Multivariate Discrimination Research Articles

Metabolomics based on GC-MS combined with chemometrics for geographical discrimination of garlic (Allium sativum L.)

Ensuring the geographic origin of products helps protect consumers against fraud and mislabeling. This study investigates the metabolomic profiles of garlic (Allium sativum L.) samples from Brazil and China using gas chromatography-mass spectrometry (GC-MS). Multivariate analysis Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal PLS-DA (Ortho PLS-DA) revealed clear metabolic distinctions between the two sample groups with high classification accuracy. Brazilian garlic samples showed higher levels of sulfur compounds, such as allyl propyl sulfide, cysteine sulfonic acid, and S-carboxymethyl-L-cysteine, which contribute to its pungency and potential health benefits. Amino acids L-arginine, L-glutamic acid, and L-cysteine were also significantly increased in garlic from Brazil. In contrast, Chinese garlic exhibited higher intensities of sugars (e.g., D-fructose, D-mannose) and essential amino acids (L-tryptophan, L-valine, and L-leucine). These differences in metabolism are the result of distinct metabolic profiles. GC-MS-based untargeted metabolomic and chemometric analysis were able to differentiate the geographical origin of Brazilian and Chinese garlics and remain valuable to food authentication and quality control. Further studies are needed to confirm the biomarkers for origin verification here stated.

Measurement of single top-quark production in association with a W boson in pp collisions at s=13 TeV with the ATLAS detector

The inclusive cross section for the production of a single top quark in association with a W boson is measured using 140 fb−1 of proton-proton collision data collected with the ATLAS detector at s=13 TeV. Events containing two charged leptons and at least one jet identified as originating from a b-quark are selected. A multivariate discriminant is constructed to separate the tW signal from the tt¯ background. The cross section is extracted using a profile likelihood fit to the signal and control regions and it is measured to be σtW=75−14+15 pb, in good agreement with the Standard Model prediction. The measured cross section is used to extract a value for the left-handed form factor at the Wtb vertex times the Cabibbo-Kobayashi-Maskawa matrix element |fLVVtb| of 0.97±0.10. © 2024 CERN, for the ATLAS Collaboration 2024 CERN

Development of a clinical risk score for the prediction of Pneumocystis jirovecii pneumonia in hospitalised patients

BackgroundThe performance and availability of invasive and non-invasive investigations for the diagnosis of Pneumocystis jirovecii pneumonia (PCP) vary across clinical settings. Estimating the pre-test probability of PCP is essential to the optimal selection and interpretation of diagnostic tests, such as the 1,3-β-D-glucan assay (BDG), for the prioritization of bronchoscopy, and to guide empiric treatment decisions. We aimed to develop a multivariable risk score to estimate the pre-test probability of PCP.MethodsThe score was developed from a cohort of 626 individuals who underwent bronchoscopy for the purposes of identifying PCP in a Canadian tertiary-care centre, between 2015 and 2018. We conducted a nested case-control study of 57 cases and 228 unmatched controls. Demographic, clinical, laboratory, and radiological data were included in a multivariable logistic regression model to estimate adjusted odds ratios for PCP diagnosis. A clinical risk score was derived from the multivariable model and discrimination was assessed by estimating the score’s receiver operating characteristic curve.ResultsParticipants had a median age of 60 years (interquartile range [IQR] 49–68) and 115 (40%) were female; 40 (14%) had HIV and 49 (17%) had a solid organ transplant (SOT). The risk score included prior SOT or HIV with CD4 ≤ 200/µL (+ 2), serum lactate dehydrogenase ≥ 265.5 IU/mL (+ 2), radiological pattern typical of PCP on chest x-ray (+ 2) or CT scan (+ 2.5), and PCP prophylaxis with trimethoprim-sulfamethoxazole (-3) or other antimicrobials (-2). The median score was 4 points (IQR, 2-4.5) corresponding to a 28% probability of PCP. The risk prediction model had good discrimination with a c-statistic of 0.79 (0.71–0.84). Given the operating characteristics of the BDG assay, scores ≤ 3 in patients without HIV, and ≤ 5.5 in those with HIV, paired with a negative BDG, would be expected to rule out PCP with 95% certainty.ConclusionWe propose the PCP Score to estimate pre-test probability of PCP. Once validated, it should help clinicians determine which patients to refer for invasive investigations, when to rely on serological testing, and in whom to consider pre-emptive treatment.

Search for Baryon Number Violation in Top Quark Production and Decay Using Proton-Proton Collisions at s=13 TeV

A search is presented for baryon number violating interactions in top quark production and decay. The analysis uses data from proton-proton collisions at a center-of-mass energy of 13 TeV, collected with the CMS detector at the LHC with an integrated luminosity of 138 fb−1. Candidate events are selected by requiring two oppositely charged leptons (electrons or muons) and exactly one jet identified as originating from a bottom quark. Multivariate discriminants are used to separate the signal from the background. No significant deviation from the standard model prediction is observed. Upper limits are placed on the strength of baryon number violating couplings. For the first time the production of single top quarks via baryon number violating interactions is studied. This allows the search to set the most stringent constraints to date on the branching fraction of the top quark decay to a lepton, an up-type quark (u or c), and a down-type quark (d, s, or b). The results improve the previous bounds by 3 to 6 orders of magnitude based on the fermion flavor combination of the baryon number violating interactions. © 2024 CERN, for the CMS Collaboration 2024 CERN

Classification of Eugenia uniflora and E. punicifolia based on anatomical features and multivariate discrimination based on chemical composition.

Classification of Eugenia uniflora and E. punicifolia based on anatomical features and multivariate discrimination based on chemical composition.

Salivary metabolomics in patients with oral lichen planus: a preliminary study based on NMR spectroscopy

ObjectivesThe present preliminary study aimed to investigate the salivary metabolic profile in patients with asymptomatic oral lichen planus (OLP) using nuclear magnetic resonance (NMR) spectroscopy.Material and methodsStimulated whole mouth saliva (SWMS) samples were collected from 15 reticular OLP female patients and 15 from age- and sex-matched controls (HCs). A total of 23 metabolites were identified and quantified. Mann–Whitney’s U test was used to compare the determined concentration salivary metabolite concentrations between OLP patients and the healthy controls.ResultsThe concentration of acetate, methylamine, and pyruvate was elevated, whereas the concentration of tyrosine was decreased in the saliva of OLP patients compared with HCs. To identify a combination of metabolites, multivariate discrimination function analysis (DFA) was conducted. DFA analysis have shown that the most powerful discrimination between the groups was achieved when methylamine and tyrosine were considered as combined biomarkers.ConclusionsSalivary tyrosine was of particular interest and a promising finding for the screening of OLP and its progression. Further longitudinal studies are required to establish it as a reliable salivary biomarker in OLP.Clinical relevanceThe salivary metabolic profiling can describe the pathologic characteristics of OLP on non-invasive saliva samples and NMR analysis. Salivary metabolites provide details to considered early detectors and to impact oral health of OLP patients.

In Search of Authenticity Biomarkers in Food Supplements Containing Sea Buckthorn: A Metabolomics Approach.

Sea buckthorn (Hippophae rhamnoides L.) (SB) is increasingly consumed worldwide as a food and food supplement. The remarkable richness in biologically active phytochemicals (polyphenols, carotenoids, sterols, vitamins) is responsible for its purported nutritional and health-promoting effects. Despite the considerable interest and high market demand for SB-based supplements, a limited number of studies report on the authentication of such commercially available products. Herein, untargeted metabolomics based on ultra-high-performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry (UHPLC-QTOF-ESI+MS) were able to compare the phytochemical fingerprint of leaves, berries, and various categories of SB-berry herbal supplements (teas, capsules, tablets, liquids). By untargeted metabolomics, a multivariate discrimination analysis and a univariate approach (t-test and ANOVA) showed some putative authentication biomarkers for berries, e.g., xylitol, violaxanthin, tryptophan, quinic acid, quercetin-3-rutinoside. Significant dominant molecules were found for leaves: luteolin-5-glucoside, arginine, isorhamnetin 3-rutinoside, serotonin, and tocopherol. The univariate analysis showed discriminations between the different classes of food supplements using similar algorithms. Finally, eight molecules were selected and considered significant putative authentication biomarkers. Further studies will be focused on quantitative evaluation.

Search for pair production of third-generation leptoquarks decaying into a bottom quark and a τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-lepton with the ATLAS detector

A search for pair-produced scalar or vector leptoquarks decaying into a b-quark and a τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-lepton is presented using the full LHC Run 2 (2015–2018) data sample of 139 fb-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} collected with the ATLAS detector in proton–proton collisions at a centre-of-mass energy of s=13\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{s} =13$$\\end{document} TeV. Events in which at least one τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-lepton decays hadronically are considered, and multivariate discriminants are used to extract the signals. No significant deviations from the Standard Model expectation are observed and 95% confidence-level upper limits on the production cross-section are derived as a function of leptoquark mass and branching ratio B\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {B}$$\\end{document} into a τ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au $$\\end{document}-lepton and b-quark. For scalar leptoquarks, masses below 1460 GeV are excluded assuming B=100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {B}=100$$\\end{document}%, while for vector leptoquarks the corresponding limit is 1650 GeV (1910 GeV) in the minimal-coupling (Yang–Mills) scenario.

Gas sensor-based machine learning approaches for characterizing tarragon aroma and essential oil under various drying conditions

Aroma is one of the most significant quality traits for many pharmaceutical plants and their products as it indicates the quality of the raw material. The aroma may be lowered to imperceptible levels, altered, or damaged during the processing of herbs, such as drying or fermentation. Using an electronic nose (e-nose) is one of the most efficient approaches for identifying and evaluating the aroma of essential oils (EO). In this study, tarragon was dried in a hybrid dryer designed explicitly for frying the herbs at four temperatures (40, 50, 60, and 70 °C) and three air velocities (1, 1.5, and 2 m/s). After extracting its EO, the purity of the tarragon EO was assessed by an e-nose comprising nine metal oxide semiconductor (MOS) sensors. The highest EO levels (0.359) was obtained upon drying at 40 °C. By increasing the temperature from 40° to 70°C, the EO was declined, and its lowest level (0.26) was assessed at 70 °C. multivariate data analysis and artificial neural networks modeling were also employed to quantify and classify the obtained EOs based on the output of the sensor. Multivariate discrimination analysis (MDA) and Quadratic discriminant analysis (QDA) offered a 100 % accuracy in classifying 12 groups of EO.

Search for a new heavy scalar particle decaying into a Higgs boson and a new scalar singlet in final states with one or two light leptons and a pair of τ-leptons with the ATLAS detector

A search for a new heavy scalar particle X decaying into a Standard Model (SM) Higgs boson and a new singlet scalar particle S is presented. The search uses a proton-proton (pp) collision data sample with an integrated luminosity of 140 fb−1 recorded at a centre-of-mass energy of s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{s} $$\\end{document} = 13 TeV with the ATLAS detector at the Large Hadron Collider. The most sensitive mass parameter space is explored in X mass ranging from 500 to 1500 GeV, with the corresponding S mass in the range 200–500 GeV. The search selects events with two hadronically decaying τ-lepton candidates from H → τ+τ− decays and one or two light leptons (ℓ = e, μ) from S → VV (V = W, Z) decays while the remaining V boson decays hadronically or to neutrinos. A multivariate discriminant based on event kinematics is used to separate the signal from the background. No excess is observed beyond the expected SM background and 95% confidence level upper limits between 72 fb and 542 fb are derived on the cross-section σ(pp → X → SH) assuming the same SM-Higgs boson-like decay branching ratios for the S → VV decay. Upper limits on the visible cross-sections σ(pp → X → SH → WWττ) and σ(pp → X → SH → ZZττ) are also set in the ranges 3–26 fb and 6–33 fb, respectively.

Development of a predictive tool for sacral nerve modulation implantation in the treatment of non-obstructive urinary retention and/or slow urinary stream: a study from the Neuro-Urology Committee of the French Association of Urology.

This study aimed to seek predictive factors and develop a predictive tool for sacral nerve modulation (SNM) implantation in patients with non-obstructive urinary retention and/or slow urinary stream (NOUR/SS). This study was designed as a retrospective study including all patients who have undergone a two-stage SNM for NOUR/SS between 2000 and 2021 in 11 academic hospitals. The primary outcome was defined as the implantation rate. Secondary outcomes included changes in bladder emptying parameters. Univariate and multivariable logistic regression analysis were performed and determined odds ratio for IPG implantation to build a predictive tool. The performance of the multivariable model discrimination was evaluated using the c-statistics and an internal validation was performed using bootstrap resampling. Of the 357 patients included, 210 (58.8%) were finally implanted. After multivariable logistic regression, 4 predictive factors were found, including age (≤ 52 yo; OR = 3.31 CI95% [1.79; 6.14]), gender (female; OR = 2.62 CI95% [1.39; 4.92]), maximal urethral closure pressure (≥ 70 cmH2O; OR: 2.36 CI95% [1.17; 4.74]), and the absence of an underlying neurological disease affecting the lower motor neuron (OR = 2.25 CI95% [1.07; 4.76]). Combining these factors, we established 16 response profiles with distinct IPG implantation rates, ranging from 8.7 to 81.5%. Internal validation found a good discrimination value (c-statistic, 0.724; 95% CI 0.660-0.789) with a low optimism bias (0.013). This allowed us to develop a predictive tool ( https://predictivetool.wixsite.com/void ). The present study identified 4 predictive factors, allowing to develop a predictive tool for SNM implantation in NOUR/SS patients, that may help in guiding therapeutic decision-making. External validation of the tool is warranted.

Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines

The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (X, Y and Z) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds.

Measurement of inclusive and differential cross sections for single top quark production in association with a W boson in proton-proton collisions at sqrt{s} = 13 TeV

Measurements of the inclusive and normalised differential cross sections are presented for the production of single top quarks in association with a W boson in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data used were recorded with the CMS detector at the LHC during 2016–2018, and correspond to an integrated luminosity of 138 fb−1. Events containing one electron and one muon in the final state are analysed. For the inclusive measurement, a multivariate discriminant, exploiting the kinematic properties of the events is used to separate the signal from the dominant textrm{t}overline{textrm{t}} background. A cross section of 79.2pm 0.9{left(textrm{stat}right)}_{-8.0}^{+7.7}left(textrm{syst}right)pm 1.2left(textrm{lumi}right) pb is obtained, consistent with the predictions of the standard model. For the differential measurements, a fiducial region is defined according to the detector acceptance, and the requirement of exactly one jet coming from the fragmentation of a bottom quark. The resulting distributions are unfolded to particle level and agree with the predictions at next-to-leading order in perturbative quantum chromodynamics.

Search for flavour-changing neutral current interactions of the top quark and the Higgs boson in events with a pair of τ-leptons in pp collisions at sqrt{s} = 13 TeV with the ATLAS detector

A search for flavour-changing neutral current (FCNC) tqH interactions involving a top quark, another up-type quark (q = u, c), and a Standard Model (SM) Higgs boson decaying into a τ-lepton pair (H → τ+τ−) is presented. The search is based on a dataset of pp collisions at sqrt{s} = 13 TeV that corresponds to an integrated luminosity of 139 fb−1 recorded with the ATLAS detector at the Large Hadron Collider. Two processes are considered: single top quark FCNC production in association with a Higgs boson (pp → tH), and top quark pair production in which one of top quarks decays into Wb and the other decays into qH through the FCNC interactions. The search selects events with two hadronically decaying τ-lepton candidates (τhad) or at least one τhad with an additional lepton (e, μ), as well as multiple jets. Event kinematics is used to separate signal from the background through a multivariate discriminant. A slight excess of data is observed with a significance of 2.3σ above the expected SM background, and 95% CL upper limits on the t → qH branching ratios are derived. The observed (expected) 95% CL upper limits set on the t → cH and t → uH branching ratios are 9.4times {10}^{-4}left({4.8}_{-1.4}^{+2.2}times {10}^{-4}right) and 6.9times {10}^{-4}left({3.5}_{-1.0}^{+1.5}times {10}^{-4}right) , respectively. The corresponding combined observed (expected) upper limits on the dimension-6 operator Wilson coefficients in the effective tqH couplings are Ccϕ< 1.35 (0.97) and Cuϕ< 1.16 (0.82).

Observation of four-top-quark production in the multilepton final state with the ATLAS detector

This paper presents the observation of four-top-quark (tt¯tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\bar{t}t\\bar{t}$$\\end{document}) production in proton-proton collisions at the LHC. The analysis is performed using an integrated luminosity of 140 fb-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {fb}^{-1}$$\\end{document} at a centre-of-mass energy of 13 TeV collected using the ATLAS detector. Events containing two leptons with the same electric charge or at least three leptons (electrons or muons) are selected. Event kinematics are used to separate signal from background through a multivariate discriminant, and dedicated control regions are used to constrain the dominant backgrounds. The observed (expected) significance of the measured tt¯tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\bar{t}t\\bar{t}$$\\end{document} signal with respect to the standard model (SM) background-only hypothesis is 6.1 (4.3) standard deviations. The tt¯tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\bar{t}t\\bar{t}$$\\end{document} production cross section is measured to be 22.5-5.5+6.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$22.5^{+6.6}_{-5.5}$$\\end{document} fb, consistent with the SM prediction of 12.0±2.4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$12.0 \\pm 2.4$$\\end{document} fb within 1.8 standard deviations. Data are also used to set limits on the three-top-quark production cross section, being an irreducible background not measured previously, and to constrain the top-Higgs Yukawa coupling and effective field theory operator coefficients that affect tt¯tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t\\bar{t}t\\bar{t}$$\\end{document} production.

Determining the shelf life and quality changes of potatoes (Solanum tuberosum) during storage using electronic nose and machine learning.

The activities of alpha-amylase, beta-amylase, sucrose synthase, and invertase enzymes are under the influence of storage conditions and can affect the structure of starch, as well as the sugar content of potatoes, hence altering their quality. Storage in a warehouse is one of the most common and effective methods of storage to maintain the quality of potatoes after their harvest, while preserving their freshness and sweetness. Smart monitoring and evaluation of the quality of potatoes during the storage period could be an effective approach to improve their freshness. This study is aimed at assessing the changes in the potato quality by an electronic nose (e-nose) in terms of the sugar and carbohydrate contents. Three potato cultivars (Agria, Santé, and Sprite) were analyzed and their quality variations were separately assessed. Quality parameters (i.e. sugar and carbohydrate contents) were evaluated in six 15-day periods. The e-nose data were analyzed by means of chemometric methods, including principal component analysis (PCA), linear data analysis (LDA), support vector machine (SVM), and artificial neural network (ANN). Quadratic discriminant analysis (QDA) and multivariate discrimination analysis (MDA) offer the highest accuracy and sensitivity in the classification of data. The accuracy of all methods was higher than 90%. These results could be applied to present a new approach for the assessment of the quality of stored potatoes.

Mortality Risks after Two Years in Frail and Pre-Frail Older Adults Admitted to Hospital.

Frailty is characterized by a progressive decline in the physiological functions of multiple body systems that lead to a more vulnerable condition, which is prone to the development of various adverse events, such as falls, hospitalization, and mortality. This study aims to determine whether frailty increases mortality compared to pre-frailty and to identify variables associated with a higher risk of mortality. Two cohorts, frail and pre-frail subjects, are evaluated according to the Fried phenotype. A complete examination of frailty, cognitive status, comorbidities and pharmacology was carried out at hospital admission and was extracted through electronic health record (EHR). Mortality was evaluated from the EHR. Kaplan-Meier estimates of survival probability functions were calculated at two years censoring time for frail and pre-frail cohorts. The log-rank test assessed significant differences between survival probability functions. Significant variables for frailty (p < 0-05) were extracted by independent sample t-test. Further selection was based on variable significance found in multivariate logistic regression discrimination between frail and pre-frail subjects. Cox regression over univariate t-test-selected variables was calculated to identify variables associated with higher proportional hazard risks (HR) at two years. Frailty is associated with greater mortality at two years censoring time than pre-frailty (log-rank test, p < 0.0001). Variables with significant (p < 0.05) association with mortality identified in both cohorts (HR 95% (CI in the frail cohort) are male sex (0.44 (0.29-0.66)), age (1.05 (1.01-1.09)), weight (0.98 (0.96-1.00)), and use of proton-pump inhibitors (PPIs) (0.60 (0.41-0.87)). Specific high-risk factors in the frail cohort are readmission at 30 days (0.50 (0.33-0.74)), SPPB sit and stand (0.62 (0.45-0.85)), heart failure (0.67 (0.46-0.98)), use of antiplatelets (1.80 (1.19-2.71)), and quetiapine (0.31 (0.12-0.81)). Specific high-risk factors in the pre-frail cohort are Barthel's score (120 (7.7-1700)), Pfeiffer test (8.4; (2.3-31)), Mini Nutritional Assessment (MNA) (1200 (18-88,000)), constipation (0.025 (0.0027-0.24)), falls (18,000 (150-2,200,000)), deep venous thrombosis (8400 (19-3,700,000)), cerebrovascular disease (0.01 (0.00064-0.16)), diabetes (360 (3.4-39,000)), thyroid disease (0.00099 (0.000012-0.085)), and the use of PPIs (0.062 (0.0072-0.54)), Zolpidem (0.000014 (0.0000000021-0.092)), antidiabetics (0.00015 (0.00000042-0.051)), diuretics (0.0003 (0.000004-0.022)), and opiates (0.000069 (0.00000035-0.013)). Frailty is associated with higher mortality at two years than pre-frailty. Frailty is recognized as a systemic syndrome with many links to older-age comorbidities, which are also found in our study. Polypharmacy is strongly associated with frailty, and several commonly prescribed drugs are strongly associated with increased mortality. It must be considered that frail patients need coordinated attention where the diverse specialist taking care of them jointly examines the interactions between the diversity of treatments prescribed.

Emotional scene processing in biotypes of psychosis

Social-emotional deficits in psychosis may be indexed by deviations in emotional scene processing, but event-related potential (ERP) studies indicate such deviations may not map cleanly to diagnostic categories. Neurobiologically defined psychosis subgroups offer an alternative that may better capture neurophysiological correlates of social-emotional deficits. The current study investigates emotional scene-elicited ERPs in Biotypes of psychosis in a large (N = 622), well-characterized sample. Electroencephalography was recorded in healthy persons (N = 129), Biotype-1 (N = 195), Biotype-2 (N = 131), and Biotype-3 (N = 167) psychosis cases. ERPs were measured from posterior and centroparietal scalp locations. Neural responses to emotional scenes were compared between healthy and psychosis groups. Multivariate group discrimination analyses resulted in two composite variates that differentiated groups. The first variate displayed large differences between low-cognition (Biotype-1, Biotype-2) and intact-cognition groups (Biotype-3, healthy persons). The second indicated a small-to-moderate distinction of Biotypes-2 and -3 from Biotype-1 and healthy persons. Two multivariate correlations were identified indicating associations between 1) self-reported emotional experience and generalized cognition and 2) socio-occupational functioning and late-stage emotional processing. Psychosis Biotypes displayed emotional processing deficits not apparent in DSM psychosis subgroups. Future translational research may benefit from exploring emotional scene processing in such neurobiologically-defined psychosis groups.

Grandmothers residing with grandchildren: Social determinants of health, health behaviors, and health outcomes

The purpose of this study was to explore the associations between social determinants of health, health behaviors, and physical and mental health among African American and Hispanic caregiving grandmothers. We use cross-sectional secondary data from the Chicago Community Adult Health Study, originally designed to understand the health of individual households based on residential context. In a multivariate regression model, discrimination, parental stress, and physical health problems were significantly associated with depressive symptoms in caregiving grandmothers. Considering the multiple sources of stress experienced by this grandmother sample, researchers should develop and strengthen contextually relevant interventions for improving the health of caregiving grandmothers. Healthcare providers must be equipped with skills to address caregiving grandmothers’ unique needs related to stress. Finally, policy makers should promote the development of legislation that can positively influence caregiving grandmothers and their families. Expanding the lens through which caregiving grandmothers living in minoritized communities are viewed can catalyze meaningful change.

The Footprint of Type 1 Diabetes on Red Blood Cells: A Metabolomic and Lipidomic Study.

The prevalence of diabetes type 1 (T1D) in the world populations is continuously growing. Although treatment methods are improving, the diagnostic is still symptom-based and sometimes far after onset of the disease. In this context, the aim of the study was the search of new biomarkers of the disease in red blood cells (RBCs), until now unexplored. The metabolomic and the lipidomic profile of RBCs from T1D patients and matched healthy controls was determined by NMR spectroscopy, and different multivariate discrimination models were built to select the metabolites and lipids that change most significantly. Relevant metabolites were further confirmed by univariate statistical analysis. Robust separation in the metabolomic and lipidomic profiles of RBCs from patients and controls was confirmed by orthogonal projection on latent structure discriminant analysis (OPLS-DA), random forest analysis, and significance analysis of metabolites (SAM). The main changes were detected in the levels of amino acids, organic acids, creatine and phosphocreatine, lipid change length, and choline derivatives, demonstrating changes in glycolysis, BCAA metabolism, and phospholipid metabolism. Our study proves that robust differences exist in the metabolic and lipidomic profile of RBCs from T1D patients, in comparison with matched healthy individuals. Some changes were similar to alterations found already in RBCs of T2D patients, but others seemed to be specific for type 1 diabetes. Thus, many of the metabolic differences found could be biomarker candidates for an earlier diagnosis or monitoring of patients with T1D.