol.

Small cationic cobalt, and cobalt-nickel alloy clusters with ethanol attached are generated in a pulsed molecular beam experiment using a laser ablation source. While the metal center is successively varied with respect to size and composition, a full-size study of these transition metal clusters is possible. The clusters are investigated IR photodissociation spectroscopy in the region of OH- and CH-stretching vibrations.

Machine Learning Nucleation Collective Variables with Graph Neural Networks.

The efficient calculation of nucleation collective variables (CVs) is one of the main limitations to the application of enhanced sampling methods to the investigation of nucleation processes in realistic environments. Here we discuss the development of a graph-based model for the approximation of nucleation CVs that enables orders-of-magnitude gains in computational efficiency in the on-the-fly evaluation of nucleation CVs. By performing simulations on a nucleating colloidal system mimicking a multistep nucleation process from solution, we assess the model's efficiency in both postprocessing and on-the-fly biasing of nucleation trajectories with pulling, umbrella sampling, and metadynamics simulations.

Intake reminders are effective in enhancing adherence to direct oral anticoagulants in stroke patients: a randomised cross-over trial (MAAESTRO study).

Background: Direct oral anticoagulants (DOAC) effectively prevent recurrent ischaemic events in atrial fibrillation (AF) patients with recent stroke. However, excellent adherence to DOAC is mandatory to guarantee sufficient anticoagulation as the effect quickly subsides.

Aim: To investigate the effect of intake reminders on adherence to DOAC.

The first HyDRA challenge for computational vibrational spectroscopy.

Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge.

RNA viruses, M satellites, chromosomal killer genes, and killer/nonkiller phenotypes in the 100-genomes S. cerevisiae strains.

We characterized previously identified RNA viruses (L-A, L-BC, 20S, and 23S), L-A-dependent M satellites (M1, M2, M28, and Mlus), and M satellite-dependent killer phenotypes in the Saccharomyces cerevisiae 100-genomes genetic resource population. L-BC was present in all strains, albeit in 2 distinct levels, L-BChi and L-BClo; the L-BC level is associated with the L-BC genotype. L-BChi, L-A, 20S, 23S, M1, M2, and Mlus (M28 was absent) were in fewer strains than the similarly inherited 2µ plasmid.

Intrinsic-Strain Engineering by Dislocation Imprint in Bulk Ferroelectrics.

We report an intrinsic strain engineering, akin to thin filmlike approaches, via irreversible high-temperature plastic deformation of a tetragonal ferroelectric single-crystal BaTiO_{3}. Dislocations well-aligned along the [001] axis and associated strain fields in plane defined by the [110]/[1[over ¯]10] plane are introduced into the volume, thus nucleating only in-plane domain variants. By combining direct experimental observations and theoretical analyses, we reveal that domain instability and extrinsic degradation processes can both be mitigated during the aging and fatigue processes, and demonstrate that this requires careful strain tuning of the ratio of in-plane and out-of-plane domain variants.

Structural Investigation of Small Nickel-Ethanol Clusters Using Vibrational Spectroscopy in a Molecular Beam.

The structural identification of small nickel clusters with ethanol can help to understand fundamental steps for heterogenous catalysis. We investigate the rows [Ni (EtOH) ] with x=1-4, and [Ni (EtOH) ] with y=1-3 via IR photodissociation spectroscopy in a molecular beam experiment. Analyzing the CH- and OH-stretching frequencies and comparing these experimental results with density functional theory (DFT) calculations on the PW91/6-311+G(d,p) level leads to the identification of intact motifs for all clusters and hints for C-O cleavage of the ethanol in two particular cases.

Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning.

We identify effective stochastic differential equations (SDEs) for coarse observables of fine-grained particle- or agent-based simulations; these SDEs then provide useful coarse surrogate models of the fine scale dynamics. We approximate the drift and diffusivity functions in these effective SDEs through neural networks, which can be thought of as effective stochastic ResNets. The loss function is inspired by, and embodies, the structure of established stochastic numerical integrators (here, Euler-Maruyama and Milstein); our approximations can thus benefit from backward error analysis of these underlying numerical schemes.

Development and acceptance of a new adherence monitoring package to identify non-adherent patients with polypharmacy in primary care: a feasibility study.

Background: Adherence to pharmacotherapy is crucial to prevent symptom deterioration in chronic diseases. However, non-adherence to chronic treatments is prevalent, especially in polypharmacy. Practical tools to assess adherence to polypharmacy in primary care are missing.