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Deuterium metabolic imaging (DMI) is a non-invasive magnetic resonance spectroscopic imaging technique enabling in vivo mapping of glucose metabolism. Dynamic DMI provides time-resolved metabolite maps and allows spatially resolved fitting of metabolic models to capture metabolite concentration dynamics. However, conventional fitting tools often require long processing times for high-resolution datasets, limiting their practical utility. To address this bottleneck, we propose a deep autoencoder (DAE) for rapid spectral–temporal fitting of dynamic DMI data, supporting arbitrary parametric model constraints to describe metabolite concentration dynamics. The DAE was benchmarked against spectral–temporal fitting using FSL-MRS and LCModel. Fitting accuracy was evaluated on in vivo and synthetic whole-brain dynamic DMI data acquired at 7T using Bland–Altman metrics, Pearson correlation coefficients, structural similarity index measures, and root mean squared errors for both metabolite concentrations and model constraint parameters. The DAE achieved processing times of 0.29 ms per voxel, corresponding to an acceleration of more than three orders of magnitude compared to LCModel/FSL-MRS (0.55/0.65 s per voxel). On in vivo data, it showed excellent agreement with LCModel, and on synthetic data, it consistently outperformed both reference methods across all evaluation metrics. The proposed DAE enables accurate spectral–temporal fitting for whole-brain dynamic DMI scans within less than a second, matching or exceeding the performance of conventional state-of-the-art fitting methods. This makes it a promising tool for integration into efficient post-processing pipelines for research and clinical DMI workflows. [Display omitted] •Deep Autoencoder approach for dynamic fitting of dynamic DMI data.•Proposed model shows strong agreement with reference standard on in vivo data.•Proposed model outperforms reference methods on synthetic fitting accuracy.•Proposed model fits whole-brain dynamic DMI datasets in under one second.•Proposed model achieves >1000× speedup over reference methods.

Human plasma is a biofluid that is high in information content, making it an excellent candidate for metabolomic studies. H NMR has been a popular technique to detect several dozen metabolites in blood plasma. In order for H NMR to become an automated, high-throughput method, challenges related to (1) the large signal from lipoproteins and (2) spectral overlap between different metabolites have to be addressed. Here diffusion-weighted H NMR is used to separate lipoprotein and metabolite signals based on their large difference in translational diffusion. The metabolite H NMR spectrum is then quantified through spectral fitting utilizing full prior knowledge on the metabolite spectral signatures. Extension of the scan time by 3 minutes or 15% per sample allowed the acquisition of a H NMR spectrum with high diffusion weighting. The metabolite H NMR spectra could reliably be modeled with 28 metabolites. Excellent correlation was found between results obtained with diffusion NMR and ultrafiltration. The combination of minimal sample preparation together with minimal user interaction during processing and quantification provides a metabolomics technique for automated, quantitative H NMR of human plasma.

This paper gives an overview of the use of polynomial chaos (PC) expansions to represent stochastic processes in numerical simulations. Several methods are presented for performing arithmetic on, as well as for evaluating polynomial and nonpolynomial functions of variables represented by PC expansions. These methods include {Taylor} series, a newly developed integration method, as well as a sampling-based spectral projection method for nonpolynomial function evaluations. A detailed analysis of the accuracy of the PC representations, and of the different methods for nonpolynomial function evaluations, is performed. It is found that the integration method offers a robust and accurate approach for evaluating nonpolynomial functions, even when very high-order information is present in the PC expansions.

Based on Fourier spectrum and k order Bessel function analysis of PWM (pulse width modulation) driving wave as a process or event flow in DC–DC converters, we verify that periodic PWM cannot change the characteristics of discrete spectrum distribution and suppress EMI (electromagnetic interference) of DC–DC converters effectively. But the chaotic PWM analyzed using random point process theory shows its power spectrum density is fully continuous and it can suppress the converter's EMI very well.

The first two chapters of this book offer a modern, self-contained exposition of the elementary theory of triangulated categories and their quotients. The simple, elegant presentation of these known results makes these chapters eminently suitable as a text for graduate students. The remainder of the book is devoted to new research, providing, among other material, some remarkable improvements on Brown's classical representability theorem. In addition, the author introduces a class of triangulated categories\"--the \"well generated triangulated categories\"--and studies their properties. This exercise is particularly worthwhile in that many examples of triangulated categories are well generated, and the book proves several powerful theorems for this broad class. These chapters will interest researchers in the fields of algebra, algebraic geometry, homotopy theory, and mathematical physics.

Microplastics are emerging contaminants that undergo progressive aging under environmental conditions such as sunlight irradiation, mechanical forces, temperature variations, and the presence of biological organisms. Since aging modifies microplastic properties, such as their own toxicity and the toxicity of trapped pollutants, advanced methods to analyze microplastics are required. Here we review methods to analyze microplastic aging with focus on the aging process, qualitative identification, quantitative characterization, and chemometrics. Qualitative identification is done by mechanical techniques, thermal techniques, e.g., thermal degradation and gas chromatography–mass spectrometry, and spectral techniques, e.g., infrared, Raman, fluorescent, and laser techniques. Quantitative characterization is done by microscopy and mass spectrometry. Microplastic aging results in a series of surface physical changes, biofilm formation, chemical oxidation, thermal alternation, and mechanical deterioration. Changes in mechanical and thermal properties allow to differentiate aged microplastics. Infrared and Raman spectroscopy are rapid and sensitive for chemical identification of microplastics in complex environmental samples. Combining two techniques is preferable for accurate detection and categorization.

A major goal of proteomics research is the accurate and sensitive identification and quantification of a broad range of proteins within a sample. Data-independent acquisition (DIA) approaches that acquire MS/MS spectra independently of precursor information have been developed to overcome the reproducibility challenges of data-dependent acquisition and the limited breadth of targeted proteomics strategies. Typical DIA implementations use wide MS/MS isolation windows to acquire comprehensive fragment ion data. However, wide isolation windows produce highly chimeric spectra, limiting the achievable sensitivity and accuracy of quantification and identification. Here, we present a DIA strategy in which spectra are collected with overlapping (rather than adjacent or random) windows and then computationally demultiplexed. This approach improves precursor selectivity by nearly a factor of 2, without incurring any loss in mass range, mass resolution, chromatographic resolution, scan speed, or other key acquisition parameters. We demonstrate a 64% improvement in sensitivity and a 17% improvement in peptides detected in a 6-protein bovine mix spiked into a yeast background. To confirm the method’s applicability to a realistic biological experiment, we also analyze the regulation of the proteasome in yeast grown in rapamycin and show that DIA experiments with overlapping windows can help elucidate its adaptation toward the degradation of oxidatively damaged proteins. Our integrated computational and experimental DIA strategy is compatible with any DIA-capable instrument. The computational demultiplexing algorithm required to analyze the data has been made available as part of the open-source proteomics software tools Skyline and msconvert (Proteowizard), making it easy to apply as part of standard proteomics workflows.Graphical Abstractᅟ

Model reduction via spectral submanifolds (SSMs) has displayed benefits such as the facilitation of nonlinear analysis and significant speed-up gains. One needs the sensitivity of the SSM-based model reduction to carry over these benefits to the settings of optimal design, modal updating, and uncertainty quantification of high-dimensional nonlinear mechanical systems. Here, we construct explicit third-order, SSM-based model reduction for general mechanical systems. We further derive the explicit sensitivity of the third-order SSM-based reduction using direct and adjoint methods. We demonstrate the effectiveness of the derived explicit sensitivity via a few examples with increasing complexity. We also show that the obtained sensitivity can be used to effectively construct perturbed SSMs, backbone curves, and forced response curves.

Despite the importance of uncertainties encountered in climate model simulations, the fundamental mechanisms at the origin of sensitive behavior of long-term model statistics remain unclear. Variability of turbulent flows in the atmosphere and oceans exhibits recurrent large-scale patterns. These patterns, while evolving irregularly in time, manifest characteristic frequencies across a large range of time scales, from intraseasonal through interdecadal. Based on modern spectral theory of chaotic and dissipative dynamical systems, the associated low-frequency variability may be formulated in terms of Ruelle-Pollicott (RP) resonances. RP resonances encode information on the nonlinear dynamics of the system, and an approach for estimating them—as filtered through an observable of the system—is proposed. This approach relies on an appropriate Markov representation of the dynamics associated with a given observable. It is shown that, within this representation, the spectral gap—defined as the distance between the subdominant RP resonance and the unit circle—plays a major role in the roughness of parameter dependences. The model statistics are the most sensitive for the smallest spectral gaps; such small gaps turn out to correspond to regimes where the low-frequency variability is more pronounced, whereas autocorrelations decay more slowly. The present approach is applied to analyze the rough parameter dependence encountered in key statistics of an El-Niño–Southern Oscillation model of intermediate complexity. Theoretical arguments, however, strongly suggest that such links between model sensitivity and the decay of correlation properties are not limited to this particular model and could hold much more generally.

The cluster counting method (including all Cs complexes missed out in the usage of single Cs complex per element) has previously been reported to provide a significantly better composition estimate than using a single Cs complex per element, for a D9 steel sample. The current study evaluates this method with a nitrogen-implanted zirconium specimen with high oxygen impurity concentrations. Therefore, this system is prone to experiencing strong matrix effects. Quantified elemental depth profiles of the specimen were obtained using XPS to compare with SIMS results. The study confirms that cluster counting of Cs complexes yields significantly better composition estimates than the single Cs complex approach. While fluorine and chlorine are trace-level impurities, their concentrations dominate the composition estimates in both cluster counted and single Cs-complex approaches because the complexes of halogens exhibit orders of magnitude higher sensitivities than those of the other elements. Applying relative sensitivity factors of a few tens for these species renders the concentrations of these impurities insignificant. With this correction, the composition estimates by both Cs complex methods improved towards that provided by XPS. At this stage also, the match of the cluster-counted composition with the XPS estimate remains markedly better than that by the single Cs-complex estimate. Additionally, the sensitivity factor values for all cluster-counted Cs complexes were incorporated and adjusted to ensure that the elemental compositions obtained by SIMS matched exactly with those determined by XPS. These relative sensitivity factors are also provided for academic purposes, aiding future studies on secondary ion and cluster formation. Notably, a comparable match could not be achieved between the single CsX species and the XPS profiles, even when varying the sensitivity factor values for the individual CsX species. Furthermore, the cluster counting method provides a means to estimate the sputter rate at each depth of the depth profile.