Multivariate Principal Component Analysis Research Articles

The effect of packaging material and adjunct culture on the biogenic amine content, microbiological and textural properties of Dutch-type cheese

The aim of the study was to assess the effects of the packaging materials applied (shrink foil, copolymer coating and food wax) in combination with Lacticaseibacillus casei CCDM 198 microbial strain on biochemical, microbiological and textural properties of Dutch-type cheese during ripening (at 12 ± 1 °C for 168 days). A multivariate principal component analysis method based on a correlation matrix was employed to evaluate the relationships between basic chemical properties, textural and microbiological parameters, biogenic amine and free amino acid contents in relation to the packaging material used and the length of the ripening time. The use of different packaging materials did not have a significant effect on the salt content, fat content, and pH values, whereas significant differences were observed for dry matter content and textural properties development. The presence of Lacticaseibacillus casei CCDM 198 strain accelerated proteolysis in all samples. Shrink foil and food wax had similar effects on the development of biogenic amine content during ripening, while considerable differences were observed for the samples wrapped in copolymer coating. Copolymer coating in combination with Lacticaseibacillus casei CCDM 198 strain was able to reduce biogenic amine content by up to 28% after 168 days.

Pasture Recovery Period Affects Humic Substances and Oxidations of Organic Matter in Eastern Amazon

Land management practices that overlook soil limitations and potential have led to varying degrees of degradation. This study evaluates the carbon content in chemical and oxidisable soil fractions across different pasture recovery periods, comparing them to secondary forests. The management practices assessed include the following: secondary forest (SF), perennial pasture (PP), perennial pasture recovered five years ago (P5), and perennial pasture recovered eight years ago (P8), all on Plinthosols. We analysed carbon levels in oxidisable fractions and humic substances at depths of 0–0.10 m, 0.10–0.20 m, 0.20–0.30 m, and 0.30–0.40 m. The SF and P8 areas showed the highest organic matter content within the humic fractions, compared to the PP and P5 areas. Additionally, the P8 area demonstrated an increase in the labile and moderately recalcitrant fractions of organic matter, standing out among the different fractions evaluated. The multivariate principal component analysis indicated that P8 has the greatest impact on soil quality, followed by FS, P5, and PP. The pasture recovery over the past eight years has significantly improved soil carbon accumulation, highlighting the benefits of land restoration.

Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.

Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in SouthCarolina.

Spatial Functional Outlier Detection in Multivariate Spatial Functional Data

Multivariate spatial functional data consists of multiple functions of time-dependent attributes observed at each spatial point. This study focuses on detecting spatial outliers in spatial functional data. Firstly, we develop a new method called Mahalanobis Distance Spatial Outlier (MDSO) to detect functional outliers in the data. The method introduces the multivariate functional Mahalanobis semi-distance and multivariate pairwise functional Mahalanobis semi-distance metrics based on the multivariate functional principal components analysis to calculate the dissimilarity between functions at each spatial point. Via simulation, we show that MDSO performs better than the other competing methods. Secondly, MDSO has been extended to detect spatial functional outliers as well. The functional outliers can now be categorized as global or/and local functional outliers. The appropriate number of neighbors and the cut-off point for the degree of isolation are determined via simulation. Finally, we demonstrate the application of the MDSO on a water quality data set obtained from Sungai Klang basin in Malaysia. The results can be used to support the authority in making better decisions on the management of the river basin or other spatial data with time-independent attributes.

A federated data fusion-based prognostic model for applications with multi-stream incomplete signals

Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not large enough to train a reliable prognostic model. To address this challenge, this article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model using their multi-stream, high-dimensional, and incomplete data while keeping each user’s data local and confidential. The prognostic model first employs multivariate functional principal component analysis to fuse the multi-stream degradation signals. Then, the fused features coupled with the times-to-failure are utilized to build a (log)-location-scale regression model for failure prediction. To estimate parameters using distributed datasets and keep the data privacy of all participants, we propose a new federated algorithm for feature extraction. Numerical studies indicate that the performance of the proposed model is the same as that of classic non-federated prognostic models and is better than that of the models constructed by each user itself.

Multivariate functional principal component analysis and k-means clustering to identify kinematic foot types during gait in children with cerebral palsy

BackgroundChildren with neuromuscular disorders, such as cerebral palsy, frequently develop foot deformities, such as equinopronovalgus and equinosupovarus, leading to walking difficulties and discomfort. Traditional assessment methods, including clinical measures and radiographs, often fail to capture the dynamic nature of these deformities, resulting in suboptimal treatment. 3D gait analysis using multisegment foot models offers a more detailed understanding of these deformities. Research questionTo determine whether the combination of multisegment foot models, multivariate functional principal component analysis, and k-means cluster analyses could identify distinct, clinically relevant foot types in a large pediatric cohort with cerebral palsy. MethodsThis was a retrospective analysis of 3D gait data from 197 patients with cerebral palsy collected using a multisegment foot model. Multivariate functional principal component analysis was used to reduce these data prior to using k-means clustering to identify foot posture clusters. Further analyses, including ANOVA and Fisher's Exact tests, were used to evaluate demographic, radiographic, and gait characteristics to explain the clinical relevance of each cluster. ResultsAnalysis of kinematic data from 371 feet revealed six clinically significant clusters, with a low misclassification rate of 2 %. One-factor ANOVAs demonstrated significant differences across clusters for all MPCs, whereas no significant differences were noted in basic anthropometric variables. Significant variations were observed in radiographic and gait function variables, and a strong association between GMFCS levels and cluster categorization was identified. SignificanceThe novel approach of integrating multivariate functional principal component analysis and k-means clustering identified a spectrum of foot deformities in children with CP, ranging from equinosupovarus to marked equinopronovalgus. This methodology provides an objective classification based on kinematic data and can facilitate improved diagnosis and treatment of cerebral palsy-related foot deformities.

Gaining insights into the responses of individual yeast cells to ethanol fermentation using Raman tweezers and chemometrics

Saccharomyces cerevisiae is the most common microbe used for the industrial production of bioethanol, and it encounters various stresses that inhibit cell growth and metabolism during fermentation. However, little is currently known about the physiological changes that occur in individual yeast cells during ethanol fermentation. Therefore, in this work, Raman spectroscopy and chemometric techniques were employed to monitor the metabolic changes of individual yeast cells at distinct stages during high gravity ethanol fermentation. Raman tweezers was used to acquire the Raman spectra of individual yeast cells. Multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis were employed to analyze the Raman spectra dataset. MCR-ALS extracted the spectra of proteins, phospholipids, and triacylglycerols and their relative contents in individual cells. Changes in intracellular biomolecules showed that yeast cells undergo three distinct physiological stages during fermentation. In addition, heterogeneity among yeast cells significantly increased in the late fermentation period, and different yeast cells may respond to ethanol stress via different mechanisms. Our findings suggest that the combination of Raman tweezers and chemometrics approaches allows for characterizing the dynamics of molecular components within individual cells. This approach can serve as a valuable tool in investigating the resistance mechanism and metabolic heterogeneity of yeast cells during ethanol fermentation.

Assessment of trace elements in Iranian kefir samples by using ICP-OES technique

ABSTRACT In this research, levels of 12 trace elements (Al, Zn, Cd, Ni, Hg, Pb, Co, Cr, Se, Fe, As and Mn) were measured in Iranian kefir samples by using ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) method. Based on our findings, the maximum and minimum averages of essential elements were related to Fe (423 ± 146 µg/kg) and Se (not detected or ND), respectively, and the maximum and minimum averages of toxic elements were related to As (13.3 ± 4.68 µg/kg) and Hg (ND), respectively. Based on the amount of fat in the kefir samples, the highest mean values of toxic and essential elements was related to As (15.9 ± 5.25 µg/kg) and Fe (466 ± 177 µg/kg) in high-fat kefir samples, respectively. In addition, the correlation between the amount of fat and elements in the samples was shown with a multivariate principal component analysis (PCA). Based on the obtained results, the amount of trace elements in kefir samples was lower than the existing standards (Cd = 10, Pb = 20, Al = 500, Hg = 20, As = 140, Mn = 100 and Fe = 2700 µg/kg). Also, in reviewing the results by PCA analysis, the differences and similarities between the type and amount of toxic elements and essential elements in kefir samples have been shown. Finally, based on the obtained results, it can be concluded that the consumption of this food item can be safe due to the low level of elements below the acceptable limit.

Variability of Silver Fir Needle (Abies alba Mill.) Anatomical Features in the Southeast Europe Natural Populations.

The survival of marginal/peripheral silver fir (Abies alba Mill.) populations in the broader region of Southeast Europe is endangered due to climate change and population decline. This study aimed to determine the level and pattern of variability for the anatomical traits of needles and the possibility of linking the pattern of phenotypic variability with environmental factors. In most of the analyzed needle traits, the statistically significant variability between populations was determined. According to the results of the multivariate principal component analysis, it is evident that the populations are distinct from each other, in three groups. The climatic factors Hargreaves reference evaporation, mean annual temperature, and growing degree-days, were statistically significantly correlated. The altitude and heating degree-days are statistically significantly correlated with the following three environmental factors: Hargreaves reference evaporation, mean annual temperature and growing degree-days, but negatively with others. The paper's findings indicate significant moderate and high correlations between the anatomical traits of the needles' central bundle diameter with the resin duct diameter, the distance between the vascular bundle and the resin duct and the epidermis thickness with cuticle, the resin duct diameter with the distance between the vascular bundle and the resin duct and the epidermis thickness with cuticle, as well as the distance between the vascular bundle and the resin duct with the hypodermis height and the epidermis thickness with cuticle. The results of agglomerative hierarchical clustering analysis, performed for anatomical and climatic traits, confirmed the existence of three groups of tested populations according to the altitude gradient. Research results provide knowledge on the diversity and structure of Abies alba populations of Southeast Europe, important for further research and guidelines for the species' conservation and genetic variability preservation in the southern marginal distribution area and keeping in line with climate change projections.

A multisensor approach coupled with multivariate statistics and geostatistics for assessing the status of land degradation: The case of soils contaminated in a former outdoor shooting range

Soil contamination in outdoor shooting ranges (OSRs) is a major threat for human health, particularly when, after the end of activities, the land is used for recreational areas or agricultural production. The status of land degradation of an OSR in southern Italy was assessed using a multisensor approach. It was based on: i) proximal sensors, including electromagnetic induction (EMI) for measuring soil electrical conductivity (ECa) and magnetic susceptibility (MSa), γ-ray spectrometry for K, eU and eTh analyses and ultrasonic penetrometry detecting cone index (CI) data representative of soil's strength, ii) field surveys on soil thickness (ST), and iii) laboratory analyses of potentially-toxic-elements (PTEs) by portable X-ray fluorescence spectrometry and polycyclic aromatic hydrocarbons (PAHs) by gas-chromatography. Spatial variability of measurements was modelled and mapped using geostatistical methods. The most densely measured covariate (i.e., the ECa of the topsoil) was used within kriging with external drift to improve the PTEs predictions. The PTEs maps were complemented by maps of spatial uncertainty. A robust multivariate principal component analysis (rPCA) was applied to proximal sensor and laboratory data and allowed to identify associations of PAHs, lead, CI with the topsoil ECa along the first component (PC1), highlighting the correlation between land anthropogenic effects and EMI measures; while the association between the ST (estimating the depth of underground travertine hard-layers) and the bottom soil ECa and MSa along the second component (PC2) evidenced the influence of soil stratigraphy on the EMI measures. This study demonstrates that the simultaneous use of different proximal sensors associated with laboratory analysis can allow to assess and model the spatial variability of the land degradation status of an OSR, including soil compaction, organic and inorganic contamination. The correlation between EMI data with the PTEs content highlights the potential of this technique in the field of soil contamination.

Hydrogeological trends in an alluvial valley in the Brazilian semiarid: Impacts of observed climate variables change and exploitation on groundwater availability and salinity

Study regionMimoso Alluvial Valley, Semiarid Brazil. Study focusThis study aims to assess the influence of climate variability and exploitation on groundwater accessibility, quantity and quality, based on a spatiotemporal data analysis from long-term monitoring field campaigns, conducted monthly from 2000 to 2019. This study successfully identified representative stable monitoring points, piezometers and wells, for piezometric and salinity levels employing the technique of relative differences, taking into account aquifer hydraulic properties. Trend analysis was then carried out adopting the Mann-Kendall Method, Sen's Slope test, Pettitt test, and the Seasonal Trend decomposition through the Loess (STL) method. Principal Component Multivariate Analysis (PCA) was also employed to validate long-term analysis of annual groundwater levels, rainfall, evapotranspiration, and pumping rates. New hydrological insights for the regionThe spatial-temporal variability of salinity is closely linked to the salt balance in regions with low hydraulic permeability and groundwater use for irrigation. Groundwater salinity presented a strong link between rainfall and groundwater levels. A temporal rise in evapotranspiration alongside declines in rainfall and groundwater levels was observed, potentially exacerbating groundwater scarcity alongside intensified aquifer exploitation. Furthermore, groundwater salinity presented a decreasing trend for the pumping wells and no trends for piezometers. This could potentially disrupt future irrigation plans and cause long-term water shortages in a region already under severe water scarcity. Hence, groundwater pumping constitutes a positive management alternative for controlling groundwater salinity in the domain.

Risk Assessment and Sources Apportionment of Toxic Metals in Two Commonly Consumed Fishes from a Subtropical Estuarine Wetland System.

The widespread occurrence of heavy metals in aquatic environments, resulting in their bioaccumulation within aquatic organisms like fish, presents potential hazards to human health. This study investigates the concentrations of five toxic heavy metals (Pb, Hg, Zn, Cu, and Cr) and their potential health implications in two economically important fish species (Otolithoides pama and Labeo bata) from a subtropical estuarine wetland system (Feni estuary, Bangladesh). Muscle and gill samples from 36 individual fish were analyzed using energy dispersive X-ray fluorescence (EDXRF). The results revealed that the average quantities of heavy metals in both fishes' muscle followed the declining order of Zn (109.41-119.93 mg/kg) > Cu (45.52-65.43 mg/kg) > Hg (1.25-1.39 mg/kg) > Pb (0.68-1.12 mg/kg) > Cr (0.31-5.82 mg/kg). Furthermore, Zn was found to be present in the highest concentration within the gills of both species. While the levels of Cu, Zn, and Cr in the fish muscle were deemed acceptable for human consumption, the concentrations of Pb and Hg exceeded the permissible limits (>0.5 mg/kg) for human consumption. Different risk indices, including estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), and carcinogenic or target risk (TR), revealed mixed and varying degrees of potential threat to human health. According to the EDI values, individuals consuming these fish may face health risks as the levels of Zn, Cu, and Cr in the muscle are either very close to or exceed the maximum tolerable daily intake (MTDI) threshold. Nevertheless, the THQ and HI values suggested that both species remained suitable for human consumption, as indicated by THQ (<1) and HI (<1) values. Carcinogenic risk values for Pb, Cr, and Zn all remained within permissible limits, with TR values falling below the range of (10-6 to 10-4), except for Zn, which exceeded it (>10-4). The correlation matrix and multivariate principal component analysis (PCA) findings revealed that Pb and Cr primarily stemmed from natural geological backgrounds, whereas Zn, Cu, and Hg were attributed to human-induced sources such as agricultural chemicals, silver nanoparticles, antimicrobial substances, and metallic plating. Given the significance of fish as a crucial and nutritious element of a balanced diet, it is essential to maintain consistent monitoring and regulation of the levels and origins of heavy metals found within it.

Evaluation Study of Ammonium Removal from Groundwater by Electrodialysis: Case Study of Real Groundwater from the City of Kenitra in Morocco.

In this work, we investigated the feasibility of ammonium removal by electrodialysis (ED), a well-known electro-membrane process, based on the selective migration of anions and cations through anion exchange membranes (AEMs) and cation exchange membranes (CEMs). ED experiments are performed using a laboratory pilot. The ion exchange membranes (IEMs) pair used is AXE/CMX, AXE as an AEMs and CMX as a CEMs. The first tests are performed with real groundwater solutions from Kenitra city (Morocco), spiked with an initial concentration of 3 mg/L NH4Cl. The results gave a specific demineralization (SD) to NH4 + ions of 84.60 %; for a demineralization rate (DR) of 80 % and a time of 60 min. The multivariate principal component analysis (PCA) presents a total inertia of 99.23 %, the majority of the variables of which are positively correlated on the C1 axis with a variance of 95.5 % than that of C2 of 3.78 %. The quality of the diluted water determined by the Legrand-Poirier method showed that the water was aggressive and that the addition of 4.54 mg/L Ca2+ was necessary to balance the water and make it fit for human consumption.

Evaluation and Selection of Multi-Spectral Indices to Classify Vegetation Using Multivariate Functional Principal Component Analysis

The identification, classification and mapping of different plant communities and habitats is of fundamental importance for defining biodiversity monitoring and conservation strategies. Today, the availability of high temporal, spatial and spectral data from remote sensing platforms provides dense time series over different spectral bands. In the case of supervised mapping, time series based on classical vegetation indices (e.g., NDVI, GNDVI, …) are usually input characteristics, but the selection of the best index or set of indices (which guarantees the best performance) is still based on human experience and is also influenced by the study area. In this work, several different time series, based on Sentinel-2 images, were created exploring new combinations of bands that extend the classic basic formulas as the normalized difference index. Multivariate Functional Principal Component Analysis (MFPCA) was used to contemporarily decompose the multiple time series. The principal multivariate seasonal spectral variations identified (MFPCA scores) were classified by using a Random Forest (RF) model. The MFPCA and RF classifications were nested into a forward selection strategy to identify the proper and minimum set of indices’ (dense) time series that produced the most accurate supervised classification of plant communities and habitat. The results we obtained can be summarized as follows: (i) the selection of the best set of time series is specific to the study area and the habitats involved; (ii) well-known and widely used indices such as the NDVI are not selected as the indices with the best performance; instead, time series based on original indices (in terms of formula or combination of bands) or underused indices (such as those derivable with the visible bands) are selected; (iii) MFPCA efficiently reduces the dimensionality of the data (multiple dense time series) providing ecologically interpretable results representing an important tool for habitat modelling outperforming conventional approaches that consider only discrete time series.

Voice self-assessment in individuals with Parkinson's Disease as compared to general voice disorders

BackgroundIndividuals with Parkinson's Disease (IwPD) often fail to adjust their voice in different situations, without awareness of this limitation. Clinicians use self-report questionnaires that are typically designed for individuals with General Voice Disorders (GVD) in the vocal assessment of IwPD. However, these instruments may not consider that IwPD have a reduced self-perception of their vocal deficits. This study aimed to compare self-reported vocal symptoms and voice loudness between IwPD and GVD. Methods28 IwPD and 26 with GVD completed the Voice Symptom Scale (VoiSS) questionnaire to evaluate their voice self-perception. Vocal loudness (dB) was also assessed. Univariate and multivariate analyses were used to compare the outcomes from these measures between the two groups. Principal Component Analysis and Hierarchical Clustering Analysis were applied to explore data patterns related to voice symptoms. ResultsIwPD reported significantly fewer vocal symptoms than those with GVD in all VoiSS questionnaire domains. Multivariate principal component analysis found no significant correlations between VoiSS scores and participant similarities in voice measures. Despite experiencing hypophonia, IwPD scored lower in all VoiSS domains but still fell in the healthy voice range. Hierarchical Clustering Analysis grouped participants into three distinct categories, primarily based on age, vocal loudness, and VoiSS domain scores, distinguishing between PD and GVD individuals. ConclusionsIwPD reported fewer vocal symptoms than GVD. The voice self-assessment seems to be unreliable to assess vocal symptoms in IwPD, at least regarding loudness. New self-report instruments tailored to PD individuals are needed due to their particular voice characteristics.

Air pollution exposure may impact the composition of human milk oligosaccharides

Human milk oligosaccharides (HMOs) impact neonate immunity and health outcomes. However, the environmental factors influencing HMO composition remain understudied. This study examined the associations between ambient air pollutant (AAP) exposure and HMOs at 1-month postpartum. Human milk samples were collected at 1-month postpartum (n = 185). AAP (PM2.5, PM10, NO2) exposure included the 9-month pregnancy period through 1-month postpartum. Associations between AAP with (1) HMO diversity, (2) the sum of sialylated and fucosylated HMOs, (3) 6 a priori HMOs linked with infant health, and (4) all HMOs were examined using multivariable linear regression and principal component analysis (PCA). Exposure to AAP was associated with lower HMO diversity. PM2.5 and PM10 exposure was positively associated with the HMO 3-fucosyllactose (3FL); PM2.5 exposure was positively associated with the sum of total HMOs, sum of fucosylated HMOs, and the HMO 2′-fucosyllactose (2′FL). PCA indicated the PM2.5, PM10, and NO2 exposures were associated with HMO profiles. Individual models indicated that AAP exposure was associated with five additional HMOs (LNFP I, LNFP II, DFLNT, LNH). This is the first study to demonstrate associations between AAP and breast milk HMOs. Future longitudinal studies will help determine the long-term impact of AAP on human milk composition.

Regio-specific lipid fingerprinting of edible sea cucumbers using LC/MS

Sea cucumbers are a rich source of bioactive compounds and are gaining popularity as nutrient-rich seafood. They are consumed as a whole organism in Pacific regions. However, limited data are available on the comparison of their lipid composition and nutritional value. In this study, untargeted liquid chromatography/mass spectrometry was applied to comprehensively profile lipids in the skin, meat, and intestinal contents of three color-distinct edible sea cucumbers. Multivariate principal component analysis revealed that the lipid composition of the intestinal contents of red, black, and blue sea cucumbers differs from that of skin, and meats. Polyunsaturated fatty acids (PUFAs) are abundant in the intestinal contents, followed by meats of sea cucumber. Lipid nutritional quality assessments based on fatty acid composition revealed a high P:S ratio, low index of atherogenicity, and high health promotion indices for the intestinal contents of red sea cucumber, suggesting its potential health benefits. In addition, hierarchical cluster analysis revealed that the intestinal contents of sea cucumbers were relatively high in PUFA-enriched phospholipids and lysophospholipids. Ceramides are abundant in black skin, blue meat, and red intestinal content samples. Overall, this study provides the first insights into a comprehensive regio-specific profile of the lipid content of sea cucumbers and their potential use as a source of lipid nutrients in food and nutraceuticals.

Characterization and childhood exposure assessment of toxic heavy metals in household dust under true living conditions from 10 China cities

Health hazards caused by metal exposure in household dust are concerning environmental health problems. Exposure to toxic metals in household dust imposes unclear but solid health risks, especially for children. In this multicenter cross-sectional study, a total of 250 household dust samples were collected from ten stratified cities in China (Panjin, Shijiazhuang, Qingdao, Lanzhou, Luoyang, Ningbo, Xi'an, Wuxi, Mianyang, Shenzhen) between April 2018 and March 2019. Questionnaire was conducted to gather information on individuals' living environment and health status in real-life situations. Multivariate logistic regression and principal component analysis were conducted to identify risk factors and determine the sources of metals in household dust. The median concentration of five metals in household dust from 10 cities ranged from 0.03 to 73.18 μg/g. Among the five heavy metals, only chromium in household dust of Mianyang was observed significantly both higher in the cold season and from the downwind households. Mercury, cadmium, and chromium were higher in the third-tier cities, with levels of 0.08, 0.30 and 97.28 μg/g, respectively. There were two sources with a contribution rate of 38.3 % and 25.8 %, respectively. Potential risk factors for increased metal concentration include long residence time, close to the motorway, decoration within five years, and purchase of new furniture within one year. Under both moderate and high exposure scenarios, chromium showed the highest level of exposure with 6.77 × 10−4 and 2.28 × 10−3 mg·kg−1·d−1, and arsenic imposed the highest lifetime carcinogenic risk at 1.67 × 10−4 and 3.17 × 10−4, respectively. The finding highlighted the priority to minimize childhood exposure of arsenic from household dust.

Effects of investment opportunities on firms' financial performance: a fresh look through a dynamic panel approach and multivariate analysis

The aim of this study is to investigate the macroeconomic component Investment Opportunities influence, extracted by the multivariate PCA method, on firms' financial performance. The analysis was conducted in two stages: i) Principal Component Analysis (PCA) to circumvent the multicollinearity problem and identification of the Investment Opportunities variable; and ii) Application of the Systemic Generalized Method of Moments (GMM) both to analyze the relationship between Investment Opportunities and firms' financial performance (measured by current and future ROE), and to correct for data endogeneity. The sample comprises 160 non-financial companies, with quarterly data from 2010 to 2020. We use the PCA method exploratively to identify the first principal component as the predictor variable in the econometric model. It was extracted from macroeconomic variables, which include GDP variation and activity indexes, confidence, and agents’ expectations in Brazilian economy. Finally, we applied the GMM panel data modeling. The results showed that the Investment Opportunities variable has a positive and significant effect on companies’ financial performance, with persistent effects in future quarters. Furthermore, we demonstrate that financial performance is highly responsive to these opportunities with significant results in all estimated models. This study makes a practical and theoretical contribution by demonstrating, through multivariate analysis, how economic conditions are important exogenous drivers of firm performance and competitive advantage.

Demonstrating the relevance of spatial-functional statistical analysis in marine ecological studies: The case of environmental variations in micronektonic layers

In this study, we conducted an analysis of a multifrequency acoustics dataset acquired from scientific echosounders in the West African water. Our objective was to explore the spatial arrangement of marine organism aggregations. We investigated various attributes of these intricate biological entities, such as thickness, relative density, and depth, in relation to their surroundings. These environmental conditions were represented at a fine scale using a towed multiparameter system. This study is closely intertwined with two key domains: Fisheries acoustics techniques and functional data analysis. Fisheries acoustics techniques facilitate the collection of high-resolution spatial and temporal data concerning marine organisms at various depths and spatial scales, all without causing any disturbance. On the other hand, spatial-functional data analysis is a statistical approach for examining data characterised by functional attributes distributed across a spatial domain. This analysis encompasses dimension reduction techniques, as well as supervised and unsupervised methods, which take into consideration spatial dependencies within extensive datasets.We began by applying multivariate statistical techniques and subsequently employed Functional Data Analysis (FDA). In the modeling section, we introduced the spatial dimension with the spatial coordinates as covariates in the General Additive Model (GAM) and Functional Generalized Spectral Additive Model (FGSAM) models, aiming to underscore its relevance in those contexts. In an exploratory phase, Multivariate Functional Principal Component Analysis provided detailed insights into the variations of parameters at different depths, a capability not offered by traditional Principal Component Analysis. When it came to regression tasks, we explored the interactions between descriptors of Sound Scattering Layers and key environmental variables, both with and without considering spatial dimensions. Our findings revealed significant distinctions between northern and southern Sound Scattering Layers, as well as between coastal and high-sea regions. The use of the spatial locations enhanced the performance of GAM and FGSAM, particularly in the case of salinity, reflecting the influence of water mixing and seawater temperature. The multifaceted effects of environmental variations on Sound Scattering Layers underscore the importance of spatial-functional statistical analysis in ecological studies involving complex, spatially functional objects. Beyond the scope of this specific case study, the application of functional data analysis shows promise for a wide array of ecological studies dealing with extensive spatial datasets.