Multiway Analysis Research Articles

The 6 degrees-of-freedom range of motion of the L1-S1 vertebrae in young and middle-aged asymptomatic people.

Controlled laboratory study. To determine the 6 degrees of freedom of lumbar vertebra in vivo during different functional activities in young and middle-aged asymptomatic subjects. A total of 26 asymptomatic subjects (M/F, 15/11; age, 20-55 years) were recruited in this study. They were divided into two groups: young group (number: 14; age: 20-30 years old) and middle-aged group (number: 12; age: 45-55 years old). The lumbar segment of each subject was scanned by computed tomography for the construction of three-dimensional (3D) models of the vertebra from L1 to S1. The lumbar spine was imaged by using a dual fluoroscopic system when the subjects performed different trunk postures. The 3D models of vertebrae were matched to two fluoroscopic images simultaneously in software. The range of motion (ROM) of vertebrae in the young and middle-aged groups was compared by using multiway analysis of variance, respectively. During the supine to the upright posture, vertebral rotation of the L1-S1 occurred mainly around the mediolateral axis (mean: 3.9 ± 2.9°). Along the mediolateral axis, vertebral translation was significantly lower at L1-2 (7.7 ± 2.4 mm) and L2-3 (8.0 ± 3.5 mm) than at L3-4 (1.6 ± 1.2 mm), L4-5 (3.3 ± 2.6 mm), and L5-S1 (2.6 ± 1.9 mm). At the L4-5 level, the young group had a higher rotational ROM than the middle-aged group around all three axes during left-right bending. Along the anteroposterior axis, the young group had a lower translational ROM at L4-5 than the middle-aged group during left-right bending (4.6 ± 3.3 vs. 7.6 ± 4.8 mm; P < 0.05). At L5-S1, the young group had a lower translational ROM than the middle-aged group during flexion-extension, left-right bending, and left-right torsion. This study explored the lumbar vertebral ROM at L1-S1 during different functional postures in both young and middle-aged volunteers. There were higher coupled translations at L3-4 and L4-5 than at the upper lumbar segments during supine to upright. The vertebral rotation decreased with age. In addition, the older subjects had a higher anteroposterior translation at the L4-5 segment and higher mediolateral translation at the L5-S1 segment than the young group. These data might provide basic data to be compared with spinal pathology.

Simultaneous determination of five triarylmethane colorants in syrup by applying multivariate curve resolution to second-derivative visible absorption spectra.

A method for the simultaneous determination of five triarylmethane colorants (brilliant blue FCF, patent blue V, patent blue VF, fast green FCF, and green S) in syrup samples was proposed by applying multivariate curve resolution to second-derivative visible absorption spectra in citrate and carbonate buffer solutions. Even for heavily overlapping spectra, adopting second-derivative spectra and multiway analysis in citrate and carbonate buffer solutions enhanced specificity for the analytes and achieved prediction performance with a coefficient of determination (R2) > 0.90, root mean square error of prediction (RMSEP) < 0.20mg/L, and limit of detection (LOD) < 0.50mg/L for all five analytes. The basis set expansion method succeeded in a reasonable estimation of the second-derivative spectra with the interferant. For commercial syrup samples, the present method predicted results without significant differences compared to the results measured using liquid chromatography-mass spectrometry. The proposed method provides a rapid alternative to liquid chromatography techniques for the quantification of multiple colorants in food samples.

A Parsimonious Taxonomy of The Newly Retired: Spousal and Disability Combinations Shape Part or Complete Retirement.

The inadequate classification of retiree sub-groups ultimately results in misaligned policy. To generate sets of sub-groups that may be appropriately targeted for policy and interventions, variables are used that reflect the social structure of retirees, particularly the options of partial and complete retirement, marital status, gender, as well as the retirement status of the spouse, where relevant, and disability. Three sets of longitudinal Australian data were combined, each reflecting a four-year period (2003–2007, 2007–2011, 2011–2015) during which the individuals aged 45 to 69 retired (n = 1179). A multiway frequency analysis was performed to develop an inductive, combinatorial model of retirement from work. The resulting parsimonious taxonomy of sub-groups of the newly retired reflected main effects and interactions of key social-structural variables. Notably, a key driver of the pattern of results was that couples tend to coordinate their retirement behavior in both the decision to retire and form of retirement. Non-partnered retirees were more likely to be women. Disability was also a driver of retirement for non-partnered retirees, regardless of gender. Identifying sub-groups based on combinations of retiree characteristics can better inform policy design, appropriate health promotion interventions and potential specific triggers for enacting those policies. Overall, marital status, spousal retirement behavior and disability may each present a more useful basis for a taxonomy of retirement than more individually oriented age- and wealth-based systems.

Application of Parallel Factor Analysis (PARAFAC) to the Regional Characterisation of Vineyard Blocks Using Remote Sensing Time Series

Monitoring wine-growing regions and maximising the value of production based on their region/local specificities requires accurate spatial and temporal monitoring. The increasing amount and variability of information from remote sensing data is a potential tool to assess this challenge for the grape and wine industry. This article provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess the value of simultaneously considering spectral and temporal information to highlight site-specific canopy evolution in relation to environmental factors and management practices, which present a large diversity at this regional scale. Parallel Factor Analysis (PARAFAC) was used as an unsupervised technique to recover pure spectra and temporal signatures from multi-way spectral imagery of vineyards in the Languedoc-Roussillon region in the south of France. The model was developed using a time series of Sentinel-2 satellite imagery collected over 4978 vineyard blocks between May 2019 and August 2020. From the Sentinel-2 (spectral and temporal) signal, the PARAFAC analysis allowed the identification of spectral and temporal profiles in the form of pure components, which corresponded to vegetation and soil. The PARAFAC analysis also identified that two of the pure spectra were strongly related to characteristics and dynamics of vineyard cultivation at a regional scale. A conceptual framework was proposed in order to simultaneously consider both vegetation and soil profiles and to summarise the mass of data accordingly. This methodology allowed the computation of a concentration index that characterised how close a field was to a vegetation or a soil profile over the season. The concentration indices were validated for the vegetation and the soil over two growing seasons (2019 and 2020) with geostatistical analysis. A non-random distribution of the concentration index at the regional scale was assumed to highlight a strongly spatially organised phenomenon related to spatially organised environmental factors (soil, climate, training system, etc.). In a second step, spatial patterns of indices were subjected to the expertise of a panel of advisors of the wine industry in order to validate them in relation to vine-growing conditions. Results showed that the introduction of the PARAFAC method opened up the possibility to identify relevant spectro-temporal profiles for vine monitoring purposes.

Motion of Lumbar Endplate in Degenerative Lumbar Scoliosis Patients with Different Cobb Angle In Vivo: Reflecting the Biomechanics of the Lumbar Disc.

Objective To evaluate the influence of degenerative lumbar scoliosis (DLS) with different Cobb angles and degenerative discs on the range of motion (ROM) of the lumbar endplates during functional weight-bearing activities in vivo. Summary of Background. DLS data might influence spinal stability and range of motion of the spine. Altered lumbar segment motion is thought to be related to disc degeneration. However, to date, no data have been reported on the motion patterns of the lumbar endplates in patients with DLS in vivo. Methods We recorded 42 DLS patients with the apical disc at L2-L3 and L3-L4. Patients were divided into A group with a coronal Cobb angle >20° (number: 13; 62.00 ± 8.57 years old) and group B with a coronal Cobb angle <20° (number: 28; 65.79 ± 6.66 years old). Patients' discs were divided into a degenerated disc group (III-V) and a nondegenerated disc group (I-II) according to the Pfirrmann classification. Computed tomography (CT) was performed on every subject to build 3-dimensional (3D) models of the lumbar vertebrae (L1–S1), and then the vertebras were matched according to the dual fluoroscopic imaging system. The kinematics of the endplate was compared between the different Cobb angle groups and the healthy group reported in a previous study and between the degenerative disc group and nondegenerative disc group by multiway analysis of variance. Results Coupled translation at L5-S1 was higher than other levels during the three movements. During the flexion-extension of the trunk, around the anteroposterior axis, rotation in group A was higher than that in the control group at L2-L3 and L3-L4 (6.62 ± 3.61 mm vs 4.36 ± 2.55 mm, 5.01 ± 3.19 mm; P < 0.05, P < 0.05). During the left-right bending of the trunk, around the mediolateral axis, rotations in groups A and B were higher than those in the control group at L5-S1 (17.52 ± 11.43°, 17.25 ± 9.22° vs 10.08 ± 5.42°; P < 0.05, P < 0.05). During the left-right torsion, around the anteroposterior axis, rotation in group A was higher than that in group B and the control group at L2-3 (9.69 ± 5.94° vs 5.77 ± 4.02°, 4.47 ± 2.00°; P < 0.05, P < 0.05). In patients with Cobb angle <20°, coupled translation was higher in the degenerated disc group than in the nondegenerated disc group, especially along the anteroposterior axis. Conclusion An increase in the coupled rotation of the endplate at the scoliotic apical level in patients with DLS was related to a larger Cobb angle. Moreover, segments with degenerative discs had higher coupled translations in the anteroposterior direction than segments with nondegenerative discs in DLS patients with Cobb angle <20°. These data might provide clues regarding the etiology of DLS and the basis for operative planning.

EA-ADMM: noisy tensor PARAFAC decomposition based on element-wise average ADMM

Tensor decomposition is widely used to exploit the internal correlation in multi-way data analysis and process for communications and radar systems. As one of the main tensor decomposition methods, CANDECOMP/PARAFAC decomposition has advantages of uniqueness and interpretation properties which are significant in practical applications. However, traditional decomposition method is sensitive to both predefined rank and noise that results in inaccurate tensor decomposition. In this paper, we propose a improved algorithm called the Element-wise Average Alternating Direction Method of Multipliers by minimizing the sum of all factors’ trace norm and the noise variance. Our algorithm could overcome the dependence on predefined rank in traditional decomposition algorithms and alleviate the impact of noise. Moreover, this algorithm can be transferred to solve the problem of tensor completion conveniently. The simulation results show that our proposed algorithm could decompose the noisy tensor to the factors with above 90% similarity in various SNR and also interpolate the incomplete tensor with higher similar coefficient and lower relative reconstruction error when the missing rate is less than 0.5.

Active Learning for Multi-way Sensitivity Analysis with Application to Disease Screening Modeling.

Sensitivity analysis is an important aspect of model development as it can be used to assess the level of confidence that is associated with the outcomes of a study. In many practical problems, sensitivity analysis involves evaluating a large number of parameter combinations which may require an extensive amount of time and resources. However, such a computational burden can be avoided by identifying smaller subsets of parameter combinations that can be later used to generate the desired outcomes for other parameter combinations. In this study, we investigate machine learning-based approaches for speeding up the sensitivity analysis. Furthermore, we apply feature selection methods to identify the relative importance of quantitative model parameters in terms of their predictive ability on the outcomes. Finally, we highlight the effectiveness of active learning strategies in improving the sensitivity analysis processes by reducing the total number of quantitative model runs required to construct a high-performance prediction model. Our experiments on two datasets obtained from the sensitivity analysis performed for two disease screening modeling studies indicate that ensemble methods such as Random Forests and XGBoost consistently outperform other machine learning algorithms in the prediction task of the associated sensitivity analysis. In addition, we note that active learning can lead to significant speed-ups in sensitivity analysis by enabling the selection of more useful parameter combinations (i.e., instances) to be used for prediction models.

Tracing sources of oilfield wastewater based on excitation-emission matrix fluorescence spectroscopy coupled with chemical pattern recognition techniques

In order to prevent the illegal discharge of oilfield wastewater, this work proposed excitation-emission matrix fluorescence (EEMF) spectroscopy coupled with two kinds of chemical pattern recognition methods for tracing the sources of oilfield wastewater. The first pattern recognition method was built from the relative concentrations extracted by alternating trilinear decomposition (ATLD) based on partial least squares-discriminant analysis (PLS-DA) algorithm, and the other one was modeled based on strictly multi-way partial least squares-discriminant analysis (N-PLS-DA). Both methods showed good discrimination abilities for oilfield wastewater samples from three different sources. The total recognition rates of the training and prediction sets are 100%, the values of sensitivity and selectivity are 1. This study showed that EEMF spectroscopy combined with chemical pattern recognition techniques could be used as a potential tool for tracing the sources of oilfield wastewater.

Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes

Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis. In this article, (I) the cycle temporal algorithm (CTA) combined with the dynamic kernel principal component analysis (DKPCA) and the multiway dynamic kernel principal component analysis (MDKPCA) fault detection algorithms are proposed, which are used for continuous and batch process fault detections, respectively. In addition, (II) a fault variable identification model based on reconstructed-based contribution (RBC) model that paves the way for determining the cause of the fault are proposed. The proposed fault diagnosis model was applied to Tennessee Eastman (TE) process and penicillin fermentation process for fault diagnosis. And compare with other fault diagnosis methods. The results show that the proposed method has better detection effects than other methods. Finally, the reconstruction-based contribution (RBC) model method is used to accurately locate the root cause of the fault and determine the fault path.

The canonical partial least squares approach to analysing multiway datasets—N‐CPLS

AbstractMultiway datasets arise in various situations, typically from specialised measurement technologies, as a result of measuring data over varying conditions in multiple dimensions or simply as sets of possibly multichannel images. When such measurements are intended for predicting some external properties, the amount of methods available is limited. The multilinear partial least squares (PLS) is among the few available options. In the present work, we generalise the canonical partial least squares framework to handle multiway data. We demonstrate the resulting multiway data analysis method to be capable of building parsimonious models, encompassing continuous and categorical responses—both single and multiple—in a unifying framework. This also enables inclusion of additional responses/information that can contribute to more parsimonious models. Finally, we achieve a considerable advantage in computational speed without sacrificing numerical precision by deflating the responses and orthogonalising scores rather than the more costly deflations of the predictor data.

Self-stigma among clients of outpatient psychiatric clinics: A cross-sectional survey.

Self-stigma is common among people with mental disorders. A large body of research has examined associations between self-stigma and sociodemographic, clinical and psychosocial factors but the results are still conflicting. The aim of this study was to describe self-stigma among persons with affective and psychotic disorders and identify sociodemographic and clinical factors associated with experiences of self-stigma. A cross-sectional survey was performed with Finnish clients (N = 898) at 16 psychiatric clinics using self-reported questionnaires. The data were analyzed using descriptive statistics and with one-way and multi-way analysis of variance (ANOVA). The results showed that clients in community settings experience self-stigma (a total mean SSMIS-SF score of 74.8 [SD 22.3]). Having a diagnosis of an affective disorder, having a long history of mental disorder (>16 years) and the severity of depressive symptoms were the key factors associated with experiences of self-stigma. Clients living in community settings should be assessed regularly for depressive symptoms of mental disorders, and interventions should be conducted, especially at an early stage of the illness, to reduce self-stigma. Factors associated with self-stigma should be taken into account in the future development of interventions to reduce stigma.

Fault detection and diagnosis of the air handling unit via combining the feature sparse representation based dynamic SFA and the LSTM network

In recent years, slow feature analysis (SFA) has been successfully employed to deal with the air handling unit (AHU) system’s time-varying dynamic properties. However, since the derived slow features are the linear combinations of all original variables, the conventional SFA based methods may suffer from poor model interpretability and result in inaccurate fault detection and diagnosis (FDD) performance. To enhance the dynamic AHU system FDD effectiveness, this paper presents a novel feature sparse representation based SFA algorithm through imposing the sparsity on the slow features, which is called the sparse three-way data based dynamic SFA (STBDSFA). We make two contributions to propose the STBDSFA approach. One contribution is to infuse the feature sparse representation technique into the constructed SFA based monitoring model by performing the sparse restriction on the loading vector, which can effectively eliminate the meaningless variables’ coupling and select the key variable responsible for the fault detection. Before building the STBDSFA algorithm, the other contribution is to construct a new three-way data based dynamic SFA (TBDSFA) monitoring model to handle the AHU system’s two-directional dynamic properties. In the established TBDSFA model, the auto-regressive moving average exogenous (ARMAE) model is first adopted to reveal the variables’ auto-correlation relationships. Then, the multiway data analysis is applied to figure out the batch-wise dynamic property in multiple batch runs. After that, the dynamic SFA model is further set up to sufficiently tackle the time-wise dynamic nature within a batch run. To effectively diagnose the pattern of detected fault, another innovative work is to develop a long short-term memory (LSTM) based fault identification approach to classify the sparse slow features of fault snapshot dataset, owning to the LSTM’s superiority of coping with the dynamic time-varying nature of the sparse slow features. By comparing with some traditional and closely related FDD methods, the case study on the ASHRAE Research Project RP-1312 experimental datasets are performed to verify the performance and effectiveness of the proposed FDD scheme for the AHU system. To be specific, in comparison with the sparse principal component analysis, sparse dynamic SFA, kernel locality preserving projection and sparse three-way data based SFA approaches, the developed STBDSFA based monitoring method not only reveals no fault detection delays to alert the eleven test faults, but also achieves the highest average fault detection rates, i.e., 99.31% for the T2 statistic and 99.77% for the SPE statistic. Contrasted with the support vector machine, temporal convolutional network, convolutional neural network and deep belief network based classification algorithms, the suggested STLSTM based fault identification approach obtains the highest average fault diagnosis rate, i.e., 95.51% for the eleven fault snapshot datasets.

Effect of medically assisted reproduction (MAR) and pregnancy planning on Bayley-III Screening Test subscales in preterm infants at 12 months of corrected age: a cross-sectional study

BackgroundThe association between medically assisted reproduction and pregnancy planning on overall developmental outcomes of infants has been studied in relatively few studies. The availability of accurate tools for the early detection of developmental delays is a major issue. The purpose of this study is describing the average neurodevelopment of preterm infants and assessing the association between medically assisted reproduction, pregnancy planning and neurodevelopmental outcomes among Hungarian preterm infants.MethodsCross-sectional study of preterm infants with gestational age < 37 weeks (N = 171) who underwent developmental assessment using Bayley-III Screening Test (Bayley Scales of Infant and Toddler Development Screening Test – Third Edition) in five developmental domains (cognitive, receptive and expressive language, fine and gross motor) at 12 months of corrected age. We examined the developmental pattern of infants and the potential associations between medically assisted reproduction, pregnancy planning and Bayley-III Screening Test subscales. Information on the mode of conception and pregnancy planning was obtained through parental anamnesis.ResultsConcerning the risk for developmental delay, the language and motor subscales were the most affected. Examination of the mode of conception and developmental outcomes revealed higher neurodevelopmental skills in infants who were conceived through medically assisted reproduction. Significantly lower cognitive, receptive and expressive language skills were found to be associated with unplanned pregnancies. Multi-way analysis of variance was conducted in order to assess the impact of the mode of conception and pregnancy planning on subscales.ConclusionsWe cannot unambiguously conclude that MAR and pregnancy planning had a solely positive effect on the development of preterm infants at 12 months of corrected age, but our results are vital for the design and implementation of further research.

The Influence of Regulatory Oversight on Environmental, Social, and Governance Ratings

Firm performance in the specific areas of environmental stewardship, social responsibility, and corporate governance (ESG) has become an important criterion that investors use in determining a firm’s value. This empirical investigation, based on stakeholder theory examines the relationship between regulatory oversight and third-party ESG ratings. Our research methodology involved quantitative, observational, and retrospective analyses. The study population consisted of 471 firms from two heavily regulated industry sectors—the utility and financial sectors—and from two less regulated sectors—the information technology and consumer discretionary sectors. We compiled the ESG ratings for the firms from two independent rating services. The quantitative evaluation included multiple regression analysis and multiway frequency analysis. The findings show a statistically significant difference for firms in heavily regulated sectors compared to the ratings for firms in less regulated sectors for the environmental and governance component ratings. This study provides information to help stakeholders recognize the influence of regulation on ESG ratings and explains to investors and company leaders why ESG ratings vary among different industry sectors. This study was limited to four specific sectors but may provide insights applicable to other sectors based on regulatory intensity.

Effects of extremity trauma on physiological responses and survival to hemorrhage in conscious rats

BackgroundUnder most circumstances of battlefield injuries, trauma accompanies hemorrhage. Interestingly, there are few experimental animal models of trauma and hemorrhage. The compensatory cardiovascular responses during hemorrhage are well documented, and serve to maintain adequate perfusion of vital organs during conditions of loss of blood volume. While trauma alone has been shown to increase heart rate (HR) and vascular resistance, the effects of trauma on compensatory cardiovascular responses and the ability to survive hemorrhage have not been clearly defined. The purpose of this study is to determine the effects of an extremity injury that combines hard (bone) and soft (skeletal muscle) tissue trauma, on blood pressure (BP) and HR responses, and survival to hemorrhage in conscious rats. We hypothesized that trauma would impair compensatory cardiovascular responses, and decrease survival after hemorrhage.MethodsAll rats (male; ~ 350grams) were surgically implanted with a telemetry transmitter for measurement of BP and HR, and allowed to recover for 2 weeks prior to the experiment. A carotid catheter was inserted one day before the experiment. There were two experimental groups: hemorrhage with no trauma (n=20); and hemorrhage with trauma (n=20). On the day of experiment, rats were anesthetized briefly (10 min) to undergo trauma (crushing of the right gastrocnemius and semimembranosus muscles for 30 sec with forceps) and fibula fracture. No trauma groups were only anesthetized for 10 min without manipulation of the extremity. Rats were allowed to awaken, and 90 min later the rats underwent a conscious hemorrhage (~50% of blood volume during 25 min) via the indwelling carotid catheter. BP and HR measures were collected over 1 min intervals for 240 minutes after hemorrhage and analyzed via Data Sciences International software. Survival time and survival ratio to 240 minutes were determined. Data were analyzed via multi‐way analysis of variance for repeated measures and Kaplan‐Meier procedure using the Statistical Analysis System. Data are shown as mean ± standard deviation.ResultsCompared with no trauma, trauma increased HR during hemorrhage (P = 0.03), but had no effect on BP (P = 0.88). After hemorrhage completion, trauma decreased BP for approximately one hour (P = 0.032), but only transiently decreased HR. After hemorrhage, survival time (Trauma: 156.8 ± 80.7 and No Trauma: 210.6 ± 61.8 min, p = 0.022), and survival ratio (Trauma: 9/20 and No Trauma: 16/20, p=0.048), were significantly lower in animals with trauma compared to no trauma.ConclusionsOur results show that the presence of trauma affects compensatory mechanisms to maintain BP, and negatively impacts the ability to survive hemorrhage. These findings indicate the importance of including both trauma and hemorrhage in the development of animal models to study treatments for hemorrhage in wounded Soldiers on the battlefield or civilian trauma victims.

Determining the Geographical Origin of Fuji Apple from China by Multivariate Analysis Based on Soluble Sugars, Organic Acids, and Stable Isotopes

The aim of this study was to explore the regional characteristics of soluble sugars, organic acids, and stable isotopes (δ2H, δ18O, and δ13C) in Fuji apple and the viability of tracing the geographical origin. Totally, 181 Fuji apple samples from 2017 and 2018 from three main apple production regions in China, Bohai Bay (BHB), Loess Plateau (LP), and Northwest region (NW) were collected. The parameters of soluble sugars, organic acids, and stable isotopes in samples were analyzed with HPLC, IC, and IRMS, respectively. The results of regional difference analysis, multiway variance analysis, and correlation analysis indicated that sorbitol (Sor), glucose (Glu), fructose (Fru), sucrose (Sucr), δ2H, and δ13C can be used to distinguish the samples from the three regions. Stepwise linear discriminant analysis (SLDA) showed that the correct discriminant rate of samples from the advantageous production areas of apples in China (BHB and LP) was 82.2%, and the most effective indexes were Glu, Fru, Sucr, and δ2H. Moreover, satisfactory classification can be achieved in samples from BHB and NW, with a correct classification rate of 90.0%, and Sor, Glu, and Fru were included in the discrimination model. Furthermore, the validity of the discriminant model was verified by the prediction set. The study also found that organic acids were not suitable to distinguish the apple samples from the three regions. In addition, soluble sugars and stable isotopes could not effectively distinguish LP and NW samples, which was also the reason that the samples from the three main apple production regions could not be distinguished well.

Is It Possible to Assess Heatwave Impact on Grapevines at the Regional Level with Time Series of Satellite Images?

Unexpected climatic conditions or extreme climatic events in vineyards are a worldwide problem that requires accurate spatial and temporal monitoring. Satellite-based remote sensing is an important source of data to assess this challenge in a climate-change context. This paper provides a first insight into the capacity of a multiway analysis method applied to Sentinel-2 time series to assess heatwave impacts in vineyards at a regional scale. Multi-way partial least squares (N-PLS) regression was used as a supervised technique to predict the intensity of damage caused to vineyards by the heatwave phenomenon that impacted the vineyards in the south of France in 2019. The model was developed based on available ground truth data of yield losses for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The model showed a performance accuracy (R2) of 0.56 in the calibration set and of 0.66 in the validation set, with a standard error of cross-validation in the calibration set of 12.4% and a standard error of the prediction of yield losses in the validation set of 10.7. The model was applied at a regional scale on 4978 vineyard blocks to predict yield losses using spectral and temporal attributes. The prediction of the yield loss due to heat stress at a regional scale was related to the spatial pattern of maximum temperatures recorded during the extreme weather event. This relation was confirmed by a chi-square test (p &lt; 5%). The introduction of N-PLS insights into the analysis enables the characterisation of heat stress responses in vineyards and the identification of spectro-temporal profiles relevant for understanding the effects of heatwaves on vine blocks at a regional scale.

Multiway resolution of spectrochromatographic measurements for the quantification of echinuline in marine-derived fungi Aspergillus chevalieri using parallel factor analysis

A multiway resolution of incomplete chromatographic separation was presented for spectrochromatographic quantification of echinuline in marine-derived fungi Aspergillus chevalieri. Two-dimensional spectrochromatographic maps of calibration, validation and real samples were recorded as a function of time and wavelength using UPLC-PDA instrument under non-optimized chromatographic conditions, which gave rise to co-elution of echinuline and the constituents of sample matrix. A three-way array was obtained by concatenating the data matrices of the spectrochromatographic maps. Then, parallel factor analysis was applied to the multiway array to extract the individual contribution of echinuline in three modes (time, wavelength and sample). While time and wavelength profiles were used for the characterization of echinuline, the sample profile was used for its quantitative determination of the analyte in validation set and in real samples. Validity of the analytical method was evaluated by analyzing the validation set, which consist of test samples, standard addition samples, intra-day and inter-day samples. The proposed multiway analysis method was then applied to marine-derived fungi extracts and echinuline content was found to be 31.9 µg/g based on the average of ten assay results. The assay results provided by PARAFAC model were statistically compared with those obtained by a newly developed classical UPLC method, which ensured the complete separation of echinuline in a run time of nine minutes. The assay results were found to be comparable due to the fact that there was no significant difference between the analysis results (F = 1.63, Fcrit = 3.17; t = 0.69, tcrit = 2.11) at the significance level of 95%). Consequently, the PARAFAC method permitted the accurate determination of echinuline in fungal extracts despite the partial chromatographic separation with a run time of only three minutes.

Exploring dynamic metabolomics data with multiway data analysis: a simulation study

BackgroundAnalysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied.ResultsWe focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth.ConclusionOur numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

FB Dictionary Based SSBL-EM and Its Application for Multi-Class SSVEP Classification Using Eight-Channel EEG Signals

Many applications of signal processing require efficient reconstruction and economical processing of a signal. A classical approach for efficient reconstruction and economical processing of signal is compression. In recent studies, compressed sensing (CS) is an emerging field for compression and reconstruction. Moreover, CS is well-known for energy-efficient signal compression. However, most CS algorithms do not provide efficient reconstruction of physiological signals, such as electroencephalogram (EEG) and electrocardiogram (ECG). Efficient reconstruction is not possible due to non-stationary and non-sparsity characteristics of these signals. On the other hand, processing of these multichannel physiological signals is also time-consuming since the processing time is directly proportional to the number of channels. Hence, we have proposed a Fourier–Bessel (FB) dictionary-based spatiotemporal sparse Bayesian learning (SSBL) algorithm with expectation–maximization (EM) method for efficient reconstruction of multichannel physiological signals. This proposed method is based on the existing SSBL-EM method in which the authors used discrete cosine transform (DCT) dictionary for efficient reconstruction. In our work, we have used FB dictionary instead of DCT dictionary as the Bessel functions have non-stationary characteristic and provide good compression for signals such as EEG. The proposed method has been tested on two different databases for steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) classification using multichannel EEG signals and provides 100% classification accuracy with 50% compression and 2-s time duration using the first database. Classification has been performed using L1-regularized multiway canonical correlation analysis (L1-RMCCA) method. The performance of the proposed method has been compared with the DCT-dictionary-based SSBL-EM method. The classification accuracy result makes this proposed method more beneficial for uninterrupted wireless telemonitoring of multichannel EEG signals.