Variable Importance In Projection Research Articles

Rapid identification and differential markers of Curcumae Radix decoction pieces of different sources based on Heracles Neo ultra-fast gas phase electronic nose

Since Curcumae Radix decoction pieces have multiple sources, it is difficult to distinguish depending on traditional cha-racters, and the mixed use of multi-source Curcumae Radix will affect its clinical efficacy. Heracles Neo ultra-fast gas phase electronic nose was used in this study to quickly identify and analyze the odor components of 40 batches of Curcumae Radix samples from Sichuan, Zhejiang, and Guangxi. Based on the odor fingerprints established for Curcumae Radix decoction pieces of multiple sources, the odor components was identified and analyzed, and the chromatographic peaks were processed and analyzed to establish a rapid identification method. Principal component analysis(PCA), discriminant factor analysis(DFA), and soft independent modeling cluster analysis(SIMCA) were constructed for verification. At the same time, one-way analysis of variance(ANOVA) combined with variable importance in projection(VIP) was employed to screen out the odor components with P<0.05 and VIP>1, and 13 odor components such as β-caryophyllene and limonene were hypothesized as the odor differential markers of Curcumae Radix decoction pieces of diffe-rent sources. The results showed that Heracles Neo ultra-fast gas phase electronic nose can well analyze the odor characteristics and rapidly and accurately discriminate Curcumae Radix decoction pieces of different sources. It can be applied to the quality control(e.g., online detection) in the production of Curcumae Radix decoction pieces. This study provides a new method and idea for the rapid identification and quality control of Curcumae Radix decoction pieces.

Salivary microRNAs in pediatric eosinophilic esophagitis

Background: Diagnosis and management of eosinophilic esophagitis (EoE) occur via esophagogastroduodenoscopy with tissue biopsy. Objective: We sought to determine if salivary microribonucleic acid (miRNA) levels could differentiate children with EoE, serving as a noninvasive biomarker. Methods: Saliva was collected from children undergoing esophagogastroduodenoscopy (N = 291). miRNA analysis was conducted on 150 samples: EoE (n = 50), no pathologic alteration (n = 100). RNA was quantified with high throughput sequencing and aligned to build hg38 of the human genome using sequencing and alignment software. Quantile normalized levels of robustly expressed miRNAs (raw counts > 10 in 10% of samples) were compared across EoE and non-EoE groups with Wilcoxon rank sum testing. miRNA biomarker candidates were selected based on variable importance projection (VIP) scoring with partial least squared discriminant analysis (VIP > 1.5). Ability of these miRNAs to differentiate EoE status was assessed via logistic regression. Putative biologic targets for the miRNA candidates were determined in miRNA pathway analysis software. Results: Of the 56 salivary miRNAs reliably detected, miR-205-5p displayed the largest difference between EoE and non-EoE groups (V = 1623, adjusted p = 0.029). Six miRNAs (miR-26b-5p, miR-27b-3p, Let-7i-5p, miR-142-5p, miR-30a-5p, miR-205-5p) displayed elevated VIP scores (>1.5) and were able to differentiate EoE samples on logistic regression analysis with 70% sensitivity and 68% specificity. These six miRNAs demonstrated significant enrichment for gene targets involved in valine, leucine, and isoleucine biosynthesis (p = 0.0012), 2-oxycarboxylic acid metabolism (p = 0.043), and steroid hormone biosynthesis (p = 0.048). Conclusions: Salivary miRNAs represent a noninvasive, biologically relevant measure that may aid disease monitoring of EoE.

Application of 1H NMR metabolomics to French sparkling wines

Production and consumption of sparkling wines are constantly increasing and are confronted with substantial fraud problems. Last two decades have seen a significant increase in studies on wine metabolomics. In contrast, few metabolomics studies have focused on sparkling wines. A targeted approach based on 1H NMR metabolomics was applied to study profiles of commercial French sparkling wines from four protected designation of origin (PDO): Champagne, Crémant d’Alsace, Crémant de Bourgogne and Crémant de Bordeaux. The collected data were statistically processed by principal component analysis (PCA) and partial orthogonal least squares discriminant analysis (OPLS-DA). For origin and quality discrimination, significant discriminating metabolites were identified based on variable importance in projection (VIP) and correlation coefficient (p-(corr)) values. The results showed that 1H-NMR-based metabolomics combined with multivariate statistical analysis is an effective tool to discriminate commercial sparkling wines and sparkling wine quality. In addition, the targeted approach allowed identifying specific markers of each PDO.

Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer.

Multiple primary lung cancer (MPLC) is becoming increasingly common in clinical practice. Imaging examination is sometimes difficult to differentiate from intrapulmonary metastasis (IM) or single primary lung cancer (SPLC) before surgery. There is a lack of effective blood biomarkers as an auxiliary diagnostic method. A total of 179 patients who were hospitalized and operated in our department from January to June 2019 were collected, and they were divided into SPLC with 136 patients, MPLC with 24 patients, and IM with 19 patients. In total, 96 healthy people without lung cancer were enrolled. Medical history, imaging, and pathology data were assembled from all participants. Plasma metabolomics analysis was performed by quadrupole time-of-flight tandem mass spectrometry, and data were analyzed using SPSS19.0/Simca 14.1/MetaboAnalyst5.0 software. Significant metabolites were selected by variable importance in projection, P value, and fold change. The area under the receiver operating characteristic curve was used to evaluate their diagnostic ability. There were significant differences in plasma metabolite profiles between IM and MPLC. Seven metabolites were screened out. Two metabolites had higher levels in IM, and five metabolites had higher levels in MPLC. All had favorable discriminating capacity. Phosphatidyl ethanolamine (38:5) showed the highest sensitivity (0.95) and specificity (0.92). It was followed by l -histidine with sensitivity 0.92 and specificity 0.84. l -tyrosine can be used to identify SPLC and MPLC. The panel composed of related metabolites exhibited higher diagnostic ability. Eight principal metabolites caused remarkable differences between healthy people and MPLC, and five of them had area under the curves greater than 0.85, showing good discriminating power. Through the study of plasma metabolomics, it was found that there were obvious differences in the metabolite profiles of MPLC, IM, SPLC, and the healthy population. Some discovered metabolites possessed excellent diagnostic competence with high sensitivity and specificity. They had the potential to act as biomarkers for the screening and differential diagnosis of MPLCs.

Quantitative Analysis of the Cu Element Enhanced by AgNPs in a Single Microsized Suspended Particle Based on Optical Trapping-LIBS and Machine Learning.

Extremely severe and persistent particulate pollution caused by industrialization and urbanization impacts air quality, regional and global climates, and human health. The unstable and complex spectral signal of laser-induced breakdown spectroscopy (LIBS) with minimal feature information and interference signals considerably influences the accuracy of qualitative and quantitative analysis. In response to overcome this phenomenon, in this work, quantitative analysis of Cu element enhanced by silver nanoparticles (AgNPs) in a single microsized suspended particle was proposed herein using optical trapping-LIBS and machine learning method was proposed. Initially, the optimal AgNPs enhancement conditions were optimized. The LIBS spectra of 15 polluted black carbon samples were collected and various spectral pretreatment methods were compared to optimize the LIBS spectra. Variable selection methods include variable importance measurement (VIM), variable importance projection (VIP), VIM-successive projections algorithm (VIM-SPA), VIM-genetic algorithm (VIM-GA), and VIM-mutual information (VIM-MI). Finally, several hybrid variable selection methods were implemented in random forest (RF) calibration models. In particular, a wavelet transform (WT)-VIM-SPA-RF calibration model has constructed under the WT spectral pretreatment method and the selected and optimized input variables (VIM-SPA). Results elucidate that the WT-VIM-SPA-RF calibration model (R2P = 0.9858, MREP = 0.0396) have the best prediction performance than the WT-RF and Raw-RF models in predicting the Cu level in a single microsized black carbon particle. Compared to the WT-RF and Raw-RF models, MREP values decreased by 37% and 62%, respectively. The values of RSD, RPD, and RER of this calibration model are 2.8%, 8.39%, and 17.79%, respectively. The aforementioned results demonstrate that the WT-VIM-SPA-RF calibration model with accuracy, stability, and robustness is a promising approach for improving the quantitative accuracy of the Cu level in carbon black particles.

β-Cyclocitral-Mediated Metabolic Changes Optimize Growth and Defense Responses in Solanum lycopersicum L.

β-cyclocitral (βCC) is one of the significant oxidative products of β-carotene. It primes plants for multiple stress acclimation without compromising plant growth. Metabolic reorganization is necessary to maintain a balance between growth and defense. However, the βCC-mediated changes in a plant's metabolic network are unknown. Here, we demonstrate how βCC-induced metabolic changes enable Solanum lycopersicum L. (tomato) plants to promote defense and maintain growth under stress. An analysis of early (0-240 min) and late (72 h) changes in the tomato metabolome after βCC-treatment using liquid chromatography and tandem mass spectrometry identified 57 compounds. A principal coordinate analysis suggested that βCC treatment significantly changes the metabolite profile. A variable importance in projection (VIP) analysis revealed 16 and 19 discriminant metabolites from early and late samples, respectively (VIP ≥ 1.0). Upregulated metabolites were mainly amino acids and phytophenols. Pathway enrichment analysis showed that βCC treatment influenced amino acid metabolism at early and later times; however, phenylpropanoid and isoquinoline biosynthesis were influenced only at the later time. A 66.6% similarity in the upregulated metabolites of βCC- and simulated-herbivory-treated plants confirmed βCC's role against herbivores. We conclude that βCC steers a temporal separation in amino acids and defense metabolite accumulation that optimizes resource allocation to growth and defense.

Variation of Aroma Components of Pasteurized Yogurt with Different Process Combination before and after Aging by DHS/GC-O-MS

Pasteurized yogurt is a healthy yogurt that can be stored in ambient temperature conditions. Dynamic headspace sampling (DHS) combined with gas chromatography-olfactory mass spectrometry (GC-O-MS), sensory evaluation, electronic nose (E-nose), and partial least squares discriminant analysis (PLS-DA) were used to analyze the flavor changes of pasteurized yogurt with different process combinations before and after aging. The results of odor profiles showed that the sensory descriptors of fermented, sweet, and sour were greatly affected by different process combinations. The results of odor-active compounds and relative odor activity value (r-OAV) showed that the combination of the production process affected the overall odor profile of pasteurized yogurt, which was consistent with the sensory evaluation results. A total of 15 odor-active compounds of 38 volatile compounds were detected in pasteurized yogurt samples. r-OAV results revealed that hexanal, (E)-2-octenal, 2-heptanone, and butanoic acid may be important odor-active compounds responsible for off-odor in aged, pasteurized yogurt samples. PLS-DA and variable importance of projection (VIP) results showed that butanoic acid, hexanal, acetoin, decanoic acid, 1-pentanol, 1-nonanal, and hexanoic acid were differential compounds that distinguish pasteurized yogurt before and after aging.

Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients.

Chronic kidney disease (CKD) is the end point of a number of systemic chronic diseases. The prevalence of CKD is increasing worldwide and recent epidemiological studies are showing the high prevalence of renal failure in CKD patients using complementary and alternative medicines (CAMs). Clinicians believe that biochemical profiles of CKD patients using CAM (referred here to as CAM-CKD) may be different compared to those on standard clinical treatment and should be managed differently. The present study aims to explore the potential of the NMR-based metabolomics approach to reveal the serum metabolic disparity between CKD and CAM-CKD patients with respect to normal control (NC) subjects and if the differential metabolic patterns can provide rationale for the efficacy and safety of standard and/or alternative therapies. Serum samples were obtained from 30 CKD patients, 43 CAM-CKD patients, and 47 NC subjects. The quantitative serum metabolic profiles were measured using 1D 1H CPMG NMR experiments performed at 800 MHz NMR spectrometer. The serum metabolic profiles were compared using various multivariate statistical analysis tools available on MetaboAnalyst (freely available web-based software) such as partial least-squares discriminant analysis (PLS-DA) and random forest (a machine learning) classification method. The discriminatory metabolites were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (i.e., p < 0.05) using either Student t-test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of Q 2 and R 2. Compared to CAM-CKD patients, the sera of CKD patients were characterized by (a) elevated levels of urea, creatinine, citrate, glucose, glycerol, and phenylalanine and phenylalanine-to-tyrosine ratio (PTR) and (b) decreased levels of various amino acids (such leucine, isoleucine, valine, and alanine), high-density lipoproteins, lactate, and acetate. These changes suggested that CKD patients manifest severe oxidative stress, hyperglycemia (with dampened glycolysis), increased protein energy wasting, and reduced lipid/membrane metabolism. Statistically significant and strong positive correlation of PTR with serum creatinine levels suggested the role of oxidative stress in the progression of kidney disease. Significant differences in metabolic patterns between CKD and CAM-CKD patients were observed. With respect to NC subjects, the serum metabolic changes were more aberrant in CKD patients compared to CAM-CKD patients. The aberrant metabolic changes in CKD patients with manifestations of higher oxidative stress compared to CAM-CKD patients could explain clinical discrepancies between CKD and CAM-CKD patients and further advocate the use of different treatment strategies for CKD and CAM-CKD patients.

Coupling of NIR Spectroscopy and Chemometrics for the Quantification of Dexamethasone in Pharmaceutical Formulations.

Counterfeit or substandard drugs are pharmaceutical formulations in which the active pharmaceutical ingredients (APIs) have been replaced or ingredients do not comply with the drug leaflet. With the outbreak of the COVID-19 pandemic, fraud associated with the preparation of substandard or counterfeit drugs is expected to grow, undermining health systems already weakened by the state of emergency. Analytical chemistry plays a key role in tackling this problem, and in implementing strategies that permit the recognition of uncompliant drugs. In light of this, the present work represents a feasibility study for the development of a NIR-based tool for the quantification of dexamethasone in mixtures of excipients (starch and lactose). Two different regression strategies were tested. The first, based on the coupling of NIR spectra and Partial Least Squares (PLS) provided good results (root mean square error in prediction (RMSEP) of 720 mg/kg), but the most accurate was the second, a strategy exploiting sequential preprocessing through orthogonalization (SPORT), which led (on the external set of mixtures) to an R2pred of 0.9044, and an RMSEP of 450 mg/kg. Eventually, Variable Importance in Projection (VIP) was applied to interpret the obtained results and determine which spectral regions contribute most to the SPORT model.

Flavoromics Approach in Critical Aroma Compounds Exploration of Peach: Correlation to Origin Based on OAV Combined with Chemometrics

It is essential to seek the critical aroma compounds to identify the origins of peach as well as provide a guidance for quality evaluation. In this study, the peach was characterized by HS-SPME/GC-MS. Subsequently, the odor activity value (OAV) was calculated to specify the primary aroma-active compounds. Afterwards, the chemometrics methods were employed to explore the potentially critical aroma on the basis of p value, fold change (FC), S-plot, jack-knifing confidence interval, variable importance for projection (VIP), and the Shared and Unique Structures (SUS) plots. As a result, five compounds (methyl acetate, (E)-hex-2-enal, benzaldehyde, [(Z)-hex-3-enyl] acetate, and 5-ethyloxolan-2-one) were considered as critical aromas. Moreover, the multi-classification model was developed with an outstanding performance (accuracy of 100%) using the five critical aroma. Moreover, the potential chemical basis of odors was sought through sensory evaluation. In addition, this study provides the theoretical and practical foundation for geographical origin traceability and quality evaluation.

Variable importance for projection (VIP) scores for analyzing the contribution of risk factors in severe adverse events to Xiyanping injection

BackgroundAge and herb-drug combination are risk factors for the severity of Xiyanping injection (XYP) associated adverse events (AEs).ObjectiveTo analyze risk factors contributing to the severity of XYP’s AEs using a variable importance for projection (VIP) method.MethodsAEs related to the use of XYP were extracted from the China National Adverse Drug Reaction Monitoring Information System (2004–2017) and classified as general or severe. Data were analyzed with respect to age and 12 herb-drug combinations, including ribavirin (RB), ceftriaxone, penicillin sodium, ambroxol hydrochloride (AH), clindamycin, cefoxitin sodium, azithromycin (AZM), ceftazidime, amoxicillin sodium/potassium clavulanate, levofloxacin hydrochloride, sodium cefazolin pentahydrate, and acyclovir according to VIP scores and correlation coefficient (Coeff).ResultsA total of 21,714 AEs (general 20,660; severe 1054) related to XYP combinations were included. Using XYP alone tended to produce general AEs (All VIP = 3.124; 1.329; 1.857; 2.169; 2.400, Coeff < 0). For all set, 0–6 years old patients tend to have general AEs (VIP = 2.425, Coeff < 0), while those > 41 years old patients tend to have severe AEs (VIP = 1.180; 2.323, Coeff > 0). For 0–40 years old patients, XYP-RB combination had a greater impact on the severity of AEs (VIP = 1.158; 1.360; 1.147, Coeff > 0). For 7–17 years old patients, XYP-AZM combination tended to produce general AEs (VIP = 1.502, Coeff < 0). In individuals > 65 years old, XYP-AH combination tended to result in severe AEs (VIP = 1.232, Coeff > 0).ConclusionsVIP method was expected to effectively analyze risk factors in affecting the severity of AEs and control AEs more effectively. Age is the key factor contributing to the severity of AEs, and there are different influence directions. It is recommended that clinicians pay closer attention to the metabolic characteristics of different age groups. It is safe to use XYP alone and strictly implementing standardized operations such as medication interval and flushing will avoid undesired AEs.

An integrated strategy for thegeographical origin traceability of Goji berries by antioxidants characteristic fingerprint based online ultra-performance liquid chromatography-2,2-diphenyl-1-picrylhydrazyl- photodiode array detector-mass spectrometry combined with multivariate statistics analysis.

Goji berries are now becoming increasingly popular in the human diet due to their potential health benefits. Unscrupulous traders deliberately mislabel with certain origins to gain illegal profits, which seriously affected the consumers' benefits. In this study, an online ultra-performance liquid chromatography-2,2-diphenyl-1 -picrylhydrazyl-photodiode array detector-electrospray ionization-quadrupole time of flight mass was developed for rapid screening and identification of the antioxidants from Goji berry; then, the antioxidants characteristic fingerprint was established and explored in the origins discrimination of Goji berries from China combined with multivariate statistics analysis. As a result, twenty-eight compounds were screened from Goji berry extract, 19 of which were identified by accurate molecular and ultraviolet information according to references. Principal components analysis and partial least squares discrimination analysis achieved the accurate classification from the four regions, eight compounds were selected as origin-related antioxidant markers with variable importance in projection >1 and one-way analysis of variance (P<0.05), including rutin, rutin di-hexose, P-coumaric acid tri-hexose, dicaffeoylquinic acid isomer, Quercetin-rhamno-di-hexoside, peak14, peak16, and peak27. This study provides a feasible strategy for the geographical origins discrimination of Goji berries based on antioxidant ingredients difference and will be helpful for improving the quality control level of Goji berries.

An In Vitro Catalysis of Tea Polyphenols by Polyphenol Oxidase.

Tea polyphenol (TPs) oxidation caused by polyphenol oxidase (PPO) in manufacturing is responsible for the sensory characteristics and health function of fermented tea, therefore, this subject is rich in scientific and commercial interests. In this work, an in vitro catalysis of TPs in liquid nitrogen grinding of sun-dried green tea leaves by PPO was developed, and the changes in metabolites were analyzed by metabolomics. A total of 441 metabolites were identified in the catalyzed tea powder and control check samples, which were classified into 11 classes, including flavonoids (125 metabolites), phenolic acids (67 metabolites), and lipids (55 metabolites). The relative levels of 28 metabolites after catalysis were decreased significantly (variable importance in projection (VIP) > 1.0, p < 0.05, and fold change (FC) < 0.5)), while the relative levels of 45 metabolites, including theaflavin, theaflavin-3'-gallate, theaflavin-3-gallate, and theaflavin 3,3'-digallate were increased significantly (VIP > 1.0, p < 0.05, and FC > 2). The increase in theaflavins was associated with the polymerization of catechins catalyzed by PPO. This work provided an in vitro method for the study of the catalysis of enzymes in tea leaves.

Integrative analysis of sensory evaluation and non-targeted metabolomics to unravel tobacco leaf metabolites associated with sensory quality of heated tobacco.

Heated tobacco (Nicotiana tabacum L.) products are heating tobacco plug at a temperature of 350°C and produce different emissions in aerosol and sensory perceptions of tobacco leaf compared with combustible tobacco. Previous study assessed different tobacco varieties in heated tobacco for sensory quality and analyzed the links between sensory scores of the final products and certain chemical classes in tobacco leaf. However, contribution of individual metabolites to sensory quality of heated tobacco remains largely open for investigation. In present study, five tobacco varieties were evaluated as heated tobacco for sensory quality by an expert panel and the volatile and non-volatile metabolites were analyzed by non-targeted metabolomics profiling. The five tobacco varieties had distinct sensory qualities and can be classified into higher and lower sensory rating classes. Principle component analysis and hierarchical cluster analysis showed that leaf volatile and non-volatile metabolome annotated were grouped and clustered by sensory ratings of heated tobacco. Orthogonal projections to latent structures discriminant analysis followed by variable importance in projection and fold-change analysis revealed 13 volatiles and 345 non-volatiles able to discriminate the tobacco varieties with higher and lower sensory ratings. Some compounds such as β-damascenone, scopoletin, chlorogenic acids, neochlorogenic acids, and flavonol glycosyl derivatives had strong contribution to the prediction of sensory quality of heated tobacco. Several lyso-phosphatidylcholine and lyso-phosphatidylethanolamine lipid species, and reducing and non-reducing sugar molecules were also positively related to sensory quality. Taken together, these discriminating volatile and non-volatile metabolites support the role of leaf metabolites in affecting the sensory quality of heated tobacco and provide new information on the types of leaf metabolites that can be used to predict applicability of tobacco varieties for heated tobacco products.

Monitoring black tea fermentation quality by intelligent sensors: Comparison of image, e-nose and data fusion

To scientifically and objectively monitor the fermentation quality of black tea, a computer vision system (CVS) and electronic nose (e-nose) were employed to analyze the black tea image and odor eigenvalues of Yinghong No. 9 black tea. First, the variation trends of tea polyphenols, volatile substances, image eigenvalues and odor eigenvalues with the extension of fermentation time were analyzed, and the fermentation process was categorized into three stages for classification. Second, principal component analysis (PCA) was employed on the image and odor eigenvalues obtained by CVS and e-nose. Partial least squares discriminant analysis (PLS-DA) was performed on 117 volatile components, and 51 differential volatiles were screened out based on variable importance in projection (VIP ≥1) and one-way analysis of variance (P < 0.05), including geraniol, linalool, nerolidol, and α-ionone. Then, image features and odor features are fused by using a data fusion strategy. Finally, the image, smell and fusion information were combined with random forest (RF), K-nearest neighbor (KNN) and support vector machine (SVM) to establish the classification models of different fermentation stages and to compare them. The results show that the feature-level fusion strategy integrating the SVM was the most efficient approach, with classification accuracy rates of 100% for the training sets and 95.6% for the testing sets. The performance of Support Vector Regression (SVR) prediction models for tea polyphenol content based on feature-level fusion data outperformed data-level models (Rc, RMSEC, Rp and RMSEP of 0.96, 0.48 mg/g, 0.94, 0.6 mg/g).

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm.

Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg-1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg-1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Distant heteroploid hybridization improved Hedychium floral scent, floral color and morphologcal traits

Heteroploid hybridization is an important breeding technique for producing heteropolyploids with superior traits. Hedychium is widely used in medicine and as an ornamental plant; however, the fact that no current varieties exhibit both fragrance and colorful flowers causes a bottleneck in the genetic improvement of Hedychium. In the current study, diploid Hedychium coronarium 'ZS' (with strong fragrance and white flowers) and tetraploid Hedychium coccineum 'KMH' (scentless with red flowers) were used for the reciprocal heteroploid hybridization, and the 34 flowering hybrid offspring were analyzed for ploidy and hybrid authenticity, phenotype and floral scent. In 'ZS' ♀ × 'KMH' ♂, eight hybrids were triploids, while there were nine triploids and seventeen tetraploids in 'KMH' ♀ × 'ZS'♂. Furthermore, all hybrids were true hybrids as determined by simple sequence repeat (SSR) markers, and the presence of unreduced pollen in 'ZS' flowers was confirmed. Compared with the parents, 15 morphological traits and four flower color traits of hybrid offspring were widely separated. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) showed that the parents and 34 hybrids could be divided into three groups mainly depending on organ size and color intensity. Headspace solid phase microextraction–gas chromatography–mass spectrometry (HS–SPME–GC–MS) identified 16 volatile organic compounds (VOCs), all of which were widely separated in the hybrid offspring. The HCA and PCA results confirmed that the parents and 34 hybrids could be classified into three groups mainly based on linalool, (E)-β-ocimene, eucalyptol, and methyl benzoate. Partial least squares discriminant analysis (PLS-DA) revealed that morphology, floral color and floral scent differed significantly between triploids and tetraploids, and the variable importance in projection (VIP) of 14 indexes was greater than 1. Finally, this study screened four new allopolyploid germplasms (HB30, HB46, HB54 and HB61) with colorful flowers and rich floral scent. These findings suggest a new strategy for cultivating new varieties with strong fragrance and bright colors.

Identification of Eco-Climatic Factors Driving Yields and Genotype by Environment Interactions for Yield in Early Maturity Soybean Using Crop Simulation

Deploying crops in regions bordering their initial distribution area requires adapting existing cultivars to particular environmental constraints. In this study, we revealed the main Eco-climatic Factors (EFs)—climatic factors recorded over specific phenological periods—impacting both yields and Genotype by Environment Interactions (GEI) for yield in early maturity soybeans (Glycine max (L.) Merrill) under high latitudes. A multi-year (2017–2021) and multi-environment (n = 112) database was built based on the official post-inscription French soybean trial network “SOJA Terres Inovia-GEVES-Partenaires”. Yields of 57 cultivars covering MG00 and MG000 maturity groups were considered. For each environment, 126 EFs were calculated using a Crop Growth Model (CGM) based on observed weather data and simulated developmental stages. Partial Least Square (PLS) regression analyses using the Variable Importance in Projection (VIP) score were used to sort out the most relevant EFs for their impact on yield levels on the one side and on GEI for yield on the other side. Our results confirmed that yield levels for both maturity groups were greatly influenced by climatic factors from the seed filling phenophases, mainly End of Pod to Physiological Maturity. The cumulative potential evapotranspiration during the End of Pod to Physiological Maturity period was the main EF affecting yield levels positively for both maturity groups (VIP = 2.86; R2 = 0.64). Interestingly, EFs explaining yield levels strongly differed from those explaining GEI, in terms of both climatic factors and phenophases. GEI were mostly influenced by climatic factors from First Flower to End of Pod; these factors were maximum temperatures and solar radiation intensity. Cold stress from Sowing to First Seed also appeared to be a critical driver for GEI in MG00 soybeans. The contrasted responses of several cultivars to the main GEI-drivers highlighted a potential genetic variability that could be exploited in early maturity soybean breeding. This study revealed the complexity of GEI ecophysiology, and our results should help breeding strategies to deliver germplasm that outperforms the existing genetic material for expanding the crop to northern European regions.

Sarcopenia: investigation of metabolic changes and its associated mechanisms

BackgroundSarcopenia is one of the most predominant musculoskeletal diseases of the elderly, defined as age-related progressive and generalized loss of muscle mass with a simultaneous reduction in muscle strength and/or function. Using metabolomics, we aimed to examine the association between sarcopenia and the plasma metabolic profile of sarcopenic patients, measured using a targeted HPLC-MS/MS platform.MethodsPlasma samples from 22 (17 men) hip fracture patients undergoing surgery (8 sarcopenic, age 81.4+6.3, and 14 non-sarcopenic, age 78.4±8.1) were analyzed. T test, fold change, orthogonal partial least squares discriminant analysis, and sparse partial least squares discriminant analysis were used for mining significant features. Metabolite set enrichment analysis and mediation analysis by PLSSEM were thereafter performed.ResultsUsing a univariate analysis for sarcopenia z score, the amino acid citrulline was the only metabolite with a significant group difference after FDR correction. Positive trends were observed between the sarcopenia z score and very long-chain fatty acids as well as dicarboxylic acid carnitines. Multivariate analysis showed citrulline, non-esterified fatty acid 26:2, and decanedioyl carnitine as the top three metabolites according to the variable importance in projection using oPLS-DA and loadings weight by sPLS-DA. Metabolite set enrichment analysis showed carnitine palmitoyltransferase deficiency (II) as the highest condition related to the metabolome.ConclusionsWe observed a difference in the plasma metabolic profile in association with different measures of sarcopenia, which identifies very long-chain fatty acids, Carn.DC and citrulline as key variables associated with the disease severity. These findings point to a potential link between sarcopenia and mitochondrial dysfunction and portraits a number of possible biochemical pathways which might be involved in the disease pathogenesis.

Characterization of empty cup aroma in Soy sauce aroma type Baijiu by vacuum assisted sorbent extraction

The empty cup aroma is a typical feature of soy sauce aroma type baijiu (SSB). Herein, a novel method based on vacuum-assisted sorbent extraction (VASE) combined with gas chromatography–mass spectrometry (GC–MS) was established for research on volatile compounds in empty cup of SSB. A total of 81 compounds were identified in the empty cup aroma of 8 SSB samples, involving 27 common compounds, and lactic acid was found to have the highest content. High-performance liquid chromatography (HPLC) combined with a chiral column was used to accurately conduct quantitative analysis of L-lactic acid and D-lactic acid, and it was found that the contents of D-lactic acid in all of the 8 SSB empty cup were higher than L-lactic acid. A total of 8 different SSB samples could be distinguished by principal component analysis (PCA) and heatmap analysis, and 7 compounds including L-lactic acid, D-lactic acid, ethyl hexadecanoate, benzeneacetic acid, acetic acid, 1,2-propanediol, hexanoic acid were selected as potential markers of empty cup aroma due to their high variable importance in projection (VIP) values (VIP >1.0).