Numerical experiments with MALDI Imaging data

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Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

vanishing viscosity, networks. This work has received funding from the Alexander von Humboldt-Professorship program, the Transregio 154 Project "Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks" of the DFG, the grant PI ...

Traditionally, mass spectrometry (MS) output is the ion abundance plotted versus the ionic mass-to-charge ratio m/z. While employing only commercially available equipment, Charge Determination Analysis (CHARDA) adds a third dimension to MS, estimating for ...

The role of Bifidobacterium species and microbial metabolites such as short-chain fatty acids (SCFAs) and human milk oligosaccharides in controlling intestinal inflammation and the pathogenesis of obesity and type 1 diabetes (T1D) has been largely studied ...

The local physical properties - such as shape and flexibility - of the DNA double-helix is today widely believed to be influenced by nucleic acid sequence in a non-trivial way. Furthermore, there is strong evidence that these properties play a role in many ...

Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of ...

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

Aortic diastolic pressure decay (DPD) has been shown to have considerable pathophysiological relevance in the assessment of vascular health, as it is significantly affected by arterial stiffening. Nonetheless, the aortic pressure waveform is rarely availab ...

Glycans play an essential role in numerous physiological and pathological processes of living organisms. Despite their significant biological relevance, glycobiology remains one of the least explored fields of biochemistry. The intrinsic isomeric complexit ...

The structural elucidation of metabolite molecules is important in many branches of the life sciences. However, the isomeric and isobaric complexity of metabolites makes their identification extremely challenging, and analytical standards are often require ...

Glycans play a pivotal role in both physiological processes; however, the field of glycobiology remains relatively understudied within biochemistry. The intricate complexity of glycans, coupled with their abundance of isomeric forms, presents formidable ch ...

Author summaryIn recent years, the application of deep learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of the correct sequence of amino acids from observable MS spectra without prior knowledge, also known ...

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a funda-mental role for making forecasts, simulating scenarios and evaluating the impact of pre-ventive political, social and pharmaceutical measures. Optimal control theory represent ...

Over the course of a lifetime, the human brain acquires an astonishing amount of semantic knowledge and autobiographical memories, often with an imprinting strong enough to allow detailed information to be recalled many years after the initial learning exp ...

Polymerase chain reaction (PCR) has been the most significant driver in the field of nucleic acid testing (NAT) since its invention. Popularized as an abbreviation by the Covid-19 pandemic, PCR-based methods are the gold standard in the field of diagnostic ...

A key player in the formation of endoplasmic reticulum sheets is CLIMP-63, but mechanistic details remained elusive. Here authors combined cellular experiments and mathematical modelling to show that S-acylation of CLIMP-63 regulates its function by mediat ...

This dataset contains the bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) measured during the MOSAiC expedition from October 2019 to July 2020. These include the mass concentrations of sulfate (S ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

Under the trends of multifunctionality, tunability, and compactness in modern wave -based signal processors, in this paper, we propose a polarization-multiplexed graphene-based metasurface to realize distinct mathematical operators on the parallel time-dom ...

Numerical experiments with MALDI Imaging data

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Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

CONTROL OF HYPERBOLIC AND PARABOLIC EQUATIONS ON NETWORKS AND SINGULAR LIMITS

Adding Color to Mass Spectra of Biopolymers: Charge Determination Analysis (CHARDA) Assigns Charge State to Every Ion Peak

Innovative Biomarkers for Obesity and Type 1 Diabetes Based on Bifidobacterium and Metabolomic Profiling

Landscapes of DNA Mechanics and Genomes

Deep Learning Meets Sparse Regularization

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

On the similarity between aortic and carotid pressure diastolic decay: a mathematical modelling study

Combining ion mobility spectrometry and cryogenic ion spectroscopy with enzymatic digestion/synthesis for the study of N-linked glycans

New Approach for the Identification of Isobaric and Isomeric Metabolites

Advancing Glycan Analysis: Cryogenic Ion Spectroscopy for Database-Driven Identification

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario

A theory of memory consolidation and synaptic pruning in cortical circuits

Electronic readout of DNA amplification in nanoliter chambers. Strategies towards highly parallel semiconductor-based nucleic acid amplification testing

Dynamics of CLIMP-63 S-acylation control ER morphology

Bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols measured in the Swiss container during MOSAiC 2019/2020

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Parallel temporal signal processing enabled by polarization-multiplexed programmable THz metasurfaces