Multivariate Statistical Data Analysis Research Articles

Did Covid-19 Kill Trump Politically? The Pandemic and Voting in the 2020 Presidential Election.

ObjectiveThis article investigates the impact of public reactions to the Covid‐19 panemic on voting for former President Donald Trump in the 2020 American presidential election.MethodsThe impact of the pandemic on voting is assessed by multivariate statistical analyses of representative national survey data gathered before and after the 2020 presidential election.ResultsAnalyses show that voters reacted very negatively to Trump's handling of the pandemic. Controlling for several other relevant factors, these reactions affected voting for Trump and exerted a significant impact on the election outcome.ConclusionBefore the onset of Covid‐19 Trump had a very narrow path to victory in 2020, and the pandemic did much to ensure his defeat.

Multivariate statistical data analysis of cell‐free protein synthesis toward monitoring and control

AbstractThe optimization and control of cell free protein synthesis (CFPS) presents an ongoing challenge due to the complex synergies and nonlinearities that cannot be fully explained in first principle models. This article explores the use of multivariate statistical tools for analyzing data sets collected from the CFPS of Cereulide monoclonal antibodies. During the collection of these data sets, several of the process parameters were modified to investigate their effect on the end‐point product (yield). Through the application of principal component analysis and partial least squares (PLS), important correlations in the process could be identified. For example, yield had a positive correlation with pH and NH3 and a negative correlation with CO2 and dissolved oxygen. It was also found that PLS was able to provide a long‐term prediction of product yield. The presented work illustrates that multivariate statistical techniques provide important insights that can help support the operation and control of CFPS processes.

A multigroup framework for fault detection and diagnosis in large-scale multivariate systems

In the multivariate system, a fault is often caused only by a few variables. However, in traditional fault detection and diagnosis (FDD) methods, all of variables are included in the fault detection index. In this case, the effect of fewer faulty variables on the fault detection index may be weakened by the introduction of a larger number of fault-free variables. Consequently, the FDD performance is reduced. To address this problem, this paper proposes a multigroup FDD framework for large-scale multivariate systems. This framework is based on three new approaches: a variable grouping algorithm, and two methods for the statistical analysis of multivariate data in the form of variable groups, called group-wise sparse principal component analysis (GSPCA) and inter-group canonical correlation analysis (IGCCA). The variable grouping algorithm generates optimal variable groups by maximizing variable correlations within groups while minimizing variable correlations among groups. The GSPCA produces a set of group-wise sparse components. Each component has nonzero loadings only for variables in one group, and thus it explains variable correlations in the corresponding group. Different from GSPCA, the IGCCA can extract the maximum correlations between variable groups. The multigroup FDD framework consists of two parts: the intra-group FDD based on a joint T2 statistic that is defined using components of GSPCA, and the inter-group FDD based on a T2 statistic that is defined using the residuals generated by IGCCA. Two case studies are used to illustrate advantages of the multigroup FDD framework.

Avaliação de incêndio em ambiente de Caatinga a partir de imagens Landsat-8, índice de vegetação realçado e análise por componentes principais

O fogo é um fator importante na perturbação e perda de florestas secas tropicais globais. Os incêndios florestais exercem um papel ecológico relevante, pois afetam a biodiversidade local, as propriedades do solo e o suprimento de água. O bioma Caatinga apresenta um alto nível de degradação de atividades antrópicas e naturais, sendo extremamente afetado por incêndios originados predominantemente por atividades humanas. O sensoriamento remoto orbital, por apresentar características espaciais, espectrais e temporais específicas, é uma alternativa tecnológica imprescindível no monitoramento de áreas afetadas pelo fogo na superfície terrestre. Este trabalho teve como objetivo analisar, no âmbito espacial, espectral e temporal, o comportamento de um incêndio em ambiente de Caatinga a partir de Imagens Landsat-8, Índice de Vegetação Realçado e Análise por Componentes Principais. A quantificação de características da vegetação derivada do índice espectral fornece uma melhor avaliação da condição física da superfície terrestre sob efeitos do fogo. Técnicas de sensoriamento remoto e estatística multivariada foram utilizadas para avaliar comportamento espectral da vegetação nativa exposta a eventos de incêndio do bioma Caatinga. Os resultados do Teste de Normalidade Kolmogorov-Smirnov apresentaram um nível de significância de 5 %. A integração dos métodos estatísticos de Regressão Linear Simples e Análise por Componentes Principais possibilitaram diagnósticos importantes nas estimativas e/ou relacionamentos entre as variáveis aleatórias. A técnica multivariada permitiu avaliar 94% da variação de dados. Os mapas resultantes da metodologia testada representam um aprimoramento importante no mapeamento da distribuição da vegetação. Este estudo gera indicativos para futuras pesquisas científicas vinculadas ao gerenciamento do espaço relacionado à vulnerabilidade e recuperação de paisagens de vegetação do clima semiárido sob situações de fogo geradas por incêndios.

1H-NMR and LC-MS Based Metabolomics Analysis of Wild and Cultivated Amaranthus spp.

Amaranthus crops are important for their use as food and nutritional sources, as well as for their medicinal properties. They are mostly harvested from the wild, and cultivation of Amaranthus species is still rare, and therefore, attempts are being made to commercialize and market this important crop. This research investigated the effect of cultivation and environment on the chemical profile of both cultivated and wild A. cruentus and A. hybridus by multivariate statistical analysis of spectral data deduced by Nuclear Magnetic Resonance (NMR). Furthermore, wild samples of A. cruentus and A. hybridus were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS) for further analysis. Through NMR analysis, it was found that maltose and sucrose increased in both cultivated A. cruentus and A. hybridus. Moreover, the amino acid, proline was present in cultivated A. cruentus in high quantity whereas, proline and leucine were prominent in A. hybridus. Other compounds that were found in both wild and cultivated A. cruentus and A. hybridus are trehalose, trigonelline, lactulose, betaine, valine, alanine, fumarate, formate and kynurenine. LC-MS analysis revealed the presence of rutin, 2-phenylethenamine and amaranthussaponin I in both wild A. cruentus and A. hybridus, while chlorogenic acid was identified only in cultivated A. hybridus. On the contrary, L-tryptophan, kaempferol, phenylalanine and quercetin were detected only in wild A. cruentus. Amaranth is not only rich in macro and micronutrients, but the leaves also contain phytochemicals that vary between species and cultivated plants, and might, therefore, affect the medicinal properties of the material.

Potentialities of Rapid Analytical Strategies for the Identification of the Botanical Species of Several "Specialty" or "Gourmet" Oils.

A comprehensive data collection of authentic “specialty” or “gourmet” oils, namely cold-pressed industrial virgin oils, was performed. Eight different botanical species, i.e., Almond, Apricot, Avocado, Hazelnut, Mosqueta rose, Rosehip, Sunflower, and Walnut oils were studied plus Olive oil as the gold standard of cold-pressed virgin oils. Two different analytical approaches are proposed to rapidly verify the botanical species of the oil-based raw material. The first approach is based on a multivariate statistical analysis of conventional analytical data, namely their fatty acid composition. These data have been re-elaborated in a multivariate way by Principal Component Analysis (PCA) and classification methods. The second approach proposes a fast and non-destructive spectrophotometric analysis to determine the color of these oils to discriminate among different species. In this regard, the raw diffuse reflectance spectra (380–780 nm) obtained by a UV-Vis spectrophotometer with an integrating sphere was considered and elaborated by chemometrics. This information was compared with the results obtained by the most common approach based on the CIELab parameters. A data fusion of chromatographic and spectral data was also investigated. Either fatty acid composition or color of these oils demonstrated to be two promising markers of their botanical authenticity.

Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer.

Simple SummaryBreast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and tailored monitoring are in critical demand. Thus, metabolomics appears to be a growing tool in order to detect molecular differences between distinct groups. In this case, an untargeted analytical approach was used to identify metabolomics differences between molecular subtypes of breast cancer in comparison with healthy matched controls. Footprints for each breast cancer subtype provided diagnostic capacities with an area under the receiver-operating characteristic curve above 0.85, which suggests that our results may represent a major advance towards the improvement of personalized medicine and precise targeted therapies for the various breast cancer phenotypes. To validate these molecular profiling as potential therapeutic strategies for the different breast cancer subtypes, further analysis and larger cohorts would be necessary in the near future.Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.

Differentiation of gestational diabetes mellitus by nuclear magnetic resonance-based metabolic plasma analysis.

This study aimed to investigate the metabolic profile of gestational diabetes mellitus (GDM) at both antepartum and postpartum periods. Seventy pregnant women were divided into three groups: the normal glucose-tolerant group (NGT, n=35), the abnormal glucose-tolerant groups without insulin therapy (A1GDM, n=24) or with insulin therapy (A2GDM, n=11). Metabolic profiles of the plasma were acquired by proton nuclear magnetic resonance (1H-NMR) spectroscopy and analyzed by multivariate statistical data analysis. The relationship between demographic parameters and the potential metabolite biomarkers was further explored. Group antepartum or postpartum showed similar metabolic trends. Compare with those of the NGT group, the levels of 2-hydroxybutyrate, lysine, acetate, glutamine, succinate, tyrosine, formate, and all three BCAAs (leucine, valine, isoleucine) in the A2GDM group were increased dramatically, and the levels of lysine, acetate, and formate in the A1GDM group were elevated significantly. The dramatically decreased levels of 3-methyl-2-oxovalerate and methanol were observed both in the A1GDM group and A2GDM group. Compare to the A1GDM group, the branched-chain amino acids (BCAAs) of leucine, valine, and isoleucine were increased dramatically in the A2GDM group. The levels of aromatic amino acids (AAAs), tyrosine and phenylalanine, were significantly increased in GDM women, consistent with the severity of GDM. Interference of amino acid metabolism and disturbance in energy metabolism occurred in women with different grades of GDM. Metabolic profiles could reflect the severity of GDM. Plasma BCAA concentrations showing strong positive correlations with weight and pre-delivery BMI. This study provides a new perspective to understand the pathogenesis and etiology of GDM, which may help the clinical management and treatment of GDM.

Differences in amino acid and lipid metabolism distinguish Crohn's from idiopathic/cryptoglandular perianal fistulas by tissue metabonomic profiling and may offer clues to underlying pathogenesis.

Few studies have investigated perianal fistula etiopathogenesis, and although the cryptoglandular theory is widely accepted in idiopathic cases, in Crohn's disease, it is thought to involve the interplay between microbiological, immunological and genetic factors. A pilot study was conducted to assess for metabolic variations in Crohn's perianal fistula tissue that might differ from that of idiopathic (cryptoglandular) perianal fistula tissue as a comparator. The goal was to identify any potential biomarkers of disease, which may improve the understanding of pathogenesis. Fistula tract biopsies were obtained from 30 patients with idiopathic perianal fistula and 20 patients with Crohn's anal fistula. Two different assays were used in an ultra-high-performance liquid chromatography system coupled with a mass spectrometric detector to achieve broad metabolome coverage. Univariate and multivariate statistical data analyses were used to identify differentiating metabolic features corresponding to the perianal fistula phenotype (i.e. Crohn's disease vs. idiopathic). Significant orthogonal partial least squares discriminant analysis predictive models (validated with cross-validated-analysis of variance P value <0.05) differentiated metabolites from tissue samples from Crohn's vs. idiopathic anal fistula patients using both metabolic profiling platforms. A total of 41 metabolites were identified, suggesting alterations in pathways, including amino acid, carnitine and lipid metabolism. Metabonomics may reveal biomarkers of Crohn's perianal fistula. Further work in larger numbers is required to validate the findings of these studies as well as cross-correlation with microbiome work to better understand the impact of host-gut/environment interactions in the pathophysiology of Crohn's and idiopathic perianal fistulas and identify novel therapeutic targets.

Combination of Stable Isotope Analysis and Chemometrics to Discriminate Geoclimatically and Temporally the Virgin Olive Oils from Three Mediterranean Countries.

The knowledge of the isotopic composition of virgin olive oil (VOO) allows the evaluation of authenticity and geographical origin, being an important tool against fraud. This study aimed to assess if VOOs produced in three Mediterranean regions could be discriminated on the basis of multivariate statistical analysis of geoclimatic and isotopic data. A total of 138 geo-referenced VOO samples from Portugal, France and Turkey from two different cultivation years were collected. The isotopic composition (δ13C, δ2H and δ18O) of VOOs was obtained using an elemental analyzer coupled to an isotope ratio mass spectrometer (EA-IRMS). One-way analysis of variance for δ13C, δ2H and δ18O showed some significant differences either between crop years or geoclimatic conditions. Based on multiple regression analyses using meteorological and geographical parameters, a meteoric water line for olive oil from Portugal, France and Turkey, in two harvest years, were created to assess the impact of climate change on their δ2H and δ18O values. Principal component analysis and Linear Discriminant Analysis, used to sort samples according to geoclimatic origin, performed best for French and Portuguese olive oils. In light of the results, multivariate isotopic analysis of VOO samples may discriminate not only between geoclimatic regions but also among cultivation years.

Immunomodulatory Constituents of Conocephalum conicum (Snake Liverwort) and the Relationship of Isolepidozenes to Germacranes and Humulanes.

Structural elucidation of three new sesquiterpenoids, namely, (1Z,4E)-lepidoza-1(10),4-dien-14-ol (1), rel-(1(10)Z,4S,5E,7R)-germacra-1(10),6 diene-11,14-diol (2), and rel-(1(10)Z,4S,5E,7R)-humula-1(10),5-diene-7,14-diol (3), isolated from the liverwort Conocephalum conicum, was accomplished by a combination of extensive NMR experiments, 1H NMR simulation, and other means. Additionally, the change of the identity of bicyclogermacren-14-al, previously reported as a C. conicum constituent, to isolepidozen-14-al is proposed. Compounds 2 and 3 appear to be related to 1 via hydration involving a shared intermediate, a substituted cyclopropylmethyl cation, formed by a highly regio- and stereoselective protonation of 1, followed by a stereospecific fission of the three-membered ring. In other words, an isolepidozene derivative might be a branchpoint to humulanes and germacranes; this transformation could be of, up to now, unknown, biosynthetic and/or synthetic relevance. Multivariate statistical analysis of the compositional data of C. conicum extract constituents was used to probe the hypothesized biochemical relations. The immunomodulatory effect of 1-3 and conocephalenol (4) was evaluated in an in vitro model on both nonstimulated and mitogen-stimulated rat splenocytes. The compounds displayed varying degrees of cytotoxicity to nonstimulated splenocytes, whereas 2 and 3 were found to exert immunosuppressive effects on concanavalin A-stimulated splenocytes while not being cytotoxic at the same concentrations.

Investigation of Virulence Genes Detected in Antimicrobial-Resistance Pathogens Isolates for Five Countries across the World

A large portion of annual deaths worldwide are due to infections caused by disease-causing pathogens. These pathogens contain virulence genes, which encode mechanisms that facilitate infection and microbial survival in hosts. More recently, antimicrobial resistance (AMR) genes, also found in these pathogens, have become an increasingly large issue. While the National Center for Biotechnology Information (NCBI) Pathogen Detection Isolates Browser (NPDIB) database has been compiling genes involved in microbial virulence and antimicrobial resistance through isolate samples, few studies have identified the genes primarily responsible for virulence and compared them to those responsible for AMR. This study performed the first multivariate statistical analysis of the multidimensional NPDIB data to identify the major virulence genes from historical pathogen isolates for Australia, China, South Africa, UK, and US—the largely populated countries from five of the six major continents. The important virulence genes were then compared with the AMR genes to study whether there is correlation between their occurrences. Among the significant genes and pathogens associated with virulence, it was found that the genes fdeC, iha, iss, iutA, lpfA, sslE, ybtP, and ybtQ are shared amongst all five countries. The pathogens E. coli and Shigella, Salmonella enterica, and Klebsiella pneumoniae mostly contained these genes and were common among four of the five studied countries. Additionally, the trend of virulence was investigated by plotting historical occurrences of gene and pathogen frequency in the annual samples. These plots showed that the trends of E. coli and Shigella and Salmonella enterica were similar to the trends of certain virulence genes, confirming the two pathogens do indeed carry important virulence genes. While the virulence genes in the five countries are not significantly different, the US and the UK share the largest amount of important virulence genes. The plots from principal component analysis and hierarchical clustering show that the important virulence and AMR genes were not significantly correlated, with only few genes from both types of genes clustered into the same groups.

Nuclear Magnetic Resonance-Based Metabolomic Analysis of the Anticancer Effect of Metformin Treatment on Cholangiocarcinoma Cells.

Metformin is a widely prescribed anti-diabetes drug with potential utilities for cancer therapies. Several studies have related metformin to the reduced risk of cholangiocarcinoma (CCA), highlighting its potentialities for the treatments of CCA. However, the underlying molecular mechanisms remain elusive. Here, we demonstrated that metformin treatment could inhibit proliferations of the human CCA cell lines Mz-ChA-1 and QBC939 in dose-dependent manners. The NMR-based metabonomic analyses showed distinct discriminations between the metformin-treated (Met) and control (Ctrl) groups of both CCA cells. Characteristic metabolites were identified by a combination of multivariate statistical analysis of 1D 1H-NMR spectral data and the pair-wise t-test of metabolite levels. We then identified four significantly altered metabolic pathways based on the characteristic metabolites, including glucose metabolism, oxidative stress-related metabolism, energy metabolism, and amino acids metabolism. Comparing CCA cells with normal human umbilical vein endothelial cells (HUVECs), we found that metformin treatment profoundly promoted glycolysis and specifically increased the levels of BCAAs and UDP-GlcNAc, implying the occurrence of autophagy and cell cycle arrest in metformin-treated CAA cells. This work provides a mechanistic understanding of the anticancer effect of metformin treatment on CAA cells, and is beneficial to further developments of metformin as an anticancer drug.

Influence of Autochthonous Putative Probiotic Cultures on Microbiota, Lipid Components and Metabolome of Caciotta Cheese.

The present study was undertaken to produce probiotic Caciotta cheeses from pasteurized ewes’ milk by using different combinations of autochthonous microbial cultures, containing putative probiotic strains, and evaluate their influence on gross composition, lipid components, sensory properties and microbiological and metabolite profiles of the cheeses throughout ripening process. A control cheese was produced using commercial starter cultures. The hydrophilic molecular pools (mainly composed by amino acids, organic acids, and carbohydrates) were characterized by means of 1H NMR spectroscopy, while the cholesterol, α-tocopherol and fatty acid composition by HPLC-DAD/ELSD techniques. Conventional culturing and a PCR-DGGE approach using total cheese DNA extracts were used to analyze cheese microbiota and monitor the presence and viability of starters and probiotic strains. Our findings showed no marked differences for gross composition, total lipids, total cholesterol, and fatty acid levels among all cheeses during ripening. Differently, the multivariate statistical analysis of NMR data highlighted significant variations in the cheese’ profiles both in terms of maturation time and strains combination. The use of autochthonous cultures and adjunct probiotic strains did not adversely affect acceptability of the cheeses. Higher levels of lactobacilli (viability of 108–109 cfu/g of cheese) were detected in cheeses made with the addition of probiotic autochthonous strains with respect to control cheese during the whole ripening period, suggesting the adequacy of Caciotta cheese as a carrier for probiotic bacteria delivery.

Hydrocarbon Contamination Patterns in the Cenotes of the Mexican Caribbean: The Application of Principal Component Analysis

Cenotes are naturally occurring flooded caves that are frequent in Mexico's Yucatan Peninsula; they result from the collapse of limestone bedrock into the regional groundwater table. Cenotes in Quintana Roo are important ecological and economic hot spots but are susceptible to anthropogenic pollution. In this study, we collected water samples from 11 cenotes over multiple years to evaluate polycyclic aromatic hydrocarbon (PAH) concentrations and patterns as associated with tourist traffic. The primary PAHs detected in samples included fluoranthene, anthracene, phenanthrene and naphthalene, with total PAH concentrations increasing almost fivefold for cenotes sampled from 2016 to 2017. This is compared to only a 7% increase in tourist traffic during these years. Multivariate statistical analysis of the PAH concentration data suggests that diesel, gasoline and asphalt are the most likely pollution sources and that they are associated with periods of increased tourist traffic.

Differentially Expressed Proteins in Intra Synovial Compared to Extra Synovial Flexor Tendon Grafts in a Rabbit Tendon Transplantation Model.

Uncomplicated healing of grafts for tendon reconstruction remains an unsolved problem in hand surgery. Results are limited by adhesion formation and decreased strength properties, especially within the tight fibro-osseous sheath of the digits. This is especially problematic when an extra synovial tendon graft is used to replace an intra synovial flexor tendon. Compositional differences are likely to play an important role in these processes. The aim of this study was, therefore, to compare protein expression in pair-matched intra synovial tendon grafts with extra synovial tendon grafts, using a rabbit tendon injury model. We hypothesized that there would be significant differences in proteins critical for response to tensile loading and adhesion formation between the two groups. Using mass spectrometry and multivariate statistical data analysis, we found tissue-specific differences in 22 proteins, where 7 explained 93% (R2) of the variation, with a prediction of 81% (Q2). Among the highest discriminating proteins were Galectin, Histone H2A, and Periostin, which were found in a substantially larger amount in the extra synovial tendons compared to the intra synovial tendons. These findings may contribute to improved understanding of the differences in outcome seen after tendon reconstruction using tendon grafts with intra synovial and extra synovial grafts.

Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach.

Simple SummaryThere are currently no effective specific biomarkers for the screening of early gastric cancer. Recently, metabolomics has been used to profile small endogenous metabolites, demonstrating significant potential in the diagnosis/screening of cancer, owing to its ability to conduct a noninvasive sample analysis. Here, we performed a urine metabolomics analysis in the context of an early diagnosis of gastric cancer. This approach showed very high diagnostic sensitivity and specificity and performed significantly better than the analysis of serum tumor markers modalities. An additional genomic data analysis revealed the up-regulation of several genes in gastric cancer. This metabolomics-based early diagnosis approach may have the potential for mass screening an average-risk population and may facilitate endoscopic examination through risk stratification.The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.

Adaptive behavioural coping strategies as reaction to COVID-19 social distancing in Italy.

Social distancing, as experienced by the Italian population during the COVID-19 outbreak, generated the long-term activation of stress-response in individuals. This has been a crucial opportunity to study the coping strategies that people put in place to adapt their lives and habits to such a unique condition. For this reason, we have investigated both emotion-focused and problem-oriented coping strategies among the Italian population by relating them to other structural factors, such as social, economic and cultural conditions. More than 140,000 online interviews were collected in Italy from March 22 to April 2, 2020. This large dataset was used to carry out multivariate statistical data analysis with the aim of creating behavioral profiles, starting from coping parameters and other information. This technique has produced an association, showing a good level of statistical significance, between coping strategies and reactions to social distancing, due to the health emergency, during the data collection phase. Two coping indicators - problem-oriented and focused on positive emotions coping strategies - were selected as objective variables in a "decision tree" modelling. The results have shown a link between individual factors (i.e., atmosphere at home) and educational and social factors (i.e., compliance with restrictions during the health emergency). The reduction of social interaction had quite a significant impact on people's behavior; furthermore, coping strategies have played a crucial role in facing this stressor. For both the selected coping indicators, the best predictor was the atmosphere perceived at home. Moreover, the respondents' previous experiences have played a relevant role in the acceptance of new rules imposed by the government. This information can be useful in planning future social policies, both at national and international level, during such peculiar times.

LC-MS-Based Metabolomics for the Chemosystematics of Kenyan Dodonaea viscosa Jacq (Sapindaceae) Populations.

Dodonaea viscosa Jacq (Sapindaceae) is a medicinal plant with a worldwide distribution. The species has undergone enormous taxonomic changes which caused confusion amongst plant users. In Kenya, for example, two varieties are known to exist based on morphology, i.e., D. viscosa var. viscosa along the coast, and D. viscosa var. angustifolia in the Kenyan inland. These two taxa are recognized as distinct species in some reports. This prompted us to apply metabolomics to understand the relationship among naturally occurring populations of D. viscosa in Kenya, and to identify compounds that can assist in taxonomic delineation of the different varieties of D. viscosa from different parts of Kenya. The phytochemical variability of Kenyan D. viscosa var. angustifolia populations collected from four different geographical regions (Nanyuki, Machakos, Nairobi, and Narok) and one coastal D. viscosa var. viscosa (the Gazi) were analyzed by LC-MS using a metabolomics-driven approach. Four known compounds, two diterpenoids (dodonic acid (1), hautriwaic acid lactone (3), and two flavonoids (5,7,4′,5′-tetrahydroxy-3,6,2′-trimethoxyflavone (2) and catechin (4)) were isolated and purified from the Gazi coastal collection. The presence of these compounds and their relative abundance in other populations was determined by LC-MS analyses. Multivariate statistical analyses of LC-MS data was used for the visualization of the patterns of variation and identification of additional compounds. Eleven discriminant compounds responsible for separating chemometric clusters were tentatively identified. In an antimicrobial assay, hautriwaic acid lactone (3) and catechin (4) were the most active compounds followed by the extract from the coastal (Gazi) population. The clustering pattern of the five populations of D. viscosa suggested that the metabolite profiles were influenced by geo-environmental conditions and did not support the current classification of D. viscosa based on morphology. This study disputes the current classification of D. viscosa in Kenya and recommends revision using tools such as molecular phylogenetics.

Visible Light-Activated Water-Soluble Platicur Nanocolloids: Photocytotoxicity and Metabolomics Studies in Cancer Cells.

Nanoparticle-based drug delivery systems for cancer therapy offer a great promising opportunity as they specifically target cancer cells, also increasing the bioavailability of anticancer drugs characterized by low water solubility. Platicur, [Pt(cur) (NH3)2](NO3), is a cis-diamine-platinum(II) complex linked to curcumin. In this work, an ultrasonication method, coupled with layer by layer technology, allows us to obtain highly aqueous stable Platicur nanocolloids of about 100 nm. The visible light-activated Platicur nanocolloids showed an increased drug release and antitumor activity on HeLa cells, with respect to Platicur nanocolloids in darkness. This occurrence could give very interesting insight into selective activation of the nanodelivered Pt(II) complex and possible side-effect lowering. For the first time, the metabolic effects of Platicur nanocolloid photoactivation, in the HeLa cell line, have been investigated using an NMR-based metabolomics approach coupled with statistical multivariate data analysis. The reported results highlight specific metabolic differences between photoactivated and non-photoactivated Platicur NC-treated HeLa cancer cells.