Variable Importance In Projection Research Articles

Explorative Identification of Anatomical Parameters Associated with Successful Pessary Fitting in Pelvic Organ Prolapse Using Dynamic Magnetic Resonance Imaging.

Background: Pelvic organ prolapse (POP) affects many women and is often managed with pessary treatment, yet predicting the success of fitting remains challenging. This study aims to identify anatomical parameters associated with successful and unsuccessful pessary treatment using dynamic magnetic resonance imaging (dMRI). Methods: A cross-sectional study in Maastricht University Medical Centre (MUMC+), the Netherlands. Sixteen women with a cystocele and/or descensus uteri minimal POP-Q stage 2, using pessary treatment, were included. All women underwent a dynamic MRI of the pelvic floor at rest, during contraction and on Valsalva. The anatomical parameters evaluated included various lengths and angles. The association between the anatomical parameters and pessary fitted is assessed using partial least squares regression. The predictive accuracy was tested using cross-validation based on the partial least squares model with the most important variables. Results: Seven of the sixteen women (43.8%) were in the non-fitting group (due to movement, rotation or expulsion of the pessary), and nine women (56.3%) were in the fitting group. Participants in the non-fitting group had a significantly lower body mass index (BMI). Variables such as total vaginal length (TVL) and certain angles were highly predictive of pessary fitting success, with variable importance of projection (VIP) scores indicating their importance. The prediction models showed accuracies ranging from 53.3% to 80.0%. Conclusions: In this explorative study, TVL, cervical length (CL), sacrococcygeal angle and pubococcygeal angle were key variables associated with pessary fitting success. These findings offer valuable insights for optimizing pessary fitting procedures and the development of new pessaries.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics.

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

Metabolomic Analysis of Follicular Fluid in Normal-Weight Patients with Polycystic Ovary Syndrome.

This study aimed to examine the differential variations in the metabolic composition of follicular fluid (FF) among normal-weight patients with polycystic ovary syndrome (PCOS) and controls and to identify potential biomarkers that may offer insights into the early identification and management of these patients. We collected FF samples from 45 normal-weight women with PCOS and 36 normal-weight controls without PCOS who were undergoing in vitro fertilization-embryo transfer. An untargeted metabolomic study of collected FF from infertile women was performed using high-performance liquid chromatography-tandem spectrometry (LC-MS). The tendency of the two groups to separate was demonstrated through multivariate analysis. Univariate analysis and variable importance in projection were used to screen out differential metabolites. Metabolic pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a diagnostic model was established using the random forest algorithm. The metabolomics analysis revealed an increase in the expression of 23 metabolites and a decrease in that of 10 metabolites in the FF of normal-weight women with PCOS. According to the KEGG pathway analysis, these differential metabolites primarily participated in the metabolism of glycerophospholipids and the biosynthesis of steroid hormones. Based on the biomarker combination of the top 10 metabolites, the area under the curve value was 0.805. The concentrations of prostaglandin E2 in the FF of individuals with PCOS exhibited an inverse association with the proportion of high-quality embryos (p < 0.05). Our research identified a distinct metabolic profile of the FF from normal-weight women with PCOS. The results offer a broader comprehension of the pathogenesis and advancement of PCOS, and the detected differential metabolites could be potential biomarkers and targets for the treatment of PCOS.

Smart classification of organic and inorganic pineapple juice using dual NIR spectrometers combined with chemometric techniques

The global demand for organic foods, driven by health benefits and consumer preferences, necessitates reliable methods for distinguishing organic products from their inorganic counterparts. This study investigates the application of dual handheld near infrared (NIR) spectroscopy devices, SCiO and Tellspec, combined with chemometric techniques for the nondestructive differentiation of organic and inorganic pineapple juices. The objective was to establish a rapid and robust method to differentiate organic pineapple juice from inorganic juice using unique spectral data from the two devices. Eighty-four pineapple juice samples were analyzed with preprocessing techniques, including mean centering, multiplicative scatter correction, standard normal variate, first derivative, and second derivative applied to the spectral data. Partial least squares discriminant analysis (PLS-DA) was employed for classification, and variable importance in projection (VIP) was used for optimal wavelength selection. The results demonstrated that the Tellspec scanner, particularly with second derivative preprocessing, achieved high accuracy in differentiating organic from inorganic pineapple juice. The fusion of data from both SCiO (740–1070 nm) and Tellspec (900–1700 nm) scanners, without preprocessing, coupled with the PLS-DA model, achieved perfect classification accuracy, sensitivity, and specificity (100 %) in both training and testing sets. This study highlights the potential of integrating dual handheld NIR spectroscopy with chemometrics to effectively and accurately classify organic and inorganic pineapple juices. The findings support using these advanced techniques for quality assurance and authentication in the food industry.

Identification of the Metabolic Signature of Aging Retina.

This study aims to explore the metabolic signature of aging retina and identify the potential metabolic biomarkers for the diagnosis of retinal aging. Retinal samples were collected from both young (two months) and aging (14 months) mice to conduct an unbiased metabolic profiling. Liquid chromatography-tandem mass spectrometry analysis was conducted to screen for the metabolic biomarkers and altered signaling pathways associated with retinal aging. We identified 166 metabolites differentially expressed between young and aged retinas using a threshold of orthogonal projection to latent structures discriminant analysis variable importance in projection >1 and P < 0.05. These metabolites were significantly enriched in several metabolic pathways, including purine metabolism, citrate cycle, phenylalanine, tyrosine and tryptophan biosynthesis, glycerophospholipid metabolism, and alanine, aspartate and glutamate metabolism. Among these significantly enriched pathways, glycerophospholipid metabolites emerged as promising candidates for retinal aging biomarkers. We assessed the potential of these metabolites as biomarkers through an analysis of their sensitivity and specificity, determined by the area under the receiver-operating characteristic (ROC) curves. Notably, the metabolites like PC (15:0/22:6), PC (17:0/14:1), LPC (P-16:0), PE (16:0/20:4), and PS (17:0/16:1) demonstrated superior performance in sensitivity, specificity, and accuracy in predicting retinal aging. This study sheds light on the molecular mechanisms underlying retinal aging by identifying distinct metabolic profiles and pathways. These findings provide a valuable foundation for developing future clinical applications in diagnosing, identifying, and treating age-related retinal degeneration. This study sheds light on novel metabolic profiles and biomarkers in aging retinas, potentially paving the way for targeted interventions in preventing, diagnosing, and treating age-related retinal degeneration and other retinal diseases.

Screening for Early Biomarkers of Cisplatin-Induced Acute Kidney Injury in Rats Through Serum Metabolomics Technology.

To systematically identify early biomarkers of cisplatin-induced acute kidney injury (AKI) in rats. An experimental study. Place and Duration of the Study: Experimental Animal Laboratory of Lanzhou University, Gansu, China, and the Department of Pharmacy, The First Hospital of Lanzhou University, Gansu, China, from July 2022 to October 2023. In this study, an AKI model was established by continuously injecting cisplatin into rats at a dose of 1 mg/kg once a day for control group and for 2, 3, 4, and 5 days to other four groups, respectively. Subsequently, rat plasma samples were collected for metabolomics analysis to identify early differentiated metabolites in the plasma prior to creatinine elevation. Furthermore, accurate HPLC-MS/MS methods were developed to validate the biomarker variation in other AKI models. The occurrence of time-dependent renal cortical injury and significant alterations of creatinine (Cr) concentration were observed on day-4 and 5, which demonstrated successful model construction. Sixty-six compounds changed on Day-2 while 61 compounds changed on Day-3. Eleven compounds with variable importance in projection (VIP) >1.5 and false discover rate (FDR) <0.2 were selected and identified by HPLC-MS/MS. Among these, N-acetylglutamine and citramalic acid changed earlier than serum creatinine (sCr) in the AKI model. N-acetylglutamine and citramalic acid may serve as early biomarker of cisplatin-induced AKI. Acute kidney injury, Biomarker, Cisplatin, Metabolomics, LC-MS/MS, Rats.

Predicting intraocular lens tilt using a machine learning concept.

To use a combination of partial least squares regression and a machine learning approach to predict intraocular lens (IOL) tilt using preoperative biometry data. Kepler University Clinic Linz, Linz, Austria. Prospective single-center study. Optical coherence tomography, autorefraction, and subjective refraction were performed at baseline and 8 weeks after cataract surgery. In analysis I, only 1 eye per patient was included and a tilt prediction model was generated. In analysis II, a pairwise comparison between right and left eyes was performed. In analysis I, 50 eyes of 50 patients were analyzed. Difference in amount, orientation, and vector from preoperative to postoperative lens tilt was -0.13 degrees, 2.14 degrees, and 1.20 degrees, respectively. A high predictive power (variable importance for projection [VIP]) for postoperative tilt prediction was found for preoperative tilt (VIP = 2.2), pupil decentration (VIP = 1.5), lens thickness (VIP = 1.1), axial eye length (VIP = 0.9), and preoperative lens decentration (VIP = 0.8). These variables were applied to a machine learning algorithm resulting in an out of bag score of 0.92 degrees. In analysis II, 76 eyes of 38 patients were included. The difference of preoperative to postoperative IOL tilt of right and left eyes of the same individual was statistically relevant. Postoperative IOL tilt showed excellent predictability using preoperative biometry data and a combination of partial least squares regression and a machine learning algorithm. Preoperative lens tilt, pupil decentration, lens thickness, axial eye length, and preoperative lens decentration were found to be the most relevant parameters for this prediction model.

1H NMR Profiling of Honey Bee Brains across Varying Ages and Seasons.

Honey bees (Apis mellifera) provide a useful model for studying aging because of the differences in longevity between the relatively short-lived summer and long-lived winter bees, as well as bees lacking signs of cognitive senescence as they age. Bee brains were dissected from newly emerged, 14-day-, and 28-day- old bees in mid- and late summer, as well as brood nest bees in fall, winter, and spring, before, during, and after overwintering, respectively. Brains were examined with nuclear magnetic resonance (NMR) spectroscopy to analyze their metabolome. Nine variable importance in projection (VIP) variables were identified, primarily amino acids and choline derivatives. Differences in metabolite concentrations were found with different ages of summer bees, mostly between newly emerged and 14-day- old bees, such as a decrease in phenylalanine and an increase in β-alanine, but there were also changes in older adults, such as o-phosphocholine that declined in 28-day- old bees. Differences in brood nest bees were observed, including a decline in tryptophan and an increase in β-alanine. These may provide distinct metabolomic signatures with age and season. Such research holds promise for a better understanding of the complex interplays between bee physiology, development, and aging, which has implications for improving bee health and management.

Chemical analysis of different parts from agarwood columns by artificially agarwood-inducing method based on GC–MS and UPLC-TOF-MS

Agarwood is resin-containing wood produced by plants that have been injured. It is widely used in herbal medicine, incense, decorative items, and so on. In this study, we conducted resin area statistical analysis, determined starch particle and reducing sugar contents, and performed multivariate statistical analysis of chemical composition by GC–MS and UPLC-Q-TOF-MS to explore the different components in sections cut from an agarwood column, designated as A1–A4. The results showed that after stimulation by Agar-Bit inducer, the internal phloem parenchyma cells of the column started to form agarwood, and then starch granules were converted into soluble reducing sugars and agarwood resin. Section A1 showed rapid loss of starch granules, resulting in higher contents of reducing sugars and resin. The resin areas of agarwood in the respective sections were different, gradually decreasing on going from A1 to A4. Total numbers of metabolites of 87 and 63 were identified by GC–MS and UPLC-Q-TOF-MS, respectively. Of these, 10 and 16 metabolites with significant differences (variable importance projection >1) were selected through multivariate statistical analysis. These metabolites included chromones, sesquiterpenes, alkanes, and fatty acids. Among them, 6-methoxy-2-(2-phenylethyl)chromone and 6,7-dimethoxy-2-(2-phenylethyl)chromone were significant markers detected by both GC–MS and UPLC-Q-TOF-MS, which may be essential substances responsible for differences in the agarwood-forming capacities of the cut sections. In conclusion, there has been limited research on the different agarwood-forming capacities of agarwood columns. Here, we explored the differences in various sections of agarwood through chemical analysis to provide a more comprehensive and in-depth understanding of its constitution.

The inversion of arid-coastal cultivated soil salinity using explainable machine learning and Sentinel-2

The escalating salinization of cultivated soil poses a significant threat to the ecological environment. It is imperative to establish a monitoring system and mitigate the spread of salinization in arid and coastal areas through remote sensing, incorporating high-precision cross-regional models for soil salt content inversion. This study focuses on typical saline-alkali soils in arid and coastal regions of China. Using Sentinel 2 data (including 6 bands and 27 spectral indices), along with soil texture, moisture content, temperature, precipitation, and digital elevation model (DEM) data to establish an arid-coastal salinity inversion model. Variable selection methods such as pearson correlation coefficient (PCC), variable importance in projection (VIP), gray relational analysis (GRA), and gradient boosting machine (GBM) were used, while using 9 models including adaptive boosting (Adaboost), extremely randomized trees (ERT), and light gradient boosting machine (LightGBM). The best model was further elucidated using the Shapley additive explanations method. Results indicate that the common sensitive characteristic variables of arid-coastal areas were spectral indices and soil properties in PCC, the spectral variable bands and indices in VIP, and all variables in GRA and GBM. The best inversion model GBM-ERT (R2 = 0.91, RMSE = 1.06) in arid areas exhibited higher accuracy than the best inversion model GBM-Adaboost (R2 = 0.77, RMSE = 1.74) in coastal areas. The arid-coastal inversion model PCC-LightGBM demonstrated the best inversion performance (R2 = 0.64, RMSE = 2.29) and simulation performance in arid (R2 = 0.67) and coastal areas (R2 = 0.63). Dead fuel index (DFI) had the most significant impact on model prediction (0.89) and the second ratio index (RI2) contributed the highest relative importance (18 %) to the model. Our analysis indicates that the arid-coastal model of PCC-LightGBM established using common characteristic variables, can effectively monitor large-scale soil salinity.

Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates. This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health. A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24h dietary recall (24h-DR). Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population. A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

The hotspot of consumers complaints on infant formula: Exploring the relationship among volatile components, sensory evaluation, and aroma recombination and omission experiment

Two-stage infant formula (IF) is rich in polyunsaturated fatty acids (PUFAs), However, PUFAs are susceptible to oxidation, thus affecting the flavor profile of the product. This study aimed to evaluate 4 samples with the participation of 60 consumers. Among these, three samples with the lowest consumer preference and one with the highest preference were selected as controls to explain the causes of off-flavors in two-stage infant formulas. Through qualitative and quantitative analysis of 28 flavor-active compounds in two-stage infant formulas using GC-MS, E-nose, GC-O, odor activity values (OAVs), and variable importance in projection (VIP) values, hexanal, heptanal, pentanal, octanal, and 1-penten-3-one were identified as the important compounds affecting the flavor of two-stage infant formulas. Further, recombination/deficiency experiments confirmed hexanal and heptanal as the key undesirable flavor components in two-stage infant formulas.

Comparative analysis of the differences in volatile organic components of three lavender essential oils in Ili region using sensory evaluation, GC-IMS and GC-MS techniques

Lavender (Lavandula angustifolia Mill.) is a widely utilized aromatic plant, with the economic value of its essential oil (EO) largely dependent on its aroma. This study investigated the differences in volatile organic compounds (VOCs) within the EOs of three species of lavender (H70-1, French blue, Taikong blue) in Ili region from 2019 to 2023 with the combination of sensory evaluation, gas chromatography-ion mobility spectrometry (GC-IMS), and gas chromatography-mass spectrometry (GC-MS). The EO from Taikong blue lavender exhibited greater stability in VOC composition compared to the other two varieties. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) effectively distinguished the aromas of the three EOs aroma. Combining odor activity value (OAV) and variable importance in projection (VIP) values identified five VOCs crucial for discriminating among the three lavender EO types. This study provides theoretical support for the cultivation and commercialization of lavender as an industrial crop, as well as for quality control of EO production in the Ili region.

Classification of Garlic (Allium sativum L.) Crops by Fertilizer Differences Using Ground-Based Hyperspectral Imaging System

In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models for distinguishing garlic fertilizer application differences by employing statistical and machine learning techniques, such as partial least squares (PLS), based on data acquired from a ground-based hyperspectral imaging system in the agricultural sector. The garlic variety chosen for this study was Hongsan, and the fertilizer application plots were segmented into three distinct sections. Data were acquired within the VIS/NIR wavelength range using hyperspectral imaging. Following data acquisition, the standard normal variate (SNV) pre-processing technique was applied to enhance the dataset. To identify the optimal wavelengths, various techniques such as sequential forward selection (SFS), successive projections algorithm (SPA), variable importance in projection (VIP), and interval partial least squares (iPLS) were employed, resulting in the selection of 12 optimal wavelengths. For the fertilizer application difference model, six integrated vegetation indices were chosen for comparison with existing growth indicators. Using the same methodology, the model construction showed accuracies of 90.7% for PLS. Thus, the proposed model suggests that efficient regulation of garlic fertilizer application can be achieved by utilizing statistical and machine learning techniques.

Application of ATR-FTIR for Green Arabica Bean Shelf-Life Determination in Accelerated Storage.

Coffee bean oxidation is associated with enzymatic and non-enzymatic browning, the degradation of desirable aromatic compounds, the development of undesirable flavors, increased susceptibility to microbial spoilage, and volatile compound losses. This study investigated natural dry process (DP) and honey process (HP) green coffee beans stored in GrainPro® bags for 0, 5, 10, and 20 days under accelerated storage conditions at 30 °C, 40 °C, and 50 °C with relative humidity of 50%. A kinetic model was used to estimate the shelf life of the green coffee beans. DP recorded durability of 45.67, 29.9, and 24.92 days at 30 °C, 40 °C, and 50 °C, respectively, with HP 60.34, 38.07, and 19.22 days. Partial least squares (PLS) analysis was performed to build the models in order to predict the shelf life of coffee based on peroxide (PV) and thiobarbituric acid reactive substances (TBARS) values. In terms of prediction with leave-one-out cross-validation (LOOCV), PLS provided a higher accuracy for TBARS (R2 = 0.801), while PV was lower (R2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV (R2 = 0.802) and TBARS (R2 = 0.932). Based on the variable importance of projection (VIP) scores, the ATR-FTIR peaks as 3000-2825, 2154-2150, 1780-1712, 1487-2483, 1186-1126, 1107-1097, and 1012-949 cm-1 were identified to be the most related to PV and TBARS on green coffee beans shelf life. ATR-FITR showed potential as a fast and accurate technique to evaluate the oxidation reaction that related to the loss of coffee quality during storage.

Targeted metabolomics study of fatty-acid metabolism in lean metabolic-associated fatty liver disease patients.

The annual incidence of metabolic-associated fatty liver disease (MAFLD) in China has been increasing and is often overlooked owing to its insidious characteristics. Approximately 50% of the patients have a normal weight or are not obese. They are said to have lean-type MAFLD, and few studies of such patients are available. Because MAFLD is associated with abnormal lipid metabolism, lipid-targeted metabolomics was used in this study to provide experimental evidence for early diagnosis and pathogenesis. To investigate the serum fatty-acid metabolic characteristics in lean-type MAFLD patients using targeted serum metabolomic technology. Between January and June 2022, serum samples were collected from MAFLD patients and healthy individuals who were treated at Shanghai Putuo District Central Hospital for serum metabolomics analysis. Principal component analysis and orthogonal partial least squares-discriminant analysis models were developed, and univariate analysis was used to screen for biomarkers of lean-type MAFLD and analyze metabolic pathways. UPLC-Q-Orbitrap/MS content determination was used to determine serum palmitic acid (PA), oleic acid (OA), linoleic acid (LA), and arachidonic acid (AA) levels in lean-type MAFLD patients. Urea nitrogen and uric acid levels were higher in lean-type MAFLD patients than in healthy individuals (P < 0.05). Alanine transaminase and cholinesterase levels were higher in lean-type MAFLD patients than in healthy individuals (P < 0.01). The expression of high-density lipoprotein and apolipoprotein A-1 were lower in lean-type MAFLD patients than in healthy individuals (P < 0.05) and the expression of triglycerides and fasting blood glucose were increased (P < 0.01). A total of 65 biomarkers that affected the synthesis and metabolism of fatty acids were found with P < 0.05 and variable importance in projection > 1". The levels of PA, OA, LA, and AA were significantly increased compared with healthy individuals. The metabolic profiles of lean-type MAFLD patients and healthy participants differed significantly, yielding 65 identified biomarkers. PA, OA, LA, and AA exhibited the most significant changes, offering valuable clinical guidance for prevention and treatment of lean-type MAFLD.

Research on spatial patterns of soil erosion in wind erosion region based on the revised wind erosion equation and partial least squares regression

Understanding the complex influencing factors of soil erosion is critical for maintaining regional ecological security. The revised wind erosion equation (RWEQ) and partial least squares regression (PLSR) model were used to reveal the primary factors affecting soil erosion. Based on the results of the RWEQ model, the wind erosion area was 8,439.3 km2 in 2018, accounting for 54.3% of the total planning area, and mild erosion, moderate erosion, intense erosion, very intense erosion, and fierce erosion accounted for 18.22%, 11.43%, 11.78%, 10.16%, and 2.74% of the total planning area, respectively. Based on the results of the PLSR model, the results indicate that the proportion of mining land, meteorological indicators, and mean square deviation of slope difference were positively correlated with the soil erosion area, intensity, and severity, respectively. The proportion of grassland and forest land was negatively correlated with the soil erosion area, intensity, and severity. The mean square deviation of slope difference, the mean square deviation of difference in elevation, and the proportion of urban land use contributed the most to the soil erosion severity index, with variable importance in projection (VIP) scores of 1.55, 1.44 and 1.43, respectively. The mean square deviation of slope difference, the proportion of urban land, and the mean square deviation of difference in elevation contributed the most to the soil erosion area, with VIP scores of 1.53, 1.26 and 1.24, respectively. The results provide new insights into wind erosion and the influences of weather conditions and human influences, which can guide regional soil and water conservation planning in wind erosion regions.

Changes in meat quality and volatile flavor compounds profile in beef loin during dry-aging

The objective of this study was to evaluate the effects of dry-aging on beef meat quality changes and flavor compound developments during dry-aging periods. The results showed increased pH and weight loss (P < 0.01), while decreased water content, drip loss, centrifugation loss, and cooking loss (P < 0.01) with dry-aging. The extension of dry-aging significantly improved shear force and myofibrillar protein fragmentation (P < 0.05) of beef loins. However, longer-term dry-aged (28 d) meat exhibited much lower a* values, and higher 2-thiobarbituric reactive substances and carbonyl content (P < 0.01) at the end of dry-aging and even subsequent display. A total of 62 volatile flavor compounds were detected with whole dry-aging periods, and aldehydes were the major potential contributors to cooked beef meat flavor. Principal component analysis indicated the difference in flavor compounds profiles between unaged and dry-aged beef. The content of Strecker aldehydes (2-methyl-butanal and 3-methyl-butanal), some acids, heterocyclic compounds, and ethyl acetate increased with the prolongation of dry-aging time. By combining partial least squares discrimination analysis, 34 compounds with variable importance projection (VIP) > 1 were screened as important aroma compounds changed during the dry-aging processing of beef.

Exploration of the effects of geographical regions on the volatile and non-volatile metabolites of black tea utilizing multiple intelligent sensory technologies and untargeted metabolomics analysis

Geographical regions profoundly influence the flavor characteristics of Congou black tea (CBT). In this study, 35 CBT samples from 7 geographical regions were comprehensively characterized by integrated multiple intelligent sensory technologies and untargeted metabolomics analysis. A satisfactory discrimination was achieved through the fusion of multiple intelligent sensory technologies (R2Y = 0.918, Q2 = 0.859). A total of 104 non-volatile and 169 volatile metabolites were identified by UHPLC-HRMS and GC–MS, respectively. Of these, 45 critical differential non-volatile metabolites and 76 pivotal differential volatile metabolites were pinpointed based on variable importance in projection >1 and p < 0.05. Moreover, 52 key odorants with OAV ≥ 1 were identified, with hexanal, phenylacetaldehyde, linalool, β-cyclocitral, methyl salicylate, geraniol, α-ethylidene phenylacetaldehyde, and trans-β-ionone being recognized as the common odorants across 7 geographical regions. The results provide theoretical support for a comprehensive understanding of the effect of geographical regions on the flavor of black tea.

Serum macroelements and microelements levels in periparturient dairy cows in relation to fatty liver diseases

BackgroundFatty liver in dairy cows is a common metabolic disease defined by triglyceride (TG) buildup in the hepatocyte. Clinical diagnosis of fatty liver is usually done by liver biopsy, causing considerable economic losses in the dairy industry owing to the lack of more effective diagnostic methods. Therefore, this study aimed to investigate the potential utility of blood biomarkers for the diagnosis and early warning of fatty liver in dairy cows.ResultsA total of twenty-four lactating cows within 28 days after parturition were randomly selected as experimental animals and divided into healthy cows (liver biopsy tested, n = 12) and cows with fatty liver (liver biopsy tested, n = 12). Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the macroelements and microelements in the serum of two groups of cows. Compared to healthy cows (C), concentrations of calcium (Ca), potassium (K), magnesium (Mg), strontium (Sr), selenium (Se), manganese (Mn), boron (B) and molybdenum (Mo) were lower and copper (Cu) was higher in fatty liver cows (F). Meanwhile, the observed differences in macroelements and microelements were related to delivery time, with the greatest major disparity between C and F occurring 7 days after delivery. Multivariable analysis was used to test the correlation between nine serum macroelements, microelements and fatty liver. Based on variable importance projection and receiver operating characteristic (ROC) curve analysis, minerals Ca, Se, K, B and Mo were screened as the best diagnostic indicators of fatty liver in postpartum cows.ConclusionsOur data suggested that serum levels of Ca, K, Mg, Se, B, Mo, Mn, and Sr were lower in F than in C. The most suitable period for an early-warning identification of fatty liver in cows was 7 days after delivery, and Ca, Se, K, B and Mo were the best diagnostic indicators of fatty liver in postpartum cows.