Principal Component Analysis Scores Research Articles

Impact of land acquisition for large-scale agricultural investments on vulnerability of displaced households to climate change shocks in Ethiopia

ABSTRACT Although the socio-economic impacts of land acquisition for large-scale agricultural investments (LSAIs) are well researched, studies exploring the impact of LSAIs on climate change vulnerability of the displaced smallholders are scanty or none. This study, therefore, intends to bridge this knowledge gap. It empirically investigates the impact of LSAIs on the climate change vulnerability of displaced smallholders. Data was collected from 521 sample households (49% displaced and 51% non-displaced households) using the face-to-face survey data collection method in the Oromia region. Principal component analysis (PCA) and propensity score matching (PSM) methods were applied to analyze the data. The results indicate that LSAIs have increased the vulnerability of displaced smallholder farmers to climate change shocks by up to three-fold compared to the control households, which is significant at 1%. The finding implies that policymakers should consider the implications of land deals on the vulnerability of the displaced smallholders and design appropriate adaptation strategies to mitigate the impacts. The empirical finding from this study has filled the existing information gap in the areas of climate change vulnerability of the displaced farmers.

Effect of Sun Drying on Phytoconstituents and Antiviral Activity of Ginger against Low-Pathogenic Human Coronavirus

Rhizomes of ginger are commonly used as a spice and for home remedies in either fresh or dry form. This study aimed to assess the effect of sun drying on the volatile constituents, total phenolic and flavonoid content, and the antiviral activity of ginger against low-pathogenic human coronavirus. The antiviral effect of the major volatile compounds was predicted through molecular docking. GC/MS was employed for profiling the volatile constituents of both fresh and dry ginger oils. Moreover, chemometric analysis was applied to discriminate between fresh and dry ginger and to investigate the correlation between their volatile constituents and the antiviral activity using principal component analysis (PCA) and partial least-squares regression (PLS-R). GC/MS analysis revealed that the major effects of the drying process were an increase in α-curcumene and β-sesquiphellandrene. Moreover, total phenolic and flavonoid contents of dried ginger decreased considerably. A PCA score plot revealed significant discrimination between fresh and dry ginger, with α-curcumene and 4-thujanol identified as the main discriminating markers. These findings were validated by in silico molecular docking studies, which revealed that the compounds under consideration had good drug-like characteristics. Thus, ginger is rich in valuable phytoconstituents which showed promising therapy in viral infections such as COVID-19.

Quantification of ternary microplastic mixtures through an ultra-compact near-infrared spectrometer coupled with chemometric tools

The ubiquitous distribution of plastics and microplastics (MPs) and their resistance to biological and chemical decay is adversely affecting the environment. MPs are considered as emerging contaminants of concern in all the compartments, including terrestrial, aquatic, and atmospheric environments. Efficient monitoring, detection, and removal technologies require reliable methods for a qualitative and quantitative analysis of MPs, considering point-of-need testing a new evolution and a great trend at the market level.In the last years, portable spectrometers have gained popularity thanks to the excellent capability for fast and on-site measurements. Ultra-compact spectrometers coupled with chemometric tools have shown great potential in the polymer analysis, showing promising applications in the environmental field. Nevertheless, systematic studies are still required, in particular for the identification and quantification of fragments at the microscale. This study demonstrates the proof-of-concept of a Miniaturized Near-Infrared (MicroNIR) spectrometer coupled with chemometrics for the quantitative analysis of ternary mixtures of MPs. Polymers were chosen representing the three most common polymers found in the environment (polypropylene, polyethene, and polystyrene). Daily used plastic items were mechanically fragmented at laboratory scale mimicking the environmental breakdown process and creating “true-to-life” MPs for the assessment of analytical methods for MPs identification and quantification. The chemical nature of samples before and after fragmentation was checked by Raman spectroscopy. Sixty three different mixtures were prepared: 42 for the training set and 21 for the test set. Blends were investigated by the MicroNIR spectrometer, and the dataset was analysed using Principal Component Analysis (PCA) and Partial Least Square (PLS) Regression. PCA score plot showed a samples distribution consistent with their composition. Quantitative analysis by PLS showed the great capability prediction of the polymer's percentage in the mixtures, with R2 greater than 0.9 for the three analytes and a low and comparable Root-Mean Square Error. In addition, the developed model was challenged with environmental weathered materials to validate the system with real plastic pollution. The findings show the feasibility of employing a portable tool in conjunction with chemometrics to quantify the most abundant forms of MPs found in the environment.

Chemometric techniques in oil spill identification: A case study in Dalian 7.16 oil spill accident of China

Chemometric methods have unique advantages regarding comprehensive consideration of multiple parameters and the classification of samples or variables. Classification of oil spill sources was carried out by using chemometric techniques, such as Repeatability Limit, hierarchical cluster analysis (HCA), Student's t-test and Principal component analysis (PCA) Biplot. In addition, this paper takes the fingerprint identification of a Dalian “7.16″ oil spill accident as an example to illustrate the effectiveness of chemometric techniques in oil identification. PCA scores plot (explaining 82.77% of variance accounted for three PCs) showed that samples belong to four clusters and result of HCA method further confirmed that. The residual oil in Jinshatan Beach and Haibei Square may be caused by the explosion of Dalian “7–16” oil pipeline accident. The use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution. The results will be of great significance to improve the accuracy and efficiency of oil spill identification based on oil fingerprint.

Comparison of near-infrared and dielectric spectra for quantitative identification of bovine colostrum adulterated with mature milk.

The nonhomogeneity of bovine colostrum leads to strong scattering effects for electromagnetic waves, which affects the application of electromagnetic spectroscopy in detecting colostrum. This work aimed to compare the performance of near-infrared spectroscopy (NIRS) and dielectric spectroscopy (DS) in quantitatively predicting the content of mature milk as an adulterant in colostrum. The near-infrared spectra in the range of 833 to 2,500 nm and the dielectric spectra in the range of 20 to 4,500 MHz of 150 adulterated colostrum samples containing 0 to 50% mature milk were analyzed. The different proportions of mature milk in colostrum significantly changed near-infrared and dielectric spectra. The principal component analysis score plot showed that both NIRS and DS could identify the proportion of mature milk in colostrum, but the 2 methods had different characteristics. Linear partial least squares regression and nonlinear least squares support vector machine (LSSVM) models based on near-infrared and dielectric spectra were established to identify doping proportions. The results showed that DS had better identification performance with a root-mean-square error of prediction of 4.9% and a residual prediction deviation of 3.441 by successive projection algorithm LSSVM, whereas NIRS was relatively weak with a root-mean-square error of prediction of 7.3% and a residual prediction deviation of 2.301 by full-spectra LSSVM. This work provides important insights for the quantitative prediction of nonhomogeneous liquid food by DS.

Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters.

There is increased interest in the food industry for emulsions as delivery systems to preserve the stability of sensitive biocompounds with the aim of improving their bioavailability, solubility, and stability; maintaining their texture; and controlling their release. Emulsification in continuously operated microscale devices enables the production of emulsions of controllable droplet sizes and reduces the amount of emulsifier and time consumption, while NIR, as a nondestructive, noninvasive, fast, and efficient technique, represents an interesting aspect for emulsion investigation. The aim of this work was to predict the average Feret droplet diameter of oil-in-water and oil-in-aqueous mint extract emulsions prepared in a continuously operated microfluidic device with different emulsifiers (PEG 1500, PEG 6000, and PEG 20,000) based on the combination of near-infrared (NIR) spectra with chemometrics (principal component analysis (PCA) and partial least-squares (PLS) regression) and artificial neural network (ANN) modeling. PCA score plots for average preprocessed NIR spectra show the specific grouping of the samples into three groups according to the emulsifier used, while the PCA analysis of the emulsion samples with different emulsifiers showed the specific grouping of the samples based on the amount of emulsifier used. The developed PLS models had higher R2 values for oil-in-water emulsions, ranging from 0.6863 to 0.9692 for calibration, 0.5617 to 0.8740 for validation, and 0.4618 to 0.8692 for prediction, than oil-in-aqueous mint extract emulsions, with R2 values that were in range of 0.8109-0.8934 for calibration, 0.5017-0.6620, for validation and 0.5587-0.7234 for prediction. Better results were obtained for the developed nonlinear ANN models, which showed R2 values in the range of 0.9428-0.9917 for training, 0.8515-0.9294 for testing, and 0.7377-0.8533 for the validation of oil-in-water emulsions, while for oil-in-aqueous mint extract emulsions R2 values were higher, in the range of 0.9516-0.9996 for training, 0.9311-0.9994 for testing, and 0.8113-0.9995 for validation.

Investigation of the Effect of Exendin-4 on Oleic Acid-Induced Steatosis in HepG2 Cells Using Fourier Transform Infrared Spectroscopy.

Non-alcoholic fatty liver disease (NAFLD) is a common liver lesion that is untreatable with medications. Glucagon-like peptide-1 receptor (GLP-1R) agonists have recently emerged as a potential NAFLD pharmacotherapy. However, the molecular mechanisms underlying these drugs’ beneficial effects are not fully understood. Using Fourier transform infrared (FTIR) spectroscopy, we sought to investigate the biochemical changes in a steatosis cell model treated or not with the GLP-1R agonist Exendin-4 (Ex-4). HepG2 cells were made steatotic with 400 µM of oleic acid and then treated with 200 nM Ex-4 in order to reduce lipid accumulation. We quantified steatosis using the Oil Red O staining method. We investigated the biochemical alterations induced by steatosis and Ex-4 treatment using Fourier transform infrared (FTIR) spectroscopy and chemometric analyses. Analysis of the Oil Red O staining showed that Ex-4 significantly reduces steatosis. This reduction was confirmed by FTIR analysis, as the phospholipid band (C=O) at 1740 cm−1 in Ex-4 treated cells is significantly decreased compared to steatotic cells. The principal component analysis score plots for both the lipid and protein regions showed that the untreated and Ex-4-treated samples, while still separated, are clustered close to each other, far from the steatotic cells. The biochemical and structural changes induced by OA-induced lipotoxicity are at least partially reversed upon Ex-4 treatment. FTIR and chemometric analyses revealed that Ex-4 significantly reduces OA-induced lipid accumulation, and Ex-4 also restored the lipid and protein biochemical alterations caused by lipotoxicity-induced oxidative stress. In combination with chemometric analyses, FTIR spectroscopy may offer new approaches for investigating the mechanisms underpinning NAFLD.

Surface Characterization as a Tool for Identifying the Factors Affecting the Dissolution Rate of Amorphous Solid Dispersion Tablets.

An amorphous solid dispersion (ASD) is a commonly used approach to enhancing the dissolution of poorly aqueous soluble drugs. Selecting the desired polymer and drug loading can be time-consuming. Surface properties, such as surface composition and wetting behavior, are essential factors controlling the dissolution of ASD tablets. Thus, our study aims to use surface characterization to understand the factors that affect the dissolution rate of ASD tablets. In this work, we prepared ASDs with itraconazole and hypromellose acetate succinate (HPMCAS) using spray drying. ASDs were prepared using three grades of HPMCAS and different drug loading levels (10%, 30%, and 50%). We prepared ASD tablets with two porosities. For each tablet, contact angles were measured using the Drop Shape Analyzer; surface free energies, disperse, and polar fractions were calculated based on the contact angles. We conducted near-infrared (NIR) and dissolution measurements of ASD tablets. Principal component analysis (PCA) was carried out to investigate the NIR spectra further. The relative PCA scores were reported with other sample properties. A partial least square (PLS) model using NIR scores, tablets' wetting properties, and dissolution rates revealed that water and buffer contact angles, surface free energy, and polar fraction are the most significant factors attributing to the dissolution rate of ASD tablets. This work understood the interplay between the surface properties and the dissolution rate of ASD tablets. Moreover, surface characterization can be the tool to screen the formulation and compaction process of ASD tablets in early development.

Cluster analysis and geospatial mapping of antibiotic resistant Escherichia coli O157 in southwest Nigerian communities

Geospatial spread and antibiotic-resistant relatedness of Escherichia coli O157, which are important virulent serotypes causing severe complications leading to high intestinal morbidity and occasional mortality in several communities in southwest Nigeria, were evaluated.Biotyped Escherichia coli strains (n = 508) from subjects with diarrhea and related intestinal infections, various domestic water sources and food animal products were evaluated for antibiotic resistance relatedness, conjugative activity, virulence factor and biofilm production. Antibiotic resistance of Escherichia coli O157 encoded with stx was mapped for geospatial spread.Detected stx-encoded Escherichia coli O157 (7.56%) of human strains were significantly higher compared to water and food animal strains (p = 0.001) with high conjugative and transformative activity (OR(95%CI) = 34.65(94.5); p = 0.023). Water- Escherichia coli O157 reveal significant median resistance to ciprofloxacin, gentamycin (p < 0.05) and human diarrheagenic strains showed >60% resistance to doxycycline (MIC50 8 μg/mL and MIC90 128 μg/mL; p = 0.018), tetracycline (MIC50 4 μg/mL and MIC90 64 μg/mL), ciprofloxacin (MIC50 2 μg/mL and MIC90 128 μg/mL) and gentamycin (MIC50 4 μg/mL and MIC90 256 μg/mL). Strains from human diarrhea, UTI, colitis, cattle, fish, sheep, ground waters, streams, and rivers characterized with biofilm, hemolysin, protease productions, R-plasmid (≈14.30kbp) and MARI (0.84) were highly related. Principal component analysis (score plot) revealed a significant association between resistant human diarrheic strains with cattle and poultry strains. A high population of heterogeneous stx-encoded diarrheagenic and colitis strains was predominant in urban settings spreading with food animal and water Escherichia coli O157 strains.Human diarrheagenic Escherichia coli O157 were highly related to antibiotic resistance and virulence pattern with water and animal products strains. Strategic interventions through the implementation of One Health approach and population-target antimicrobial stewardship are needed to mitigate the increasing intestinal morbidity and reduction of mortality impact. Regular application of spatial data on clonal dissemination is important for monitoring, surveillance of antimicrobial resistance and transmission of zoonotic food-borne Escherichia coli O157 pathogens.

Efficiency of near-infrared spectroscopy in classifying Amazonian wood wastes for bioenergy generation

Finding methods to classify heterogeneous logging wastes from sustainable forest management in the Brazilian Amazonia is essential to increase the production and quality of charcoal. This study proposes a method to classify logging wastes of 12 Amazon hardwoods based on near-infrared (NIR) spectroscopy. The traits evaluated were basic density (BAD) and wet basis moisture content (MCwb). The spectral signatures obtained from the radial and transverse surfaces of the wood samples were submitted to principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA). Spectral data measured on the radial surface of the wood yielded clearer clusters in the PCA score graph, considering the five BAD classes (very low, low, medium, high, and very high). The most promising PLS-DA model for wood classification based on BAD classes was calibrated with the radial surface spectra treated by the first derivative and validated in an independent lot with 97.9% correct classifications. A few incorrect classifications of low-density wood occurred. Still, NIR spectroscopy combined with multivariate statistics proved to be a reliable and fast tool to distinguish the wood from branches of native Amazonian species concerning BAD. It will enable more rationality and sustainability in using these natural resources for bioenergy purposes.

The cooking method features controlling eating quality of cooked rice: An explanation from the view of starch structure in leachate and morphological characteristics

This study investigated leachate and morphological properties of electric-cooked rice (ECR), electric pressure-cooked rice (EPCR), and instant cooked rice (ICR) to explore the effects of cooking methods on eating quality of cooked rice. The leachate was obtained by rinsing cooked rice with warm water. EPCR had the highest amounts of total solid and amylopectin in the leachate and the highest contents of surface and bound lipid. The amylopectin branch chain length of leachate was not significantly different among rice samples. EPCR leachate solution showed the highest apparent viscosity and the greatest decline with increasing shear rate due to high amount of amylopectin. In morphological characteristics, degrees for disruption of the starch structure and compression of protein present in rice kernel were largest in EPCR. Textural hardness of ICR was much lower than that of ECR or EPCR. EPCR had the highest glossiness, stickiness, moistness, and overall acceptability scores. Principal component analysis score plot showed significant differences in leachate and textural characteristics of cooked rice according to cooking methods.

Joint exposure to outdoor ambient air pollutants and incident chronic kidney disease: A prospective cohort study with 90,032 older adults.

ObjectivesThere is paucity of studies to investigate the association between combined and long-term exposure to air pollution and the risk of incident chronic kidney disease (CKD) in older adults.MethodsA prospective cohort of 90,032 older adults who did not have CKD at baseline were followed up from January 1, 2017, to December 31, 2019. Various pollutant data, including particulate matter with diameters ≤ 2.5 mm (PM2.5), ≤ 10 mm (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), Ozone (O3), and carbon monoxide (CO), from all monitoring stations in Binhai New Area, Tianjin were considered in calculating the mean exposure concentration of each pollutant over 2 years. By summing each pollutant concentration weighted by the regression coefficients, we developed an air pollution score that assesses the combined exposure of these air pollutants. Due to the strong correlation between air pollutants, Principal Component Analysis (PCA) score was also developed. The association between air pollutants and incident CKD in the elderly was analyzed.ResultsA total of 90,032 subjects participated in this study with a median follow-up of 545 days. Among them, 22,336 (24.8%) developed CKD. The HR (95% CI) for air pollution score and incidence of CKD was 1.062 (1.060-1.063) and p <0.001 after adjusting for all confounders. The adjusted HRs for the quartile subgroups of combined air pollution score were: Q2: 1.064 (1.013–1.117); Q3: 1.141 (1.088–1.198); and Q4: 3.623 (3.482–3.770), respectively (p for trend <0.001). The adjusted HRs for the quartile subgroups of air quality index (AQI) were: Q2: 1.035 (0.985–1.086); Q3: 1.145 (1.091–1.201); and Q4: 3.603 (3.463–3.748), respectively (p for trend <0.001). When the risk score was over 86.9, it significantly rose in a steep curve. The subgroup analysis showed that male, younger or exercise were more likely to develop CKD.ConclusionCombined air pollution score, AQI, and PCA score were associated with an increased risk of CKD in an exposure-response relationship. Our current results might also provide evidence for developing environmental protection policies.

UPLC-Q-TOF/MS-based metabolomics reveals modulatory effects of Mesona chinensis Benth polysaccharide in liver injury mice induced by cyclophosphamide

The main purpose of this study was to investigate the improvement effect of Mesona chinensis Benth polysaccharide (MP) on cyclophosphamide (CTX) induced liver injury in mice. To explore metabolic profile of liver tissue and feces among normal group, CTX-induced group and MP management group based on metabolomics method by using UPLC-Q-TOF/MS. The results showed that MP could alleviate liver injury and promote the production of short chain fatty acids (SCFAs), with the best dose of 200 mg/kg·body weight (bw). The principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) scores plots of the liver and feces samples showed a clear separation among normal, model and high-dose of MP (MPH). There were 18 endogenous metabolites in liver and 29 endogenous metabolites in feces, which were mainly involved in 8 metabolic pathways: taurine and hypotaurine metabolism, phenylalanine metabolism, α-linolenic acid metabolism, tricarboxylic acid (TCA) cycle, phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, sphingolipid metabolism as well as tryptophan metabolism. Moreover, a common metabolite arachidonic acid was observed in liver and feces samples. These endogenous metabolites may be considered to be MP's response to liver protection. It will help to further understand the mechanism of MP and provide a basis for further research.

Rapid identification of geographic origins of Zingiberis Rhizoma by NIRS combined with chemometrics and machine learning algorithms

In this study, 280 batches of Zingiberis Rhizoma samples from nine producing areas were analyzed to obtain infrared spectral information based on near-infrared spectroscopy(NIRS). Pluralistic chemometrics such as principal component analysis(PCA), partial least squares-discriminant analysis(PLS-DA), orthogonal partial least squares-discriminant analysis(OPLS-DA), K-nearest neighbors(KNN), support vector machine(SVM), random forest(RF), artificial neural network(ANN), and gradient boosting(GB) were applied for tracing of origins. The results showed that the discriminative accuracy of the spectral preprocessing by standard normal variate transformation coupled with the first derivative was 93.9%, which could be used for the construction of the discrimination model. PCA and PLS-DA score plots showed that samples from Shandong, Sichuan, Yunnan, and Guizhou could be effectively distinguished, but the remaining samples were partially overlapped. As revealed by the analysis results by machine learning algorithms, the AUC values of KNN, SVM, RF, ANN, and GB algorithms were 0.96, 0.99, 0.99, 0.99, and 0.98, respectively, with overall prediction accuracies of 83.3%, 89.3%, 90.5%, 91.7%, and 89.3%. It indicated that the developed model was reliable and the machine learning algorithm combined with NIRS for origin identification was sufficiently feasible. OPLS-DA showed that Zingiberis Rhizoma from Sichuan(genuine producing areas) could be significantly distinguished from other regions, with good discriminative accuracy, suggesting that the NIRS established in this study combined with chemometrics can be used for the identification of Zingiberis Rhizoma from Sichuan. This study established a rapid and nondestructive identification and reliable data analysis method for origin identification of Zingiberis Rhizoma, which is expected to provide a new idea for the origin tracing of Chinese medicinal materials.

Source apportionment of water pollutants in Poyang Lake Basin in China using absolute principal component score–multiple linear regression model combined with land-use parameters

Source apportionment of surface water is essential for effective pollution control and sustainable water management. Physical mechanism models usually need so much data and parameters for calibration that their application for complex hydrologic condition watershed becomes difficult. However, reverse source tracing methods only based on water quality parameters present a certain subjectivity and uncertainty. In this research, additional land-use parameters were applied as an auxiliary in principal component analysis (PCA) for accurate identification of pollution sources. Thirteen water quality parameters and two meteorology parameters were used in the PCA and absolute principal component score–multiple linear regression (APCS–MLR) model to quantitatively identify potential pollution sources and their contributions to surface water pollution of the Poyang Lake Basin, in which frequent flow and sediment flux exchange with Yangtze River make the river–lake relationship complex. The results showed that urban wastewater with 34% contribution and agricultural non-point sources with 16% contribution, were the major sources of pollution in water quality. TP and NH3–N, the most serious pollutants, causing agricultural non-point source pollutions with 40% contributions and urban wastewater with 21% contributions were the major sources in the Poyang Lake Basin. Urban wastewater with 60% contributions was the major source of organic contamination. It can be concluded that with associated land-use parameters, the GIS approach with the APCS–MLR model can improve the accuracy and certainty of source apportionment, providing aid decision information for managers on protection of surface water quality.

Development of in-situ Raman diagnosis technique of eosinophil esophagitis

The spectra of the live tissue with blood flow measured with 785 nm-excitation light showed a very weak signal due to hemoglobin (Hb). It suggested the possibility to detect eosinophil accumulation in the tissue with the 785 nm-excitation light. The excitation wavelength of 633 nm induced strong fluorescence of sapphire glass that is a material of the ball lens of BHRP (Ball lens top hollow optical fiber Raman probe). On the other hand, the previous study suggested that eosinophil including eosinophil peroxidase (EPO) that showed a strong resonance Raman effect with 633 nm-excitation light. The purpose of the present study is to collect basic information and to evaluate the viability of Raman spectroscopic analysis for the detection of eosinophil accumulation in the live esophagus. BHRP with a sapphire ball lens with 500 µm diameter was applied for measurement of live esophagus tissue of a mouse. In this study, Raman spectra of eosinophil were measured with 633 and 785 nm-excitation. The Raman spectra of eosinophil showed a strong contribution of EPO, suggested that a heme chromophore in EPO had pre-resonance enhancement via Q band with the 785 nm-excitation light. Principal component analysis (PCA) is applied for the analysis of Raman spectra of eosinophil, erythrocyte and other granulocytes. Eosinophil was successfully discriminated from other blood cells in the PCA score plots built for the datasets of the spectra measured with 633 and 785 nm-excitation wavelengths. Consequently, our study demonstrates that Raman spectroscopy with 785 nm-excitation had high viability for in situ analysis of eosinophilic esophagitis (EoE).

GC-MS Chemical Profiling, Biological Investigation of Three Salvia Species Growing in Uzbekistan.

Salvia is a potentially valuable aromatic herb that has been used since ancient times. The present work studied the chemical profile of three Salvia species essential oils (EO): S. officinalis, S. virgata and S. sclarea, as well as assessing their antioxidant and enzyme inhibitory activities. A total of 144 compounds were detected by GC-MS analysis, representing 91.1, 84.7 and 78.1% in S. officinalis, S. virgata and S. sclarea EOs, respectively. The major constituents were cis-thujone, 2,4-hexadienal and 9-octadecenoic acid, respectively. The principal component analysis (PCA) score plot revealed significant discrimination between the three species. The antioxidant activity of the EOs was evaluated using in vitro assays. Only S. virgata EO showed antioxidant activity in the 2,2-diphenyl-1-picryl-hydrazyl (DPPH) assay (26.6 ± 1.60 mg Trolox equivalent (TE)/g oil). Moreover, this oil exhibited the highest antioxidant activity in 2,2-azino bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), cupric-reducing antioxidant capacity (CUPRAC) and ferric-reducing power (FRAP) assays in comparison with the other two EOs (190.1 ± 2.04 vs. 275.2 ± 8.50 and 155.9 ± 1.33 mg TE/g oil, respectively). However, S. virgata oil did not show any effect in the chelating ability assay, while in the PBD assay, S. officinalis had the best antioxidant activity (26.4 ± 0.16 mmol TE/g oil). Enzyme inhibitory effect of the EOs was assessed against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), tyrosinase, α-glucosidase and α-amylase. AChE enzyme was more sensitive to S. officinalis EO (4.2 ± 0.01 mg galantamine equivalent (GALAE)/g oil), rather than S. virgata EO, which was ineffective. However, S. virgata had the highest BChE effect (12.1 ± 0.16 mg GALAE/g oil). All studied oils showed good tyrosinase inhibitory activity, ranging between 66.1 ± 0.61 and 128.4 ± 4.35 mg kojic acid equivalent (KAE)/g oil). Moreover, the EOs did not exhibit any glucosidase inhibition and were weak or inefficient on amylase enzyme. Partial least squares regression (PLS-R) models showed that there is an excellent correlation between the antioxidant activity and the volatile profile when being compared to that of enzyme inhibitory activity. Thus, the studied Salvia essential oils are interesting candidates that could be used in drug discovery for the management of Alzheimer’s and hyperpigmentation conditions.

Gas chromatography (GC) fingerprinting and immunomodulatory activity of polysaccharide from the rhizome of Menispermum dauricum DC.

This research aimed to establish the gas chromatography (GC) fingerprints and examine the immunomodulatory activity of the rhizome of Menispermum dauricum polysaccharides. In this study, the preparation conditions were optimized by the response surface method (RSM). GC is an effective and sensitive technique employed to measure the composition of monosaccharides; the GC fingerprints of total polysaccharides from 10 batches of the rhizome of M. dauricum (tMDP) were established, and chemometrics methods were adopted to examine the differences and similarities of tMDP from distinct regions. The similarity evaluation illustrated that the polysaccharides derived from the rhizome of M. dauricum from different origins were highly similar. The results of principal components analysis (PCA) illustrated that all the tMDPs may be integrated into one group within the 95% confidence interval, but the rhizome of M. dauricum from different origins could also be distinguished in the plot of PCA scores. Then, the major bioactive fraction MDP was purified and obtained by column chromatography. Our previous study showed that MDP exhibited significant immunomodulatory activity, but the mechanism of the in vitro immunomodulatory activity of MDP is unclear. The macrophage activation induced by MDP was abolished when Toll-like receptor 4 (TLR4) signaling was knocked down by the TLR4 inhibitor. Furthermore, western blot analysis illustrated that MDP activated RAW264.7 cells through MAPKs and NFκB pathways induced by TLR4. This research offers a theoretical foundation for quality control and additional study as a potential immunomodulator of MDP.

An integrated batting performance analytics model for women’s cricket using Principal Component Analysis and Gini scores

Over the years, women cricketers have garnered little media or societal attention. There has been much research on men’s cricket but relatively little on women’s cricket. This study aims to quantify women’s batting performances in one-day international (ODI) matches. The average batting score has traditionally been used in cricket to determine a batter’s worth. However, ranking by simple average ignores two important factors in ODI matches: consistency and strike rates. To address these issues, we propose a novel methodology for calculating ranks that incorporates Gini-based average scores rather than simple averages and considers batting strike rates in a Principal Component Analysis (PCA) framework. As a result, PCA combined with Gini scores can assist cricket fans, coaches, and managers in better understanding a player’s performance.

Investigation of urine metabolome of BALB/c mouse infected with an avirulent strain of Toxoplasma gondii

BackgroundThe protozoan parasite Toxoplasma gondii is a major concern for human and animal health. Although the metabolic understanding of toxoplasmosis has increased in recent years, the analysis of metabolic alterations through noninvasive methodologies in biofluids remains limited.MethodsHere, we applied liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics and multivariate statistical analysis to analyze BALB/c mouse urine collected from acutely infected, chronically infected and control subjects.ResultsIn total, we identified 2065 and 1409 metabolites in the positive electrospray ionization (ESI +) mode and ESI − mode, respectively. Metabolomic patterns generated from principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) score plots clearly separated T. gondii-infected from uninfected urine samples. Metabolites with altered levels in urine from T. gondii-infected mice revealed changes in pathways related to amino acid metabolism, fatty acid metabolism, and nicotinate and nicotinamide metabolism.ConclusionsThis is the first study to our knowledge on urine metabolic profiling of BALB/c mouse with T. gondii infection. The urine metabolome of infected mouse is distinctive and has value in the understanding of Toxoplasmosis pathogenesis and improvement of treatment.Graphical