Heart-brain interactions in the MR environment: characterization of the ballistocardiogram in EEG signals collected during simultaneous fMRI

AbstractThe ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject’s scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. Also, we found positive correlations between heart rate variability and ECG-BCG delay. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Download Full-text

Generic monitoring tools for water characteristics assessment

Water Science & Technology Water Supply ◽

10.2166/ws.2003.0124 ◽

2003 ◽

Vol 3 (1-2) ◽

pp. 351-357

Author(s):

S. Le Bonté ◽

M.-N. Pons ◽

O. Potier ◽

S. Chanel ◽

M. Baklouti

Keyword(s):

Municipal Wastewater ◽

Principal Component ◽

Toxic Substances ◽

Capacity Curves ◽

Water Characteristics ◽

Monitoring Tools ◽

Visible Spectra ◽

On Line ◽

Mining Procedure

An adaptive principal component analysis applied to sets of data provided by global analytical methods (UV-visible spectra, buffer capacity curves, respirometric tests) is proposed as a generic procedure for on-line and fast characterization of wastewater. The data-mining procedure is able to deal with a large amount of information, takes into account the normal variations of wastewater composition related to human activity, and enables a rapid detection of abnormal situations such as the presence of toxic substances by comparison of the actual wastewater state with a continuously updated reference. The procedure has been validated on municipal wastewater.

Download Full-text

Accurate Tongue–Palate Pressure Sensing Device to Study Speech Production and Swallowing in Patients with Complete Denture

European Journal of Dentistry ◽

10.1055/s-0040-1717002 ◽

2021 ◽

Author(s):

Bharat Mirchandani ◽

Pascal Perrier ◽

Brigitte Grosgogeat ◽

Christophe Jeannin

Keyword(s):

Speech Production ◽

Temporal Variations ◽

Complete Denture ◽

Positive Pressure ◽

Single Subject ◽

Audio Signals ◽

Motor Tasks ◽

Pressure Sensing ◽

French Speaking ◽

Swallowing Pressure

Abstract Objectives The mechanical interactions between tongue and palate are crucial for speech production and swallowing. In this study, we present examples of pressure signals that can be recorded with our PRESLA system (PRESLA holds for the French expression “PRESsion de la LAngue” [Pressure from the tongue]) to assess these motor functions, and we illustrate which issues can be tackled with such a system. Materials and Methods A single French-speaking edentulous subject, old wearer of a complete denture, with no speech production and swallowing disorders, was recorded during the production of nonsense words including French alveolar fricatives, and during dry and water swallowing. The PRESLA system used strain-gauge transducers that were inserted into holes drilled in the palatal surface of a duplicate of the prosthesis at six locations that were relevant for speech production and swallowing. Pressure signals were postsynchronized with the motor tasks based on audio signals. Results Patterns of temporal variations of the pressure exerted by the tongue on the palate are shown for the two studied motor tasks. It is shown for our single subject that patterns for fricative /s/ are essentially bell shaped, whereas pressure signals observed for water swallow begin with a maximum followed by a slow decrease during the rest of the positive pressure phase. Pressure magnitude is almost 20 times larger for water swallow than for /s/ production. Conclusions This study illustrates the usefulness of our PRESLA system for studying speech production and swallowing motor control under normal and pathological conditions.

Download Full-text

A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽

10.3390/foods10061411 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1411

Author(s):

José Luis P. Calle ◽

Marta Ferreiro-González ◽

Ana Ruiz-Rodríguez ◽

Gerardo F. Barbero ◽

José Á. Álvarez ◽

...

Keyword(s):

Random Forest ◽

Raw Materials ◽

Principal Component ◽

Hierarchical Cluster ◽

Raw Material ◽

Support Vector ◽

Protected Designation Of Origin ◽

Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

Download Full-text

Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data

Landslides ◽

10.1007/s10346-021-01654-0 ◽

2021 ◽

Author(s):

Chiara Crippa ◽

Elena Valbuzzi ◽

Paolo Frattini ◽

Giovanni B. Crosta ◽

Margherita C. Spreafico ◽

...

Keyword(s):

Rock Slope ◽

Progressive Failure ◽

Principal Component ◽

Cost Effective ◽

Displacement Rate ◽

Machine Learning Classification ◽

Management Perspective ◽

Alpine Environments

AbstractLarge slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

Download Full-text

Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA

Molecules ◽

10.3390/molecules26133842 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3842

Author(s):

Alessandro D’Alessandro ◽

Daniele Ballestrieri ◽

Lorenzo Strani ◽

Marina Cocchi ◽

Caterina Durante

Keyword(s):

Food Industry ◽

Principal Component ◽

Component Analysis ◽

Gas Chromatography Mass Spectrometry ◽

Volatile Fraction ◽

Combined Effects ◽

A Posteriori ◽

Aroma Profile ◽

Simultaneous Component Analysis

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.

Download Full-text

Simultaneous determination of various platycosides in Four Platycodon grandiflorum cultivars by UPLC-QTOF/MS

Applied Biological Chemistry ◽

10.1186/s13765-019-0457-x ◽

2019 ◽

Vol 62 (1) ◽

Author(s):

Dae Young Lee ◽

Bo-Ram Choi ◽

Jae Won Lee ◽

Yurry Um ◽

Dahye Yoon ◽

...

Keyword(s):

Principal Component ◽

Ultra Performance Liquid Chromatography ◽

Platycodon Grandiflorum ◽

Pentacyclic Triterpenoid ◽

Flight Mass Spectrometry ◽

Platycodi Radix ◽

Isomeric Pairs ◽

Different Levels

Abstract In Platycodi Radix (root of Platycodon grandiflorum), there are a number of platycosides that consist of a pentacyclic triterpenoid aglycone and two sugar moieties. Due to the pharmacological activities of platycosides, it is critical to assess their contents in PR, and develop an effective method to profile various platycosides is required. In this study, an analytical method based on ultra performance liquid chromatography coupled with quadrupole time-of-flight/mass spectrometry (UPLC-QTOF/MS) with an in-house library was developed and applied to profile various platycosides from four different Platycodi Radix cultivars. As a result, platycosides, including six isomeric pairs, were successfully analyzed in the PRs. In the principal component analysis, several platycosides were represented as main variables to differentiate the four Platycodi Radix cultivars. Their different levels of platycosides were also represented by relative quantification. Finally, this study indicated the proposed method based on the UPLC-QTOF/MS can be an effective tool for identifying the detail characterization of various platycosides in the Platycodi Radix.

Download Full-text

STATISTICAL MULTIVARIATE ANALYSIS APPLIED TO ENVIRONMENTAL CHARACTERIZATION OF SOIL IN SEMIARID REGION1

Revista Caatinga ◽

10.1590/1983-21252019v32n120rc ◽

2019 ◽

Vol 32 (1) ◽

pp. 200-210

Author(s):

Antônio Italcy de Oliveira Júnior ◽

Luiz Alberto Ribeiro Mendonça ◽

Sávio de Brito Fontenele ◽

Adriana Oliveira Araújo ◽

Maria Gorethe de Sousa Lima Brito

Keyword(s):

Organic Matter Content ◽

Principal Component ◽

Matter Content ◽

Multivariate Statistical ◽

Statistical Tool ◽

Environmental Characterization ◽

Hierarchical Grouping ◽

Two Factors ◽

Total Data

ABSTRACT Soil is a dynamic and complex system that requires a considerable number of samples for analysis and research purposes. Using multivariate statistical methods, favorable conditions can be created by analyzing the samples, i.e., structural reduction and simplification of the data. The objective of this study was to use multivariate statistical analysis, including factorial analysis (FA) and hierarchical groupings, for the environmental characterization of soils in semiarid regions, considering anthropic (land use and occupation) and topographic aspects (altitude, moisture, granulometry, PR, and organic-matter content). As a case study, the São José Hydrographic Microbasin, which is located in the Cariri region of Ceará, was considered. An FA was performed using the principal component method, with normalized varimax rotation. In hierarchical grouping analysis, the “farthest neighbor” method was used as the hierarchical criterion for grouping, with the measure of dissimilarity given by the “square Euclidean distance.” The FA indicated that two factors explain 75.76% of the total data variance. In the analysis of hierarchical groupings, the samples were agglomerated in three groups with similar characteristics: one with samples collected in an area of the preserved forest and two with samples collected in areas with more anthropized soils. This indicates that the statistical tool used showed sensitivity to distinguish the most conserved soils and soils with different levels of anthropization.

Download Full-text