Ecosystem services dynamics and their influencing factors: Synergies/tradeoffs interactions and implications, the case of upper Blue Nile basin, Ethiopia.

Understanding the relationships among ecosystem services (ESs) and their interactions with influencing factors is essential for spatially targeted ecosystem governance. However, classifying the spatial distribution of these diverse interactions still needs improvement. Furthermore, existing studies have insufficiently addressed the specific impacts of bidirectional land cover transitions on ESs. Taking the upper Blue Nile basin as a study area, we estimated the spatiotemporal distribution of annual water yield (AWY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) from 2000 to 2020, using InVEST models and associated formulas. Changes in ESs per inward-outward land cover transition were quantified based on the Cross-Tabulation Matrix. An improved pairwise method was employed to assess the spatially diverse interactions between ESs pairs and their relationship with influencing factors. The statistical significance of influencing factors was evaluated using partial least square regression. The findings indicated that high HQ values were prevalent in the west, while they were in the east for SR. The central and southern areas experienced higher CS and AWY values. During the study period, variations were observed in the mean values of SR (ranging from 22.89 to 23.88 × 102 t/ha/y), AWY (32.13-42.2 × 102 mm/ha/y), CS (90.5-102.9 × 103gC/ha/y) and HQ (0.62-0.64). Synergies were predominant in AWY-CS, AWY-SR, and CS-SR pairs. HQ revealed more of a no-effect and tradeoff relationship with other ESs. The interactions between ESs and influencing factors were dominated by synergies, followed by tradeoffs and no-effect. The influence of landscape structure (gyrate and landscape shape index) and land surface temperature on all ESs and precipitation on AWY and SR was significant (1.049 ≤ Variable Importance in the Projection ≤ 1.371). Overall, the spatiotemporal dynamics of key ESs and the modeling of their drivers are essential policy information for taking spatially explicit conservation measures. This study will also serve as a valuable methodological reference for future research.

Rapid determination of total flavonoid content, xanthine oxidase inhibitory activities, and antioxidant activity in Prunus mume by near-infrared spectroscopy.

Evaluating the quality of herbal medicine based on the content and activity of its main components is highly beneficial. Developing an eco-friendly determination method has significant application potential. In this study, we propose a new method to simultaneously predict the total flavonoid content (TFC), xanthine oxidase inhibitory (XO) activity, and antioxidant activity (AA) of Prunus mume using near-infrared spectroscopy (NIR). Using the sodium nitrite-aluminum nitrate-sodium hydroxide colorimetric method, uric acid colorimetric method, and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) free radical scavenging activity as reference methods, we analyzed TFC, XO, and AA in 90 P. mume samples collected from different locations in China. The solid samples were subjected to NIR. By employing spectral preprocessing and optimizing spectral bands, we established a rapid prediction model for TFC, XO, and AA using partial least squares regression (PLS). To improve the model's performance and eliminate irrelevant variables, competitive adaptive reweighted sampling (CARS) was used to calculate the pretreated full spectrum. Evaluation model indicators included the root mean square error of cross-validation (RMSECV) and determination coefficient (R2) values. The TFC, XO, and AA model, combining optimal spectral preprocessing and spectral bands, had RMSECV values of 0.139, 0.117, and 0.121, with RCV2 values exceeding 0.92. The root mean square error of prediction (RMSEP) for the TFC, XO, and AA model on the prediction set was 0.301, 0.213, and 0.149, with determination coefficient (RP2) values of 0.915, 0.933, and 0.926. The results showed a strong correlation between NIR with TFC, XO, and AA in P. mume. Therefore, the established model was effective, suitable for the rapid quantification of TFC, XO, and AA. The prediction method is simple and rapid, and can be extended to the study of medicinal plant content and activity.

Myrtucommulones and Related Acylphloroglucinols from Myrtaceae as a Promising Source of Multitarget SARS-CoV-2 Cycle Inhibitors.

The LABEXTRACT plant extract bank, featuring diverse members of the Myrtaceae family from Brazilian hot spot regions, provides a promising avenue for bioprospection. Given the pivotal roles of the Spike protein and 3CLpro and PLpro proteases in SARS-CoV-2 infection, this study delves into the correlations between the Myrtaceae species from the Atlantic Forest and these targets, as well as an antiviral activity through both in vitro and in silico analyses. The results uncovered notable inhibitory effects, with Eugenia prasina and E. mosenii standing out, while E. mosenii proved to be multitarget, presenting inhibition values above 72% in the three targets analyzed. All extracts inhibited viral replication in Calu-3 cells (EC50 was lower than 8.3 µg·mL-1). Chemometric analyses, through LC-MS/MS, encompassing prediction models and molecular networking, identified potential active compounds, such as myrtucommulones, described in the literature for their antiviral activity. Docking analyses showed that one undescribed myrtucommulone (m/z 841 [M - H]-) had a higher fitness score when interacting with the targets of this study, including ACE2, Spike, PLpro and 3CLpro of SARS-CoV-2. Also, the study concludes that Myrtaceae extracts, particularly from E. mosenii and E. prasina, exhibit promising inhibitory effects against crucial stages in SARS-CoV-2 infection. Compounds like myrtucommulones emerge as potential anti-SARS-CoV-2 agents, warranting further exploration.

Sociodemographic factor associations with maternal and placental outcomes: A cluster and partial least squares regression analysis.

INTRODUCTION: Maternal social disadvantage adversely affects maternal and offspring health, with limited research on placental outcomes. Therefore, we examined maternal sociodemographic factor associations with placental and birth outcomes in general (Lifeways Cross-Generation Cohort) and at-risk (PEARS Study of mothers with overweight or obesity) populations of pregnant women. METHODS: TwoStep cluster analysis profiled Lifeways mothers (n = 250) based on their age, parity, marital status, household income, private healthcare insurance, homeowner status, and education. Differences in placental and birth outcomes (untrimmed placental weight (PW), birthweight (BW) and BW:PW ratio) between clusters were assessed using one-way ANOVA and chi-square tests. Partial least squares regression analysed individual effects of sociodemographic factors on placental and birth outcomes in Lifeways and PEARS mothers (n = 461). RESULTS: Clusters were classified as "Married Homeowners" (n = 140, 56 %), "Highest Income" (n = 58, 23.2 %) and "Renters" (n = 52, 20.8 %) in the Lifeways Cohort. Renters were younger, more likely to smoke, have a means-tested medical card and more pro-inflammatory diets compared to other clusters (p < 0.01). Compared to Married Homeowners, renters' offspring had lower BW (-259.26 g, p < 0.01), shorter birth length (-1.31 cm, p < 0.01) and smaller head circumference (-0.59 cm, p = 0.02). PLS regression analyses identified nulliparity as having the greatest negative effect on PW (Lifeways and PEARS) while being a homeowner had the greatest positive effect on PW (Lifeways). CONCLUSION: Certain combinations of sociodemographic factors (particularly homeownership) were associated with less favourable lifestyle factors, and with birth, but not placental outcomes. When explored individually, parity contributed to the prediction of placental and birth outcomes in both cohorts of pregnant women.

An Adaptive Partial Least-Squares Regression Approach for Classifying Chicken Egg Fertility by Hyperspectral Imaging.

Partial least-squares (PLS) regression is a well known chemometric method used for predictive modelling, especially in the presence of many variables. Although PLS was not initially developed as a technique for classification tasks, scientists have reportedly used this approach successfully for discrimination purposes. Whereas some non-supervised learning approaches, including, but not limited to, PCA and k-means clustering, do well in identifying/understanding grouping and clustering patterns in multidimensional data, they are limited when the end target is discrimination, making PLS a preferable alternative. Hyperspectral imaging data on a total of 672 fertilized chicken eggs, consisting of 336 white eggs and 336 brown eggs, were used in this study. Hyperspectral images in the NIR region of the 900-1700 nm wavelength range were captured prior to incubation on day 0 and on days 1-4 after incubation. Eggs were candled on incubation day 5 and broken out on day 10 to confirm fertility. While a total number of 312 and 314 eggs were found to be fertile in the brown and white egg batches, respectively, the total number of non-fertile eggs in the same set of batches was 23 and 21, respectively. Spectral information was extracted from a segmented region of interest (ROI) of each hyperspectral image and spectral transmission characteristics were obtained by averaging the spectral information. A moving-thresholding technique was implemented for discrimination based on PLS regression results on the calibration set. With true positive rates (TPRs) of up to 100% obtained at selected threshold values of between 0.50 and 0.85 and on different days of incubation, the results indicate that the proposed PLS technique can accurately discriminate between fertile and non-fertile eggs. The adaptive PLS approach was, thereby, presented as suitable for handling hyperspectral imaging-based chicken egg fertility data.

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles.

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

Decoding the aroma characteristics of icewine by partial least-squares regression, aroma reconstitution, and omission studies.

The appeal of icewine is attributable to its distinct aroma characteristics, such as 'honey', 'caramel', and 'dried fruit', but little is known about the chemical basis of these aroma attributes. A set of icewines with different aroma intensities were selected by a panel of wine experts. Detailed volatile compound analyses and sensory descriptive analyses were performed on the selected icewines. Using partial least-squares regression, several lactones, esters, terpenes, furanones, and ß-damascenone were positively correlated with 'honey', 'caramel', and 'dried fruit' aromas. Aroma reconstitution studies confirmed that terpenes could significantly enhance the 'honey' aroma, but weaken the 'caramel' aroma, while lactones and furanones could significantly enhance the 'caramel' and 'dried fruit' aromas. In addition, this study demonstrated that terpenes, lactones, and furanones interacted synergistically with each other to cause the sensory perception of the characteristic aromas of icewine.

Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganisms.

BACKGROUND: Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups. RESULTS: The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R2) in the 0.94-0.99 and 0.89-0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles. CONCLUSIONS: The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.

In-line monitoring of Bordetella pertussis cultivation using fluorescence spectroscopy.

Fluorescence spectroscopy is a non-invasive and highly sensitive method for bioprocess monitoring. The use of fluorescence spectroscopy is not very well established in the industry for in-line monitoring. In the present work, a 2-D fluorometer with two excitation lights (365 and 405 nm) and emission spectra in the range of 350-850 nm were used for in-line monitoring of two strains of Bordetella pertussis cultivation operated in batch and fed batch. A Partial Least Squares (PLS) based regression model was used for the estimation of cell biomass, amino acids (glutamate and proline) and antigen (Pertactin) produced. It was observed that accurate predictions were achieved when models were calibrated separately for each cell strain and nutrient media formulation. Also, prediction accuracy was improved when dissolved oxygen, agitation and culture volume are added as additional features in the regression model. The proposed approach of combining in-line fluorescence and other online measurements is shown to have good potential for in-line monitoring of bioprocesses.

Prediction of wheat gluten composition via near-infrared spectroscopy.

Gluten composition is an important quality parameter for wheat flour, because it is strongly correlated to baking quality. Wheat proteins are commonly extracted stepwise and analysed using RP-HPLC-UV to determine the gluten composition. This procedure is very time-consuming and labour-intensive. Therefore, a new, fast and easy method to quantitate gluten proteins was established using NIR spectroscopy (NIRS). PLS-regression models were calculated containing 207 samples for calibration and 169 for test set validation. Albumin/globulin (ALGL), gluten, gliadin and glutenin content was predicted with a root mean square error of prediction (RMSEP) of 2.01 mg/g, 6.09 mg/g, 4.25 mg/g and 3.50 mg/g, respectively. High-molecular-weight glutenin subunits (HMW-GS) and low-molecular-weight glutenin subunits (LMW-GS) were predicted with a RMSEP of 1.12 mg/g and 2.38 mg/g. The relative error was too high for ALGL, LMW-GS and HMW-GS, but that of gluten, gliadins and glutenins was in a range comparable to the reference method. Therefore, the new NIRS method can be used to estimate the gluten composition of wheat flour, including the gliadin/glutenin and the LMW-GS/HMW-GS ratio.

Characterizing Spray-Dried Powders through NIR Spectroscopy: Effect of Two Preparation Strategies for Calibration Samples and Comparison of Two Types of NIR Spectrometers.

Emerging portable near infrared (NIR) spectroscopic approaches coupled with data analysis and chemometric techniques provide opportunities for the rapid characterization of spray-dried products and process optimization. This study aimed to enhance the understanding of applying NIR spectroscopy in spray-dried samples by comparing two sample preparation strategies and two spectrometers. Two sets of whey protein-maltodextrin matrixes, one with a protein content gradient and one with a consistent protein content, were spray-dried, and the effect of the two preparation strategies on NIR calibration model development was studied. Secondly, a portable NIR spectrometer (PEAK) was compared with a benchtop NIR spectrometer (CARY) for the moisture analysis of prepared samples. When validating models with the samples with focused protein contents, the best PLS protein models established from the two sample sets had similar performances. When comparing two spectrometers, although CARY outperformed PEAK, PEAK still demonstrated reliable performance for moisture analysis, indicating that it is capable as an inline sensor.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy.

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Comparing the predictions by NIR spectroscopy based multivariate models for distillation fractions of crude oils by F-test.

The whole range of distillation fractions in industrially relevant crude oil samples is predicted by using two multivariate models based on near-infrared (NIR) spectra. The first versions of the models as well as the respective model updates are considered, with the updates largely aimed at expanding the models. The prediction results are compared across all the fractions and F-test is used to critically compare the performance of the models and the effectiveness of the limited updates. The results suggest that both multivariate methods perform very comparably, and the updates do not lead to statistically significant changes, which differs from what one could conclude from the nominal prediction errors. The near-equivalency of the prediction accuracy of the updated models is additionally illustrated by perusing predictions of a number of batches from one sour and one sweet crude arriving at the refinery during a four month period.

Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.

Nowadays, the quality of natural products is an issue of great interest in our society due to the increase in adulteration cases in recent decades. Coffee, one of the most popular beverages worldwide, is a food product that is easily adulterated. To prevent fraudulent practices, it is necessary to develop feasible methodologies to authenticate and guarantee not only the coffee's origin but also its variety, as well as its roasting degree. In the present study, a C18 reversed-phase liquid chromatography (LC) technique coupled to high-resolution mass spectrometry (HRMS) was applied to address the characterization and classification of Arabica and Robusta coffee samples from different production regions using chemometrics. The proposed non-targeted LC-HRMS method using electrospray ionization in negative mode was applied to the analysis of 306 coffee samples belonging to different groups depending on the variety (Arabica and Robusta), the growing region (e.g., Ethiopia, Colombia, Nicaragua, Indonesia, India, Uganda, Brazil, Cambodia and Vietnam), and the roasting degree. Analytes were recovered with hot water as the extracting solvent (coffee brewing). The data obtained were considered the source of potential descriptors to be exploited for the characterization and classification of the samples using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). In addition, different adulteration cases, involving nearby production regions and different varieties, were evaluated by pairs (e.g., Vietnam Arabica-Vietnam Robusta, Vietnam Arabica-Cambodia and Vietnam Robusta-Cambodia). The coffee adulteration studies carried out with partial least squares (PLS) regression demonstrated the good capability of the proposed methodology to quantify adulterant levels down to 15%, accomplishing calibration and prediction errors below 2.7% and 11.6%, respectively.

Analysis of White Mulberry Leaves and Dietary Supplements, ATR-FTIR Combined with Chemometrics for the Rapid Determination of 1-Deoxynojirimycin.

Diabetes mellitus is a metabolic disease affecting more people every year. The treatment of diabetes and its complications involve substantial healthcare expenditures. Thus, there is a need to identify natural products that can be used as nutraceuticals to prevent and treat early-stage diabetes. White mulberry (Morus alba L.) is a plant that has been used in traditional Chinese medicine for thousands of years due to its many beneficial biological properties. White mulberry leaves are a source of 1-deoxynojirimycin (DNJ), which, due to its ability to inhibit α-glucosidase, can be used to regulate postprandial glucose concentration. In addition to consuming dried white mulberry leaves as herbal tea, many functional foods also contain this raw material. The development of the dietary supplements market brings many scientific and regulatory challenges to the safety, quality and effectiveness of such products containing concentrated amounts of nutraceuticals. In the present study, the quality of 19 products was assessed by determining the content of DNJ, selected (poly)phenols and antioxidant activity (DPPH• assay). Nine of these products were herbal teas, and the other samples were dietary supplements. These results indicate the low quality of tested dietary supplements, the use of which (due to the low content of nutraceuticals) cannot bring the expected beneficial effects on health. Moreover, a method for determining the content of DNJ (the essential component for antidiabetic activity) based on ATR-FTIR spectroscopy combined with PLS regression has been proposed. This might be an alternative method to the commonly used chromatographic process requiring extraction and derivatization of the sample. It allows for a quick screening assessment of the quality of products containing white mulberry leaves.

Detection and Quantification of Alprazolam Added to Long Drinks by Near Infrared Spectroscopy and Chemometrics.

In the present work, a fast, relatively cheap, and green analytical strategy to identify and quantify the fraudulent (or voluntary) addition of a drug (alprazolam, the API of Xanax®) to an alcoholic drink of large consumption, namely gin and tonic, was developed using coupling near-infrared spectroscopy (NIR) and chemometrics. The approach used was both qualitative and quantitative as models were built that would allow for highlighting the presence of alprazolam with high accuracy, and to quantify its concentration with, in many cases, an acceptable error. Classification models built using partial least squares discriminant analysis (PLS-DA) allowed for identifying whether a drink was spiked or not with the drug, with a prediction accuracy in the validation phase often higher than 90%. On the other hand, calibration models established through the use of partial least squares (PLS) regression allowed for quantifying the drug added with errors of the order of 2-5 mg/L.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra.

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed "generic" models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Non/mini-invasive monitoring of diabetes-induced myocardial damage by Fourier transform infrared spectroscopy: Evidence from biofluids.

Early identification of diabetic cardiomyopathy (DCM) can help clinicians develop targeted treatment plans and forensic pathologists make accurate postmortem diagnoses. In the present study, diabetes-induced metabolic abnormalities in the myocardium and biofluids (plasma, urine, and saliva) of db/db mice of various ages (7, 12, and 21 weeks) were investigated by attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results indicated that the diabetic and control groups had significantly different changes in the function groups of lipids, phosphate macromolecules (mostly nucleic acids), protein compositions and conformations, and carbohydrates (primarily glucose) in the myocardium and biofluids. The prediction model for quantifying DCM severity was developed on db/db mice's myocardial spectra using a genetic algorithm (GA)-partial least squares (PLS) regression method. Following that, the linear correlations between the predicted values for DCM severity and spectra for db/db biofluids were evaluated using the GA-PLS regression algorithm. The results showed there were good linear correlations between the predicted values for DCM severity and spectra for plasma (R2 = 0.929), saliva (R2 = 0.967), urine (R2 = 0.954), and combination of plasma and saliva (R2 = 0.980). This study provides a novel perspective on detecting diabetes-related biofluid and cardiac metabolic abnormalities and demonstrates the potential of biofluid infrared spectro-diagnostic models for non/mini-invasive assessment of DCM.

Big-sized trees and species-functional diversity pathways mediate divergent impacts of environmental factors on individual biomass variability in Sri Lankan tropical forests.

In tropical forests, several studies have explored the effects of environmental factors and tree species diversity as well as functional trait diversity and trait composition on aboveground biomass (AGB) stock. However, these abiotic and biotic effects on individual biomass variability (BioVar) are still largely unexplored, which limits our understanding of the plant-plant interactions for species coexistence. Here, we used the Partial Least Squares Structural Equation Models (PLS-SEMs), and other complementary analyses, on data from 189 tropical forest plots in Sri Lanka, to test the linkages amongst climate (a latent variable of solar radiation and potential evapotranspiration), soil (pH and cation exchange capacity), plot (plot size and stand density) conditions, big-sized trees, species-functional diversity, and BioVar. The PLS-SEMs showed that climate conditions decreased BioVar directly but increased indirectly via integrative promoting direct effects on soil conditions, species-functional diversity and big-sized trees. In contrast, soil conditions increased BioVar directly but decreased indirectly via integrative suppressing direct effects on species-functional diversity and big-sized trees. Interestingly, we found that the divergent indirect effects of climate and soil conditions on BioVar via big-sized trees mattered when the direct effect of big-sized trees on species-functional diversity was considered as compared to the reverse effect in PLS-SEMs. Also, the indirect positive effect of plot properties on BioVar was nearly equal to the direct effect because plot properties affected big-sized trees as similar as or lower than species-functional diversity. The positive effect of species-functional diversity on BioVar was mediated by the structural attributes of big-sized trees, indicating increased plant species co-existence. This study suggests that individual tree biomass variability (i.e., BioVar) should be considered for managing natural tropical forests in the context of the plant-plant interactions for species coexistence.

Time-related multivariate strategy for the comprehensive evaluation of microbial chemical data.

INTRODUCTION: In microbial metabolomics, the use of multivariate data analysis (MDVA) has not been comprehensively explored regarding the different techniques available and the information that each gives about the metabolome. To overcome these limitations, here we show the use of Fusarium oxysporum cultured in the presence of exogenous alkaloids as a model system to demonstrate a comprehensive strategy for metabolic profiling. MATHERIALS AND METHODS: F. oxysporum was harvested on different days of incubation after alkaloidal addition, and the chemical profiles were compared using LC-MS data and MDVA. We show significant innovation to evaluate the chemical production of microbes during their life cycle by utilizing the full capabilities of Partial Least Square (PLS) with microbial-specific modeling that considers incubation days, media culture availability, and growth rate in solid media. RESULTS AND DISCUSSCION: Results showed that the treatment of the Y-data and the use of both PLS regression and discrimination (PLSr and PLS-DA) inferred complemental chemical information. PLSr revealed the metabolites that are produced/consumed during fungal growth, whereas PLS-DA focused on metabolites that are only consumed/produced at a specific period. Both regression and classificatory analysis were equally important to identify compounds that are regulated and/or selectively produced as a response to the presence of the alkaloids. Lastly, we report the annotation of analogs from the piperidine alkaloids biotransformed by F. oxysporum as a defense response to the toxic plant metabolites. These molecules do not show the antimicrobial potential of their precursors in the fungal extracts and were rapidly produced and consumed within 4 days of microbial growth.