Multivariate Data Analysis Research Articles

Variability of the proximal phalanx in warmblood and coldblood horses – morphological and structural analyses

Anatomical shape as well as bone geometry are important factors for the mechanical properties of stiffness and strength in bones. In the light of this statement, the primary aim of the study was to evaluate the variable forms of the proximal phalanx in different types of horses. Multivariate analyses of data from 81 horses revealed that the proximal phalanx has diverse spatial forms. Differences were observed particularly in the length of the bone and the breadth of its diaphysis. In horses with lighter morphotype, the phalanx is significantly narrower in its middle section. In the second stage of the study, geometrical parameters of the phalanx were analysed with the use of peripheral quantitative computed tomography (pQCT). Tomographic analysis was conducted at three levels: at 15%, 50% and 85% of the bone length. Based on the analysis, we concluded that most of the geometric parameters have higher values in coldbloods but only at the mid-diaphysis (50%) and at 85% of the bone length. Moreover, in coldblood horses, higher strength of the phalanx at these levels, expressed by Strength Strain Index, was observed. We did not observe any significant differences between warmblood and coldblood horses in the metaphyseal proximal region which is located at 15 % of the bone length.

The Effect of Formulation Variables on the Manufacturability of Clopidogrel Tablets via Fluidized Hot-Melt Granulation-From the Lab Scale to the Pilot Scale.

Solid pharmaceutical formulations with class II active pharmaceutical ingredients (APIs) face dissolution challenges due to limited solubility, affecting in vivo behavior. Robust computational tools, via data mining, offer valuable insights into product performance, complementing traditional methods and aiding in scale-up decisions. This study utilizes the design of experiments (DoE) to understand fluidized hot-melt granulation manufacturing technology. Exploratory data analysis (MVDA) highlights similarities and differences in tablet manufacturability and dissolution profiles at both the lab and pilot scales. The study sought to gain insights into the application of multivariate data analysis by identifying variations among batches produced at different manufacturing scales for this technology. DoE and MVDA findings show that the granulation temperature, time, and Macrogol type significantly impact product performance. These factors, by influencing particle size distribution, become key predictors of product quality attributes such as resistance to crushing, disintegration time, and early-stage API dissolution in the profile. Software-aided data mining, with its multivariate and versatile nature, complements the empirical approach, which is reliant on trial and error during product scale-up.

Integrated analysis of a competing endogenous RNA network reveals a ferroptosis-related 6-lncRNA prognostic signature in clear cell renal cell carcinoma.

Establishing a robust signature for prognostic prediction and precision treatment is necessary due to the heterogeneous prognosis and treatment response of clear cell renal cell carcinoma (ccRCC). This study set out to elucidate the biological functions and prognostic role of ferroptosis-related long non-coding RNAs (lncRNAs) based on a synthetic analysis of competing endogenous RNA networks in ccRCC. Ferroptosis-related genes were obtained from the FerrDb database. The expression data and matched clinical information of lncRNAs, miRNAs and mRNAs from The Cancer Genome Atlas (TCGA) database were obtained to identify differentially expressed RNAs. The lncRNA-miRNA-mRNA ceRNA network was established utilizing the common miRNAs that were predicted in the RNAHybrid, StarBase and TargetScan databases. Then, using progressive univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis of gene expression data and clinical information, a ferroptosis-related lncRNA prognosis signature was constructed based on the lncRNAs in ceRNA. Finally, the influence of independent lncRNAs on ccRCC was explored. A total of 35 ferroptosis-related mRNAs, 356 lncRNAs and 132 miRNAs were sorted out after differential expression analysis in the TCGA-KIRC. Subsequently, overlapping lncRNA-miRNA and miRNA-mRNA interactions among the RNAHybrid, StarBase and TargetScan databases were constructed and identified; then a ceRNA network with 77 axes related to ferroptosis was established utilizing mutual miRNAs in 2 interaction networks as nodes. Next, a 6-ferroptosis-lncRNA signature including PVT1, CYTOR, MIAT, SNHG17, LINC00265, and LINC00894 was identified in the training set. Kaplan-Meier analysis, PCA, t-SNE analysis, risk score curve, and receiver operating characteristic (ROC) curve were performed to confirm the validity of the signature in the training set and verified in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) analysis showed that the signature was related to immune cell infiltration. Our research underlines the role of the 6-ferroptosis-lncRNA signature as a predictor of prognosis and a therapeutic alternative for ccRCC.

Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize

This study analyzed the capability of aerial RGB (red-green-blue) and hyperspectral-derived vegetation indices to assess the response of sweet maize (Zea mays var. saccharata L.) to different water and nitrogen inputs. A field experiment was carried out during 2020 by using both remote RGB images and ground hyperspectral sensor data. Physiological parameters (i.e., leaf area index, relative water content, leaf chlorophyll content index, and gas exchange parameters) were measured. Correlation and multivariate data analysis (principal component analysis and stepwise linear regression) were performed to assess the strength of the relationships between eco-physiological measured variables and both RGB indices and hyperspectral data. The results revealed that the red-edge indices including CIred-edge, NDRE and DD were the best predictors of the maize physiological traits. In addition, stepwise linear regression highlighted the importance of both WI and WI:NDVI for prediction of relative water content and crop temperature. Among the RGB indices, the green-area index showed a significant contribution in the prediction of leaf area index, stomatal conductance, leaf transpiration and relative water content. Moreover, the coefficients of correlation between studied crop variables and GGA, NDLuv and NDLab were higher than with the hyperspectral indices measured at the ground level. The findings confirmed the capacity of selected RGB and hyperspectral indices to evaluate the water and nitrogen status of sweet maize and provided opportunity to expand experimentation on other crops, diverse pedo-climatic conditions and management practices. Hence, the aerially collected RGB vegetation indices might represent a cost-effective solution for crop status assessment.

Study of the surface, rheological and morphological characteristics of isotactic polypropylene microplastics aged in a photothermal oxidation chamber

The aim of this study was to investigate, in addition to degradation processes, the rheological, crystalline, and molecular dynamics changes in isotactic polypropylene microplastics with average sizes of 0.5 and 2 mm, aged in a photothermal oxidation chamber for 166 days. In addition to commonly used techniques in microplastics research, time-domain nuclear magnetic resonance (TD-NMR) in fast field cycling and fixed field modes, was employed for the first time in the study of these materials, providing non-destructive information that supports other results. Concurrently with X-ray diffractometry (XRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), rheometry, Fourier-transform infrared spectroscopy (FTIR), surface wettability, and scanning electron microscopy (SEM) techniques, two antagonistic phenomena were observed over the aging period. One associated with the surface degradation of microplastics and increased molecular mobility, and the other related to morphological changes in the internal layers, leading to increased rigidity. These phenomena occur slightly differently depending on the average particle size. Through multivariate data analysis with principal component analysis (PCA) and hierarchical cluster analysis (HCA), it was possible to determine that the degradation process intensifies after 62 days of simultaneous exposure to ultraviolet radiation, temperature, and oxidizing atmosphere. The obtained data highlight that even synthetic polymers with long degradation periods can undergo, in the form of microplastics, initial morphological changes in just 15 days and degradation processes within a few months of exposure to weathering conditions.

Women Tourism Entrepreneurs in KwaZulu – Natal, South Africa: Capabilities and Challenges?

This study conducted in the context of South Africa, aims to explore gender nuances in the performances of tourism-related businesses in the Durban Central Business District, KwaZulu-Natal, with emphases on traditional societal perceptions and access to capital. Questionnaire survey, a quantitative research method, was used to collect data from 150 tourism-related business owners and managers using purposive sampling method. Data was analysed using IBM’s SPSS version 25 software. Descriptive (frequency, mean), bivariate (Spearman’s Rank Correlation (two-tailed) test, Pearson’s Chi-Square test, Mann-Whitney U test) and multivariate (Reliability test using Cronbach’s Alpha) data analyses were applied in this study. Results shows that entrepreneurial performance is influenced by the belief system of the society, marketing capabilities, and the entrepreneur background. Hence, there may be the influence of African traditional societal perceptions on African women entrepreneurs to be more contented than men with the small size of their businesses (because society expects them not to manage large businesses). This study did not make a conclusive finding that gender plays a strong role in differentiating business success or performance. However, the study found that it is somewhat easier for female entrepreneurs to find business start-up capital. Based on this study results, authors recommend that entrepreneurial training and mentorship programmes should emphasise that gender has no role in determining entrepreneurial success. Better optimisation of marketing capability and increased awareness of entrepreneurs regarding sources of business funding, are crucial for business start-ups and success.

Comparative analysis of the application efficiency of gold nanoparticles-based SERS in four foodborne pathogenic microbes

It has been widely reported that SERS can detect foodborne pathogenic bacteria, but SERS has only been limited to the detection of purified single bacteria. However, there’s few single bacterial infections in the natural state were found at the most time, and rare comparative investigations of different Gram-negative and Gram-positive bacteria by SERS exist. At the present study, we are aiming at the comparative analysis of the detection efficiency toward different foodborne pathogenic microbes by a conventional citrate-reduced gold colloid, including Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Listeria monocytogenes. The SERS spectra of samples were collected with sterile 0.5% NaCl solution as aggregating compound. First, the stability of each bacterial were detected with gold nanoparticles. Afterward, SERS signals of the four bacteria were compared at different concentrations, and it was found that Gram-positive and Gram-negative bacteria have different minimum sensitivity and detection stability. Next, the mixed bacterial SERS detection was applied to classify and identify the similar Raman spectra. Spectral data were analyzed using multivariate data analysis methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The SERS procedure we proposed, which utilized gold nanoparticles adsorbing on bacteria and multivariate analysis, allowed differentiation of species. The results of this pilot study demonstrate the potential of SERS to rapidly identify Gram-negative and Gram-positive foodborne pathogens.

Untargeted NMR-based metabolomics analysis of kidney allograft perfusates identifies a signature of delayed graft function.

Kidney transplantation (KTx) necessarily conveys an ischemia/reperfusion (I/R) process, which impacts on allograft outcomes. Delayed graft function (DGF) is defined as a non-decrease of serum creatinine by at least 10% daily on 3 consecutive days during the first 7days post-KTx. DGF significantly conditions both short- and long-term graft outcomes. Still there is a lack of DGF predictive biomarkers. This study aimed to explore the potential of kidney graft perfusate metabolomics to predict DGF occurrence. 49 human perfusates from grafts categorized upon donor type [donation after brain death (DBD)/donation after circulatory death (DCD)] and DGF occurrence and 19 perfusates from a murine model classified upon death type (DBD/DCD) were collected and analyzed by NMR-based metabolomics. The multivariate analysis of the murine data highlighted significant differences between perfusate metabolomes of DBD versus DCD. These differences were similarly observed in the human perfusates. After correcting for the type of donor, multivariate analysis of human data demonstrated a metabolomics signature that could be correlated with DGF occurrence. The metabolome of kidney grafts is influenced by the donor's type in both human and pre-clinical studies and could be correlated with DGF in the human DBD cohort. Thus, metabolomic analysis of perfusate applied prior to KTx may represent a new predictive tool for clinicians in a more personalized management of DGF. Moreover, our data paves the way to better understand the impact of donor's types on the biochemical events occurring between death and the hypothermic storage.

Comparison of autoclaving and γ-radiation impact on four spices aroma profiles and microbial load using HS-SPME GC–MS and chemometric tools

Herbal spices are widely consumed as food additives owing to their distinct aroma and taste as well as a myriad of economic and health value. The aroma profile of four major spices including bay leaf, black pepper, capsicum, and fennel was tested using HS-SPME/GC–MS and in response to the most widely used spices´ processing methods including autoclaving and γ-radiation at low and high doses. Additionally, the impact of processing on microbial contamination of spices was tested using total aerobic count. GC–MS analysis led to the identification of 22 volatiles in bay leaf, 34 in black pepper, 23 in capsicum, and 24 in fennel. All the identified volatiles belonged to oxides/phenols/ethers, esters, ketones, alcohols, sesquiterpene and monoterpene hydrocarbons. Oxides/phenol/ethers were detected at high levels in all tested spices at ca. 44, 28.2, 48.8, 61.1%, in bay leaves, black pepper, capsicum, and fennel, respectively of the total blend and signifying their typical use as spices. Total oxides/phenol/ethers showed an increase in bay leaf upon exposure to γ-radiation from 44 to 47.5%, while monoterpene hydrocarbons were enriched in black pepper upon autoclaving from 11.4 in control to reach 65.9 and 82.6% for high dose and low dose of autoclaving, respectively. Cineole was detected in bay leaf at 17.9% and upon exposure to autoclaving at high dose and γ-radiation (both doses) its level increased by 29–31%. Both autoclaving and γ-radiation distinctly affected aroma profiles in examined spices. Further, volatile variations in response to processing were assessed using multivariate data analysis (MVA) revealing distinct separation between autoclaved and γ-radiated samples compared to control. Both autoclaving at 115 °C for 15 min and radiation at 10 kGy eliminated detected bioburden in all tested spices i.e., reduced the microbial counts below the detection limit (< 10 cfu/g).

Ground air pollutants explanation based on multiple visibility graph of complex network by temporal community division.

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants' correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

Multivariate statistical approach in food and pharmaceutical quality control

IR spectra contain chemical information of matter and can be acquired from raw/untreated samples. The spectra are, however, complicated to interpret and could not be used directly for both qualitative and quantitative purposes. In this research a statistical approach namely, multivariate data analysis (MVDA) or chemometrics was employed for mining information related to chemical compositions from spectroscopic data. Two examples are used to illustrate the potential of this approach, one is edible oil (using benchtop FT-IR), and pharmaceuticals (using handheld NIR). Olive oil was differentiated from adulterants (sesame, sunflower, palm oil) in PCA, and the content of olive oil was successfully determined by the PLS model the error of olive oil content &lt; 5%. Norfloxacin content in lab-scale powder formulation yield the auspicious results with the error &lt; 6%. The results proved the developed techniques are promising for rapid analysis at significantly lower costs.

Isolation of Distinct Networks Driving Action and Cognition in Psychomotor Processes

BackgroundPsychomotor disturbances are observed across psychiatric disorders and often manifest as psychomotor slowing, agitation, disorganized behavior, or catatonia. Psychomotor function includes both cognitive and motor components, but the neural circuits driving these subprocesses and how they relate to symptoms have remained elusive for centuries. MethodsWe analyzed data from the HCP-EP (Human Connectome Project for Early Psychosis), a multisite study of 125 participants with early psychosis and 58 healthy participants with resting-state functional magnetic resonance imaging and clinical characterization. Psychomotor function was assessed using the 9-hole pegboard task, a timed motor task that engages mechanical and psychomotor components of action, and tasks assessing processing speed and task switching. We used multivariate pattern analysis of whole-connectome data to identify brain correlates of psychomotor function. ResultsWe identified discrete brain circuits driving the cognitive and motor components of psychomotor function. In our combined sample of participants with psychosis (n = 89) and healthy control participants (n = 52), the strongest correlates of psychomotor function (pegboard performance) (p < .005) were between a midline cerebellar region and left frontal region and presupplementary motor area. Psychomotor function was correlated with both cerebellar-frontal connectivity (r = 0.33) and cerebellar–presupplementary motor area connectivity (r = 0.27). However, the cognitive component of psychomotor performance (task switching) was correlated only with cerebellar-frontal connectivity (r = 0.19), whereas the motor component (processing speed) was correlated only with cerebellar–presupplementary motor area connectivity (r = 0.15), suggesting distinct circuits driving unique subprocesses of psychomotor function. ConclusionsWe identified cerebellar-cortical circuits that drive distinct subprocesses of psychomotor function. Future studies should probe relationships between cerebellar connectivity and psychomotor performance using neuromodulation.

Untargeted metabolomics reveals potential plasma biomarkers for diagnosis of primary aldosteronism using liquid chromatography-mass spectrometry.

Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed-phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy.

Application of Multivariate Data Analysis on Historical Recombinant Adenovirus Zoster Vaccine Production Data for Upstream Process Improvements

In recent years, multivariate data analysis (MVDA) has been widely used for process characterization and fault diagnosis in the biopharmaceutical industry. This study aims to investigate the feasibility of using MVDA for the development and scale-up of a perfusion process for HEK293 cell-based recombinant adenovirus zoster vaccine (Ad-HER) production. The Principal Component Analysis (PCA) results suggested comparable performance among the ATF, PATFP, and BFP perfusion systems in benchtop-scale stirred-tank bioreactor (STR). Then a Batch Evolution Model (BEM) was built using representative data from 10 L STR with a BFP system to assess the Ad-HER perfusion process performance at pilot-scale bioreactor (50 L STR and 50 L wave bioreactor). Furthermore, another BEM model and Batch Level Model (BLM) were built to monitor process parameters over time and predict the final adenovirus titer in 50 L wave bioreactor. The loading plot revealed that lactate dehydrogenase activity, viable cell diameter, and base-added during the virus production phase could be used as preliminary indicators of adenovirus yield. Finally, an adenovirus titer of 2.0±0.3×1010 IFU/mL was achieved in the 50 L wave bioreactor with BFP system, highlighting the robustness of the Ad-HER perfusion process at pilot-scale. Overall, this study emphasizes the effectiveness of MVDA as a tool for advancing the understanding of recombinant adenovirus vaccine perfusion production process development and scale-up.

Pathogenicity of monokaryotic and dikaryotic mycelia of Ganoderma boninense revealed via LC–MS-based metabolomics

This study compared the pathogenicity of monokaryotic (monokaryon) and dikaryotic (dikaryon) mycelia of the oil palm pathogen Ganoderma boninense via metabolomics approach. Ethyl acetate crude extracts of monokaryon and dikaryon were analysed by liquid chromatography quadrupole/time-of-flight-mass spectrometry (LC-Q/TOF–MS) coupled with multivariate data analysis using MetaboAnalyst. The mummichog algorithm was also used to identify the functional activities of monokaryon and dikaryon without a priori identification of all their secondary metabolites. Results revealed that monokaryon produced lesser fungal metabolites than dikaryon, suggesting that monokaryon had a lower possibility of inducing plant infection. These findings were further supported by the identified functional activities. Monokaryon exhibits tyrosine, phenylalanine, and tryptophan metabolism, which are important for fungal growth and development and to produce toxin precursors. In contrast, dikaryon exhibits the metabolism of cysteine and methionine, arginine and proline, and phenylalanine, which are important for fungal growth, development, virulence, and pathogenicity. As such, monokaryon is rendered non-pathogenic as it produces growth metabolites and toxin precursors, whereas dikaryon is pathogenic as it produces metabolites that are involved in fungal growth and pathogenicity. The LC–MS-based metabolomics approach contributes significantly to our understanding of the pathogenesis of Ganoderma boninense, which is essential for disease management in oil palm plantations.

A metabolomics approach for monitoring thermal processing temperature of bovine milk using ultra-performance liquid chromatography tandem Q-Exactive mass spectrometry and multivariate data analysis

A metabolomics approach for monitoring thermal processing temperature of bovine milk using ultra-performance liquid chromatography tandem Q-Exactive mass spectrometry and multivariate data analysis

A SIMPLE AND FLEXIBLE TEST OF SAMPLE EXCHANGEABILITY WITH APPLICATIONS TO STATISTICAL GENOMICS.

In scientific studies involving analyses of multivariate data, basic but important questions often arise for the researcher: Is the sample exchangeable, meaning that the joint distribution of the sample is invariant to the ordering of the units? Are the features independent of one another, or perhaps the features can be grouped so that the groups are mutually independent? In statistical genomics, these considerations are fundamental to downstream tasks such as demographic inference and the construction of polygenic risk scores. We propose a non-parametric approach, which we call the V test, to address these two questions, namely, a test of sample exchangeability given dependency structure of features, and a test of feature independence given sample exchangeability. Our test is conceptually simple, yet fast and flexible. It controls the Type I error across realistic scenarios, and handles data of arbitrary dimensions by leveraging large-sample asymptotics. Through extensive simulations and a comparison against unsupervised tests of stratification based on random matrix theory, we find that our test compares favorably in various scenarios of interest. We apply the test to data from the 1000 Genomes Project, demonstrating how it can be employed to assess exchangeability of the genetic sample, or find optimal linkage disequilibrium (LD) splits for downstream analysis. For exchangeability assessment, we find that removing rare variants can substantially increase the -value of the test statistic. For optimal LD splitting, the V test reports different optimal splits than previous approaches not relying on hypothesis testing. Software for our methods is available in R (CRAN: flintyR) and Python (PyPI: flintyPy).

LIBS for prospecting and Raman spectroscopy for monitoring: two feasibility studies for supporting in-situ resource utilization

Laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy are still rather new techniques for in-situ exploration of extraterrestrial planetary surfaces but have shown their suitability and great potential in several successful robotic missions already. Next to serving primary scientific applications, both methods can also be used in the context of in-situ resource utilization (ISRU) such as scouting for wanted substances and the surveillance of extraction processes. Here, we present two laboratory studies conducted in the context of ISRU with a focus on the chain from prospecting to extracting oxygen from lunar regolith. For LIBS, with optimized data processing and combined with state-of-the-art multivariate data analysis approaches, we show the potential of the technique for identifying samples with increased ilmenite content and for elemental quantification. The measurements were done using lunar regolith simulant and low pressures simulating vacuum on atmosphereless bodies such as the Moon. With Raman spectroscopy, we analyzed lunar regolith simulant samples that underwent electrochemical alteration for oxygen extraction and production of metal alloys demonstrating the potential of Raman spectroscopy for ISRU process monitoring. We also discuss the results in a broader context, evaluating the potential of both methods for other aspects of ISRU support.

Metabolic diversity, biosynthetic pathways, and metabolite biomarkers analysed via untargeted metabolomics and the antioxidant potential reveal for high temperature tolerance in tomato hybrid

High-temperature tolerant varieties/hybrids of tomato overcome stress-related changes due to their intrinsic cellular metabolic capabilities. We have used untargeted metabolite profiling and comparative biochemical and biological analyses of high temperature tolerant (HTT) tomato hybrid VRNTH18283 and sensitive (HTS) VRNTH19072 to decipher altered metabolic pathways and biomarker metabolites which were linked with enhanced antioxidant activity in HTT fruits. Parameters like fruit weight, yield per plant and key biochemical determinants viz. total soluble sugar (TSS), titratable acidity, ascorbic acid and lycopene content were significantly high in HTT hybrid compared to HTS. Metabolomics revealed upregulated metabolite diversity in HTT fruit extract (11,453 m/z features) as compared to 8834 m/z features in HTS. Statistical analysis revealed 423 differentially up-regulated and 410 down-regulated metabolite features. Multivariate data analyses showed sample discrimination based on distinct phytochemical diversification. Functionally annotated metabolite features showed enrichment in metabolic pathways including linoleic and linolenic acid metabolism, monoterpenoids biosynthesis and degradation, cutin, suberin and wax biosynthesis, and sphingolipids metabolism. Metabolite features were categorized in benzamides, amino acids, sugars, nucleotides, sterols, glycosides, phenylacetaldehydes, indoles, tryptamines and organic acids as major compound classes. Among 22 potentially discriminatory metabolites obtained from OPLS-DA model, all including prominently abundant solasodine, thiamine diphosphate, L-tryptophan, folic acid, lycopene, dihydrozeatin, myricetin, tomatidine, chlorogenic acid and α-tocotrienol were classified as metabolite biomarkers and were correlated with enhanced antioxidant activity. The study revealed that the altered physicochemical profile, increased metabolite compositional diversity and discriminatory metabolite biomarkers are liable for improved performance of the hybrid HTT VRNTH18283 against the negative impact of high temperature in comparison to HTS VRNTH19072.

Urban Indian Environment in the Context of a Pandemic

The spread of the Coronavirus disease 2019 (COVID-19) has impacted human life severely since November 2019. The urban centers in the world, especially, were highly affected by the diseases. Several socioeconomic and environmental factors probably enhanced the spread of the pandemic and consequent mortality. Many studies examining environmental factors, such as air quality, in urban centers indicate the roles of those factors in the spread of diseases and consequent mortality. However, other socioeconomic factors that directly or indirectly elevate the mass death of people are seldom studied. The present study explores the socioeconomic factors and air quality influencing COVID-19 deaths in urban India. We randomly selected 19 Indian cities and collected each city’s socioeconomic and air quality data from reliable and open sources. The data were analyzed using multivariate data analysis techniques using R statistics. The results showed significant positive relationships, population, and total area of the urban centers.