Peak Alignment Research Articles

The vestibular implant: effects of stimulation parameters on the electrically-evoked vestibulo-ocular reflex

IntroductionThe vestibular implant is a neuroprosthesis which offers a potential treatment approach for patients suffering from vestibulopathy. Investigating the influence of electrical stimulation parameters is essential to improve the vestibular implant response. Optimization of the response focuses on the electrically evoked vestibulo-ocular reflex. It aims to facilitate high peak eye velocities and adequate alignment of the eye movement responses. In this study, the basic stimulation parameters of the vestibular implant were tested for their effect on the electrically evoked vestibulo-ocular reflex.MethodsFour stimulation parameters, including the stimulation amplitude, phase duration, stimulus rate and speed of change of stimulation, were systematically tested in a cohort of nine subjects with a vestibulo-cochlear implant. These parameters were tested to evaluate their effect on fitting settings (i.e., threshold of activation, upper comfortable limit and dynamic range) as well as on the electrically evoked vestibulo-ocular reflex (peak eye velocity and alignment).ResultsIt was confirmed that, in addition to current amplitude, the peak eye velocity of the response can be increased by increasing the phase duration and pulse rate. Both parameters have little effect on the alignment of the eye response. However, a longer phase duration decreased the range between the threshold of activation and the upper comfortable limit of the electrical stimulation (i.e., dynamic range). Furthermore, these results show that next to the amplitude of the stimulation, the speed of change in stimulation has a determinative positive effect on the peak eye velocity.ConclusionThe observations in this study imply that the vestibular implant response, in terms of peak eye velocity, can be optimized with a higher pulse rate and longer phase duration. However, this comes at a trade-off between the dynamic range and power consumption. This study provides essential insights for fitting strategies in future vestibular implant care.

Gcxgclab: An R Package for Two-Dimensional Gas Chromatography Preprocessing and Analysis.

Comprehensive two-dimensional gas chromatography (GC×GC) is an established technique capable of chromatographically separating thousands of analytes in complex matrices. When coupled with highly sensitive detectors such as a high-resolution mass spectrometer, these instruments produce large multidimensional data sets. A prevailing challenge for GC×GC users is efficient data handling and analysis. Although commercial software packages exist for GC×GC data analysis, these platforms are typically optimized for low-throughput qualitative data interpretation utilizing desktop computer systems. Additionally, commercial GC×GC data processing packages offer little flexibility to explore custom or novel data processing concepts. In this work, an open-source R package, called "gcxgclab", was developed as an alternative data processing package for GC×GC users and offers data preprocessing and analysis functions including baseline correction, smoothing, phase shift, peak detection, peak alignment, extracted ion chromatogram, mass spectra extraction, mass defect analysis, and targeted and nontargeted analysis. This package was designed to be customizable and allows for batch data processing on desktops or high-performance computing systems to increase the throughput. gcxgclab is available for free on the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/gcxgclab/index.html.

Met4DX: A Unified and Versatile Data Processing Tool for Multidimensional Untargeted Metabolomics Data.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful tool in untargeted metabolomics, enabling the high-sensitivity and high-specificity characterization of metabolites. The integration of ion mobility (IM) with LC-MS, known as LC-IM-MS, enhances the analytical depth, facilitating more comprehensive metabolite profiling. However, the complexity of data generated by these technologies presents significant challenges in data processing. Addressing these challenges, we developed Met4DX, a unified and versatile software tool for processing both 3D and 4D untargeted metabolomics data. Met4DX incorporates a new MS1-oriented peak detection approach coupled with our bottom-up assembly algorithm, enabling highly sensitive and comprehensive peak detection in untargeted metabolomics data. Additionally, Met4DX employs a uniform quantification strategy to enhance the precision of peak integration across different samples. The software provides a user-friendly interface that simplifies data processing with default parameter sets, consolidating peak detection, alignment, quantification, and other procedures into a single streamlined workflow. Together, Met4DX offers a comprehensive solution for multidimensional metabolomics data processing, transforming raw data from diverse MS instruments into a final feature table containing quantification and identification results. We postulate Met4DX facilitates metabolite discovery in biological samples by deciphering the complex untargeted metabolomics data. Met4DX is freely available on the Internet (https://met4dx.zhulab.cn/).

B-274 Determining Novel Biomarker Candidates Associated With Heroin and Cocaine Exposure Using Urine Metabolomics

Abstract Background Substance abuse poses an escalating health risk in the United States. While acute drug exposure can be tracked using drugs and their metabolites as biomarkers in clinical settings, there is a notable absence of biomarkers for chronic drug exposure or addiction. The development and availability of such biomarkers could significantly enhance clinicians’ ability to identify patients at high risk of addiction, and more accurately predict disease progression and treatment responses. Methods Our study aimed to discover novel biomarkers associated with substance abuse by analyzing raw mass spectrometry (MS) data from urine comprehensive drug screening (UCDS) conducted at the University of Pittsburgh Medical Center (UPMC). The MS data, acquired through untargeted data collection using LC-qToF-MS at the UPMC Clinical Toxicology Laboratory, was initially preprocessed (including peak identification, alignment, and putative annotation) using MS-DIAL to generate a data matrix. This matrix, consisting of objects (cases) and features (m/z and retention time), was further normalized and analyzed using MetaboAnalyst. A combination of statistical methods, including point biserial correlation analysis, volcano plot, significance analysis of microarrays and metabolites (SAM), empirical Bayesian analysis of microarrays and metabolites (EBAM), partial least squares discriminant analysis (PLS-DA), and random forest (RF), were employed to select features significantly associated with the EIA-results. These features were subsequently evaluated for analyte identification using MS-FINDER. Results Our analysis identified nine significant features associated with 6MAM-EIA for heroin abuse, including 6-monoacetylmorphine itself and norfentanyl. For features associated with cocaine metabolite-EIA, 37 significant features were selected, including multiple cocaine metabolites, nicotine metabolites, and norfentanyl. Some of these selected features are likely products of in-source fragmentation or endogenous metabolites. Conclusions Our study has identified a set of potential biomarker candidates for illicit drug exposure. Notably, norfentanyl was found to be significantly associated with both 6MAM-EIA and cocaine metabolite-EIA, reflecting current trends in illicit drug use. Further chemical identification of these biomarkers is planned for future work.

Topological DNA blends exhibit resonant deformation fields and strain propagation dynamics tuned by steric constraints

Understanding how polymers deform in response to local stresses and strains, and how strains propagate from a local disturbance, are grand challenges in wide-ranging fields from materials manufacturing to cell mechanics. These dynamics are particularly complex for blends of polymers of distinct topologies, for which several different species-dependent mechanisms may contribute. Here, we use OpTiDDM (Optical-Tweezers-integrating-Differential-Dynamic-Microscopy) to elucidate deformation fields and propagation dynamics of binary blends of linear, ring and supercoiled DNA of varying sizes. We reveal robust non-monotonic dependence of strain alignment and superdiffusive transport with strain rate. However, peak alignment and superdiffusivity are surprisingly decoupled, occurring at different strain rates resonant with the distinct relaxation rates of the different topologies. Despite this universal resonance, we find that strain propagation of ring-linear blends is dictated by entanglements while supercoiled-ring blends are governed by Rouse dynamics. Our results capture critical subtleties in propagation and deformation dynamics of topological blends, shedding new light on the governing physics and offering a route towards decoupled tuning of response features. We anticipate our approach to be broadly generalizable to mapping the deformation dynamics of polymer blends, with an eye towards bottom-up bespoke materials design. Statement of SignificanceIn biology and in manufacturing, biomaterials are often subject to localized and spatially nonuniform strains and stresses. Yet, understanding the extent to which strains are absorbed, distributed, or propagated across different spatiotemporal scales remains a grand challenge. Here, we combine optical tweezers with differential dynamic microscopy to elucidate deformation fields and propagation dynamics of blends of linear, ring and supercoiled DNA, revealing robust non-monotonic trends and decoupling of strain alignment and superdiffusivity, and capturing critical subtleties in propagation and deformation dynamics. Our results, shedding important new physical insight to guide decoupled tuning of response features, may be leveraged to map the deformation dynamics of wide-ranging systems of biopolymers and other macromolecules, with an eye towards bottom-up bespoke biomaterials design.

Coupled pulsatile vascular and paravascular fluid dynamics in the human brain

BackgroundCardiac pulsation propels blood through the cerebrovascular network to maintain cerebral homeostasis. The cerebrovascular network is uniquely surrounded by paravascular cerebrospinal fluid (pCSF), which plays a crucial role in waste removal, and its flow is suspected to be driven by arterial pulsations. Despite its importance, the relationship between vascular and paravascular fluid dynamics throughout the cardiac cycle remains poorly understood in humans.MethodsIn this study, we developed a non-invasive neuroimaging approach to investigate the coupling between pulsatile vascular and pCSF dynamics within the subarachnoid space of the human brain. Resting-state functional MRI (fMRI) and dynamic diffusion-weighted imaging (dynDWI) were retrospectively cardiac-aligned to represent cerebral hemodynamics and pCSF motion, respectively. We measured the time between peaks (∆TTP) in ddϕfMRI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{d}{d\\phi }fMRI$$\\end{document} and dynDWI waveforms and measured their coupling by calculating the waveforms correlation after peak alignment (correlation at aligned peaks). We compared the ∆TTP and correlation at aligned peaks between younger [mean age: 27.9 (3.3) years, n = 9] and older adults [mean age: 70.5 (6.6) years, n = 20], and assessed their reproducibility within subjects and across different imaging protocols.ResultsHemodynamic changes consistently precede pCSF motion. ∆TTP was significantly shorter in younger adults compared to older adults (−0.015 vs. −0.069, p < 0.05). The correlation at aligned peaks were high and did not differ between younger and older adults (0.833 vs. 0.776, p = 0.153). The ∆TTP and correlation at aligned peaks were robust across fMRI protocols (∆TTP: −0.15 vs. −0.053, p = 0.239; correlation at aligned peaks: 0.813 vs. 0.812, p = 0.985) and demonstrated good to excellent within-subject reproducibility (∆TTP: intraclass correlation coefficient = 0.36; correlation at aligned peaks: intraclass correlation coefficient = 0.89).ConclusionThis study proposes a non-invasive technique to evaluate vascular and paravascular fluid dynamics. Our findings reveal a consistent and robust cardiac pulsation-driven coupling between cerebral hemodynamics and pCSF dynamics in both younger and older adults.

MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method

Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization.

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

Surface roughness evaluation when brushing heat-resistant alloy components

AbstractNowadays, the combination of additive manufacturing technologies, together with the use of materials such as Inconel 718, opens new opportunities in the manufacture of aeronautical turbines. However, parts obtained directly through additive manufacturing are far from being considered suitable in their initial state, as they exhibit a poor surface finish that compromises their mechanical characteristics. Common among the defects found in such components are ridges stemming from thermal deformations during the powder fusion process. Therefore, abrasive brushing emerges as the prevalent method to rectify these surface imperfections. However, conventional roughness indicators prove insufficient in accurately characterizing such local defects during surface analysis. Consequently, this study investigates surface conditions of Inconel 718 parts produced through additive manufacturing, particularly focusing on defects caused by peak alignments along the powder fusion direction. Thus, a series of frontal brushing operations were conducted under varying conditions, with the aim of increasing the feed rate to address these defects. Subsequently, the effectiveness of the brushing process was assessed through the calculation of peak areas. The analysis of this indicator revealed that the maximum feed rate enabling complete removal of the peak marks was 500 mm/min.

A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state

As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

Encoded Landscapes: A Link between Inka Wall Orientations and Andean Geomorphology

While some Inka structures and motifs align with astronomical and horizon markers, a significant portion of their constructions exhibit different patterns. We examined the potential correlation between the orientation patterns of the Inka walls and Andean geomorphology, aiming to uncover the extent to which the physical landscape guided these ancient architectural design methodologies. Using geospatial technology and specially developed peak detection and recognition software, we extensively and meticulously analyzed over 40,000 m of surveyed Inka walls and 20,000 mountain peaks across 11 distinct geographical areas. The analysis revealed a significant correlation between key wall orientations and the parallel peak alignment of the Andean Mountain Range. This suggests a purposeful encoding of landscape orientations into Inka architecture. These findings propose a novel perspective on the intricate relationship between Inka culture and the Andean highlands’ topography. Furthermore, this research introduces a distinctive methodological approach to exploring the impact of natural landscapes on architectural planning, establishing a foundation for comparative studies among other ancient civilizations.

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool.

We report here the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package that processes data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data visualization stack, including DataShader and Param, to create an interactive visualization of the mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS and that the visualization methods could be useful for other open-source mass spectrometry tools.

Evaluation of normalization strategies for GC-based metabolomics.

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results. Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies. Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data. Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes. TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

The Intonation of Peruvian Amazonian Spanish Declaratives: An Exploration of Spontaneous Speech

The present study explores intonational patterns in spontaneous speech in Peruvian Amazonian Spanish (PAS). The data came from 12 monolingual Spanish speakers in the city of Pucallpa, where the Spanish language has historically been in contact with the Amazonian language Shipibo-Konibo. The speakers responded to an open-ended prompt that elicited broad focus declaratives. Acoustic information from 1524 pitch accents was extracted from 194 sentences and analyzed using Praat. The analysis focused on five features: F0 rises, F0 peak alignment, downstepping, final lowering, and cases of stress clash. The results not only supported previous research on this variety that came from read speech tasks (e.g., F0 peaks consistently aligned with the stressed syllable), but also highlighted the importance of using multiple methodologies to gain a more comprehensive understanding of PAS prosody. Specifically, the varied sentence lengths and structures common in spontaneous speech provided new insights into downstepping, final lowering, and stress clash in PAS intonation. Overall, these results contribute to the growing literature on Spanish prosody in shared linguistic spaces and lend support for trends (such as F0 peak alignment) that have been reported in other language contact varieties.

MRMPro: a web-based tool to improve the speed of manual calibration for multiple reaction monitoring data analysis by mass spectrometry.

As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved. Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support. In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

Alignment method for metabolite chromatographic peaks using an N-acyl glycine retention index system

Peak alignment is a crucial data-processing step in untargeted metabolomics analysis that aims to integrate metabolite data from multiple liquid chromatography-mass spectrometry (LC-MS) batches for enhanced comparability and reliability. However, slight variations in the chromatographic separation conditions can result in retention time (RT) shifts between consecutive analyses, adversely affecting peak alignment accuracy. In this study, we present a retention index (RI)-based chromatographic peak-shift correction (CPSC) strategy to address RT shifts and align chromatographic peaks for metabolomics studies. A series of N-acyl glycine homologues (C2-C23) was synthesized as calibrants, and an LC RI system was established. This system effectively corrected RT shifts arising from variations in flow rate, gradient elution, instrument systems, and chromatographic columns. Leveraging the RI system, we successfully adjusted the RT of raw data to mitigate RT shifts and then implemented the Joint Aligner algorithm for peak alignment. We assessed the accuracy of the RI-based CPSC strategy using pooled human fecal samples as a test model. Notably, the application of the RI-based CPSC strategy to a long-term dataset spanning 157 d as an illustration revealed a significant enhancement in peak alignment accuracy from 15.5% to 80.9%, indicating its ability to substantially improve peak-alignment precision in multibatch LC-MS analyses.

Development of an angle-adjustable photonic crystal fluorescence platform for sensitive detection of oxytetracycline.

We pioneered an angle-adjustable photonic crystal fluorescence platform (APC-Fluor) that integrates PCs, an angular resolution spectrometer and a strategically aligned laser source. This configuration, featuring a coaxial rotating swing arm, allows for precise control over the angles of incidence and emission. The presence of photonic crystal microcavities facilitates the dispersion of fluorescent materials and promotes the transition of electrons from the excited state to the lowest vibrational energy level. The optical resonance effect triggered by modulating the alignment of the reflection peaks of the photonic crystals with the emission peaks of the fluorescent materials can significantly enhance the fluorescence intensity. Compared with the single BSA-AuNCs, the optimized fluorescence intensity can be significantly increased by 11.9-fold. The APC-Fluor system showcases rapid and highly sensitive detection capabilities for oxytetracycline (OTC), exhibiting a response across a concentration range from 2 to 1 × 104 nM and achieving a notably low detection limit of 1.03 nM.

Understanding 2p core-level excitons of late transition metals by analysis of mixed-valence copper in a metal-organic framework.

The L2,3-edge X-ray absorption spectra of late transition metals such as Cu, Ag, and Au exhibit absorption onsets lower in energy for higher oxidation states, which is at odds with the measured spectra of earlier transition metals. Time-dependent density functional theory calculations for Cu2+/Cu+ reveal a larger 2p core-exciton binding energy for Cu2+, overshadowing shifts in single-particle excitation energies with respect to Cu+. We explore this phenomenon in a Cu+ metal-organic framework with ∼12% Cu2+ defects and find that corrections with self-consistent excited-state total energy differences provide accurate XAS peak alignment.

Multiplet-Assisted Peak Alignment for 1H NMR-Based Metabolomics.

NMR-based metabolomics aims at recovering biological information by comparing spectral data from samples of biological interest and appropriate controls. Any statistical analysis performed on the data matrix relies on the proper peak alignment to produce meaningful results. Through the last decades, several peak alignment algorithms have been proposed, as well as alternatives like spectral binning or strategies for annotation and quantification, the latter depending on reference databases. Most of the alignment algorithms, mainly based on segmentation of the spectra, present limitations for regions with peak overlap or cases of frequency order exchange. Here, we present our multiplet-assisted peak alignment algorithm, a new methodology that consists of aligning peaks by matching multiplet profiles of f1 traces from J-resolved spectra. A correspondence matrix with the linked f1 traces is built, and multivariate data analysis can be performed on it to obtain useful information from the data, overcoming the issues of peak overlap and frequency crossovers. Statistical total correlation spectroscopy can be applied on the matrix as well, toward a better identification of molecules of interest. The results can be queried on one-dimensional (1D) 1H databases or can be directly coupled to our previously published Chemical Shift Multiplet Database.

Vocative Intonation in Language Contact: The Case of Bulgarian Judeo-Spanish

The present study investigates the prosodic realization of calling contours by bilingual speakers of Bulgarian and (Bulgarian) Judeo-Spanish and monolingual speakers of Bulgarian in a discourse completion task across three pragmatic contexts: (i) neutral (routine) context—calling a child from afar to come in for dinner; (ii) positive context—calling a child from afar to get a present; and (iii) negative (or urgent) context—calling a child from afar for a chastising. Through quantitative analyses of the F0 span between tonal landmarks, alignment of pitch peaks, intensity, and durational and prominence patterns, we systematically account for the phonetic characteristics of the contours and determine their tonal composition and meaning, thereby situating them within the intonation systems of Bulgarian Judeo-Spanish and Bulgarian. It is shown that both languages use the same inventory of contours: (1) L+H* !H-% (the so-called “vocative chant”), (2) L+H* H-L%, and (3) L+H* L-%. However, their distribution differs across contexts and varieties. Monolingual and bilingual speakers of Bulgarian, on the one hand, predominantly use (1) and (2) in neutral and positive contexts and clearly prefer (3) in negative contexts. In Bulgarian Judeo-Spanish, the bilinguals also more often recur to (3) in neutral and positive contexts and generally show more variation.