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Evaluating the efficiency of wastewater treatment plants (WWTPs) and their impact on receiving surface water bodies is a complex and highly significant task due to its regulatory implications for both environmental and public health. The monitoring of many water quality parameters related to the compliance of treated wastewater with environmental standards has led to the development of a unitless metric, the Wastewater Quality Index (WWQI), which serves as a practical tool for regulatory authorities. The aim of this research is to propose an appropriate WWQI methodology, incorporating a set of water quality indicators and a weighting approach, to evaluate wastewater effluents under operational monitoring. In this study, WWQI was successfully applied to access the operation of 21 WWTPs’ effluents within a single monitoring campaign, outside the mandatory monitoring schemes. The WWQI was computed for physical-chemical parameters including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), electrical conductivity (EC) and pH, priority substances (Cd, Ni and Pb) and a specific contaminant (Cr) using the weighted approach in the WWQI calculation, based on equal weighting, expert judgement and PCA weighing using factor loadings. The three approaches give similar results for the calculated WWQI. The expert judgment approach is more suitable for evaluating WWTP performance during a single monitoring campaign due to its simplicity compared to the PCA-based approach and its ability to prioritize specific water quality parameters over an equal weightage method.

Cancer-associated fibroblast (CAF)s in the tumour microenvironment (TME) modulate the extracellular matrix, interact with cancer cells, and facilitate communication with infiltrating leukocytes, significantly contributing to cancer progression and therapeutic response. In prostate cancer (PCa), CAFs promote malignancy through metabolic rewiring, cancer stem cell regulation, and therapy resistance. Pre-clinical studies indicate that targeting amino acid metabolism, particularly glutamine (Gln) metabolism, reduces cancer proliferation and stemness. However, most studies lack the context of CAF–cancer interaction, focusing on monocultures. This study assesses the influence of CAFs on PCa growth by manipulating Gln metabolism using colour-labelled PCa cell lines (red) and fibroblast (green) in a co-culture system to evaluate CAFs’ effects on PCa cell proliferation and clonogenic potential. CAFs increased the proliferation of hormone-sensitive LNCaP cells, whereas the castration-resistant C4-2 cells were unaffected. However, clonogenic growth increased in both cell lines. Gln deprivation and GLS1 inhibition experiments revealed that the increased growth rate of LNCAP cells was associated with increased dependence on Gln, which was confirmed by proteomic analyses. Tissue analysis of PCa patients revealed elevated GLS1 levels in both the PCa epithelium and stroma, suggesting that GLS1 is a therapeutic target. Moreover, the median overall survival analysis of GLS1 expression in the PCa epithelium and stroma identified a “high-risk” patient group that may benefit from GLS1-targeted therapies. Therefore, GLS1 targeting appears promising in castration-resistant PCa patients with high GLS1 epithelium and low GLS1 stromal expression.

Dendrobium stem is a valuable food with medicinal and edible properties. Due to its high medicinal value and price, closely related Dendrobium varieties are often sold as imitations on the market. Therefore, there is an urgent need to develop new methods that can quickly identify Dendrobium stem and its closely related species. The Heracles Neo ultra-fast gas phase electronic nose was used in this study to determine and analyze the composition and contents of volatile organic compounds (VOCs) in Dendrobium stem and samples of five other species closely related to it. A total of 20 VOCs were identified, and a fingerprint map of the VOCs was constructed. Principal component analysis (PCA), Euclidean distance, and other methods were used to comprehensively process and analyze the obtained VOC information. The AroChemBase database was also used for qualitative analysis of the compounds. The results showed that there are significant differences in the odor fingerprint spectra of Dendrobium stem and the five other closely related species. The main types of compounds in Dendrobium stem and its five closely related species were organic esters, aldehydes, ketones, and olefins. Among them, 3-methylbutanal and n-butanol were characteristic compounds of the Dendrobium stem sample, while the VOCs acetonitrile and trometamol were present in the five related Dendrobium species samples. The Heracles Neo ultra-fast gas phase electronic nose can quickly and accurately identify Dendrobium stem and its five closely related species. It can also be used for the quality evaluation of Dendrobium stem, providing a theoretical reference for reducing the phenomenon of medicinal confusion in the Dendrobium stem market.

The complexity of quality function deployment (QFD) matrices often hinders efficient decision-making in product design, leading to missed opportunities and extended development times. This study explores the integration of principal component analysis (PCA) with analytic hierarchy process-QFD (AHP-QFD) to address these challenges. PCA, a machine learning technique, was applied to QFD matrices from product design research to reduce complexity and enhance prioritization efficiency. The integrated method was tested with a product design team across various industries, including logistics, healthcare, and consumer electronics. The analysis demonstrated that PCA effectively reduced matrix complexity, optimizing feature prioritization. In the logistics sector, PCA explained 99.2% of the variance with the first five components, while in consumer electronics, it accounted for 86.9% with the first four components. However, PCA showed limitations in the healthcare sector due to evenly distributed variance among components. Expert feedback highlighted the practical benefits of the integrated approach: 75% of logistics experts and 62.5% of consumer electronics experts found the method clearer. For speed, 100% of logistics and 87.5% of consumer electronics experts preferred the method for quicker evaluations. For accuracy, 75% of logistics and 62.5% of consumer electronics experts deemed the method more accurate. Overall, the PCA-AHP-QFD method simplifies decision-making processes and reduces development time, particularly in industries where feature prioritization is crucial. These findings underscore the potential of the integrated approach to enhance product development efficiency and feature prioritization, with suitability varying based on industry characteristics.

In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization.

The Changzhou ship lock is approaching its capacity limit. In order to quantitatively analyze the influencing factors that restrict the capacity of the Changzhou ship lock, this study proposes an influencing factor analysis method based on principal component analysis (PCA). This method estimates the confidence interval of ship passing time by fitting a lognormal distribution curve, eliminates redundancy in navigability data by combining the hydrological data and cargo load data, and quantitatively analyzes the influencing factors of ship lock capacity under saturated operating conditions. The results show that the influencing factors of Changzhou ship lock capacity are classified according to their influence contribution rate as minimum water depth above the lock sill, operation direction, ship dimensions, draft, loading capacity, and actual load. The research results can provide a theoretical basis for improving the ship lock capacity and have application value for lock scheduling management.

This study investigates the potential of 2-(4-butylbenzyl)-3-hydroxynaphthalene-1,4-dione (11) and its 12 derivatives as anticancer and biofilm formation inhibitors for methicillin-resistant staphylococcus aureus using in silico methods. The study employed various computational methods, including molecular dynamics simulation molecular docking, density functional theory, and global chemical descriptors, to evaluate the interactions between the compounds and the target proteins. The docking results revealed that compounds 9, 11, 13, and ofloxacin exhibited binding affinities of −7.6, −7.9, −7.5, and −7.8 kcal mol−1, respectively, against peptide methionine sulfoxide reductase msrA/msrB (PDB: 3E0M). Ligand (11) showed better inhibition for methicillin-resistant staphylococcus aureus msrA/msrB enzyme. The complex of the 3E0M-ligand 11 remained highly stable across all tested temperatures (300, 305, 310, and 320 K). Principal Component Analysis (PCA) was employed to evaluate the behavior of the complex at various temperatures (300, 305, 310, and 320 K), demonstrating a total variance of 85%. Convergence was confirmed by the eigenvector’s cosine content value of 0.43, consistently displaying low RMSD values, with the minimum observed at 310 K. Furthermore, ligand 11 emerges as the most promising candidate among the compounds examined, showcasing notable potential when considering a combination of in vitro, in vivo, and now in silico data. While the naphthoquinone derivative (11) remains the primary candidate based on comprehensive in silico studies, further analysis using Frontier molecular orbital (FMO) suggests while the Egap value of compound 11 (2.980 eV) and compound 13 (2.975 eV) is lower than ofloxacin (4.369 eV), indicating their potential, so it can be a statement that compound 13 can also be investigated in further research.

Abstract The physicochemical characteristics of water samples from Lake Hawassa was determined with the aim of pointing out possible impacts of industrial effluents, agricultural chemicals and domestic sewage on the water quality of the lake. For this, a total of 15 physicochemical parameters were measured in 72 water samples collected from four different locations on the lake that are adjacent to areas involved in various human activities including agriculture (Tikur Wuha), resort hotel (Haile Resort), public recreation (Gudumale) and referral hospital (Hitita). Samples were collected over a period of six months covering the dry and wet seasons in 2018/19. One-way analysis of variance revealed the presence of significant difference in the physicochemical quality of the lake’s water across the four study areas and the two seasons. Principal component analysis identified the most discriminating characteristics that differentiate the studied areas according to the nature and level of pollution status. Tikur Wuha area was found to be characterized by high levels of EC and TDS, the values of these parameters were about twice or more than that measured in the other areas. This was ascribed to contamination of the lake by runoff water from the surrounding farmlands. On the other hand, the water around the other three areas was characterized by high nitrate, sulfate and phosphate. Hierarchical cluster analysis classified the sampling areas in to two groups, where Tikur Wuha constituted one group and the other three locations the second group. Linear discriminant analysis provided 100% correct classification of the samples into the two cluster groups. The measured values of turbidity, fluoride and nitrate were found to be significantly higher than the standard limits set by national and international guidelines. These results show that the lake has been facing serious pollution problems from various anthropogenic activities.

This paper presents a model for assessing the impact of various factors on maritime accidents. This paper discusses the issue of maritime transport and its risks. The taxonomy of causes and consequences used in maritime transport is explained. Two mathematical principles were used, i.e., multi-criteria decision analysis (MCDA) and principal component analysis (PCA). The analyses were carried out using the Promethee/Gaia method. The connection between causes and accidents constituted the decision problem. The evaluation criteria, a set of actions and preferences, were identified based on the method principles. The results of this research include rankings of causal chains. They were analysed in multi-criteria and single-criteria dimensions. The outcomes are presented numerically and graphically. Such research contributes to improving safety at sea. It allows us to understand how a particular transport system works. Conclusions can be drawn and measures can be initiated to change the situation in the future.

Hawthorn is a type of natural food with significant medicinal and nutritional properties; it has been listed in the “Both Food and Drug” list by the Chinese Ministry of Health Item List since 1997. However, hawthorn varieties have complex origins, and there are significant differences in the content, type, and medicinal efficacy of the chemically active ingredients in different varieties of hawthorn. This leads to the phenomenon of mixed varieties and substandard products being passed off as high-quality. In this work, by using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), we identified and analyzed volatile organic compounds (VOCs) in four varieties of hawthorn, establishing their characteristic fingerprints. As a result, a total of 153 peaks were detected, and 139 VOCs were also identified. As shown by the fingerprint profiles, the different hawthorn samples contained different VOCs. Meanwhile, by using principal component analysis (PCA), Euclidean distance, and partial least-squares discriminant analysis (PLS-DA), the relationship between the VOCs found in the different varieties of hawthorn was revealed. This study developed a simple, fast, accurate, and sensitive method for identifying, tracking, and evaluating hawthorn varieties.

Aim: The entire world population is increasingly shifting to the use of plant-based medicines either as supplementation or medication. These medicinal plants absorb trace elements and metals available in the soil solutions in ionic, complexed, or chelated forms and can either be deficient or toxic for human and animal consumption. Therefore, screening these medicinal plants for trace elements or metals is important according to the recommendation of WHO and European legislation (1881/2006/EC). Methodology: In this study, the concentrations of 12 trace elements and metals (Al, B, Ba, Be, Bi, Ga, K, Li, Mg, Se, Te, and Tl) were determined from Kigelia africana leaf and fruit extracts using 700 series Agilent inductively coupled plasma – optical emission spectrometer (ICP-OES) coupled with ultrasonic CETAC U-6000AT+ Nebulizer after microwave-assisted acid digestion of the plant samples with MDS-6G closed microwave digester. Results: The study revealed that K. africana extracts have significant trace elements with the fruit extracts having high concentrations of required trace elements (3.2 mgkg−1 Mg, 67.18 mgkg−1 K, 0.04 mgkg−1 Se, and 0.16 mgkg−1 Al). Interestingly, the fruit and leaf extracts also have the required amount of Boron (B), Bismuth (Bi), Gallium (Ga), and Lithium (Li) which are essential in human brain function, nervous system, and glucose metabolism among other therapeutic functions. Barium (Ba), Beryllium (Be), Tellurium (Te), and Thallium (Tl) are known toxic metals when accumulated in the human body and are found in low concentrations in this study. Conclusion: This study revealed that trace elements are positively correlated when analyzed with principal component (PCA) and hierarchical cluster (HCA) analyzes. This shows that K. africana plant extract can be considered one of the most important medicinal plants with several therapeutic benefits. The concentrations of several trace elements according to recommended dietary allowance (RDA) as well as the correlation between these metals and metalloids are reported for the first time using PCA and HCA.

Based on metabolomics, the metabolites of Jujube leaves LS1 (one bud and two leaves), LS2 (one bud and three leaves), and LS3 (one bud and four leaves) were taken and examined by ultra-high performance liquid chromatography-mass spectrometry technique. There were 22 substance categories that were identified. Principal component analysis was also utilized to distinguish the metabolomics at the three different foliar ages, and the results suggested that the samples at different foliar ages were clearly separated, demonstrating that the metabolites in the three foliar ages were significantly different. Through the screening of differential metabolites and hierarchical clustering analysis, our results suggested that the composition and the content distribution of the differential metabolites at three different foliar ages were significantly different. In the LS1, delphinidin, N-hydroxy tryptamine, serotonin, methylquercetin O-hexoside, tricin 7-O-hexoside, and eriodictyol C-hexoside were identified as the distinctive compounds. In the LS2, N-caffeoyl agmatine, lysoPC 18:3 (2n isomer), N-(4′-O-glycosyl)-p-coumaroyl-agmatine, dihydromyricetin, and hydroxy-methoxycinnamate were identified as the distinctive compounds. Similarly, the 3-O-p-coumaroyl-quinic acid, O-feruloyl 4-hydroxylcoumarin, isorhamnetin 3-O-neohesperidoside, cyanidin 3-O-galactoside, quercetin O-acetylhexoside, and DIMBOA glucoside were identified as the distinctive compounds in LS3. These characteristic compounds could provide a strong theoretical basis for rapid identification of jujube leaves at different foliar ages.

The precise allocation of weights to criteria plays a fundamental role in multicriteria decision-making, exerting a significant influence on the obtained results. Ensuring an appropriate weighting of criteria is crucial for conducting a fair and accurate evaluation of various alternatives. In this context, we present an innovative solution that addresses the allocation of weights to attributes in datasets, aiming to overcome limitations and challenges associated with expert consultation in multicriteria problems. The proposed method is grounded in an objective approach and adopts a hybrid perspective by integrating the mathematical principles of Principal Component Analysis with the application of the Entropy Method. This method was implemented along with the exponential weighted sum model in a case study related to the classification of neighborhoods in Mexico City based on the level of marginalization. Results were compared with the marginalization index reported in official sources, using evaluation metrics MAE and MAPE with values of 0.24 and 11.3%, respectively. This research demonstrates the efficiency of the proposed method, which integrates techniques used for attribute weighting, providing a robust and reliable tool for decision-making.

Puerariae Radix is one of the most widely used ancient traditional Chinese medicines and is also consumed as food, which has rich edible and medicinal value. Puerariae Radix can be divided into Puerariae Lobatae Radix (PL) and Puerariae Thomsonii Radix (PT). These two medicinal materials are very similar, and they are often mixed up or misused. In this study, gas chromatography–ion migration spectrometry (GC-IMS) was used to analyze the volatile organic compounds (VOCs) of PL and PT, and the differences in VOCs were analyzed using fingerprint, principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that a total of 173 VOCs were obtained from PL and PT, and 149 were qualitatively identified, including 38 aldehydes, 22 alcohols, 22 ketones, 19 esters, 13 esters, 10 acids, 10 pyrazines, 6 terpenes, 4 furans, and 2 pyridines. The characteristic VOCs of PL and PT were clarified by constructing GC-IMS fingerprints. PL and PT can be effectively distinguished, and five characteristic VOCs were screened using PCA and OPLS-DA analysis methods. This study identified and evaluated the types and differences in VOCs in PL and PT. The established method is simple, rapid, accurate, and sensitive, and it provides theoretical guidance for the identification, tracing, and quality evaluation of PL and PT.

Attenuated total reflection-Mid-Fourier transform-infrared (ATR-Mid-FT-IR) spectroscopy combined with principal component analysis (PCA) has been applied for the discrimination of amorphous solid dispersion (ASD) of kaempferol with different types of Eudragit (L100, L100-55, EPO). The ASD samples were prepared by ball milling. Training and test sets for PCA consisted of a pure compound, physical mixture, and incomplete/complete amorphous solid dispersion. The obtained results confirmed that the range 400–1700 cm−1 was the major contributor to the variance described by PC1 and PC2, which are the fingerprint region. The obtained PCA model selected fully amorphous samples as follows: five for KMP-EL100, two for KMP-EL100-55, and six for KMP-EPO (which was confirmed by the XRPD analysis). DSC analysis confirmed full miscibility of all ASDs (one glass transition temperature). FT-IR analysis confirmed the formation of hydrogen bonds between the –OH and/or –CH groups of KMP and the C=O group of Eudragits. Amorphization improved the solubility of kaempferol in pH 6.8, pH 5.5, and HCl 0.1 N.

Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood–brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.

IntroductionSoil quality deterioration with the introduction of modern agriculture is a major threat to agricultural sustainability and food security and the problem is more aggravated specially under rainfed agriculture. Asessment of soil quality is a tortuous task as it can not be measured directly. The objective of the present investigation was to evaluate the effect of long-term fertilization and manuring on soil quality and identify the most sensitive indicators of assessing soil quality under rainfed rice based system.MethodsSoil samples were collected from selected six treatments viz. control, 100%NPK, 50%NPK, 50%FYM, 100%FYM and 50%NPK+FYM of 34 years old long-term fertilizer experiment with rainfed rice-lentil cropping system situated at BHU Varanasi, India.Results and discussionResult revealed that continuous organic manure application along with inorganic fertilizer increased soil organic carbon by 54.1% over control treatment.Principal component analysis (PCA) was done to screen out key indicators and mean weight diameter, available Fe, available N, potentially mineralizable N, available Zn, FDA hydrolase activity and Clay were selected as key indicators of soil quality. The highest soil quality index (SQI) of 0.95 was found in 50% NPK+FYM treatment. Regression analysis showed better agreement of equivalent rice yield and SQI (0.87). Therefore, the balanced fertilization with organic and inorganic fertilizers is important for sustainability of the rainfed rice-lentil cropping system and this practice may be recommended for rainfed rice based system of Indian Inceptisol.

Orthosiphon aristatus (called kumis kucing in Indonesia) is a valuable herb for diabetes mellitus treatment. In this study, LC-MS/MS and PCA analyses were used to investigate the metabolite profile, classify O. aristatus extracts, and assess the inhibitory activity of a-glucosidase and the probable bioactive compounds through in silico study. Results showed that the methanol and ethanol extracts of O. aristatus were active in α-glucosidase inhibitory activity. Both extracts contained 86 compounds as known from the LC-MS/MS analysis. PCA analysis identified 10 metabolites that correlated with α-glucosidase inhibitory activity. Results of in silico analysis obtained rosmarinic acid compound potentially act as anti-diabetic activity, which can be developed for further research.

This study investigates the applicability of Principal Component Analysis (PCA) for distinguishing construction materials. The approach enhances data presentation, revealing distinct clusters and variable impacts on materials. This perspective provides valuable insights into concrete materials, guiding materials science and engineering practices. Our findings show the capacity of PCA to show a clear distinction between concrete and non-concrete composites. Compressive strength significantly affects certain composites, being influenced by aerogel loading. The peculiar role of aerogel density among the other factors is attributed to their possession of the smallest thermal conductivity. To address moderate total variance of PCA, segregation into concrete (C) and non-concrete (NC) categories is explored, offering a more robust distinction and higher clustering. Concrete materials show higher variance, emphasizing the effectiveness of the segregation approach. PCA highlights aerogel density’s influence on thermal conductivity on concrete materials. For non-concrete materials, a moderately higher variance is noted, emphasizing the critical role of aerogel-related properties (size and density). These findings underscore the importance of aerogel characteristics in shaping material behaviour.

Organic Medium Enclosed Trough (OMET) system is a new non-drainable growing technique. The study set out to investigate the interactive effects of OMET and Amaranth species variation on some growth attributes, yield, bioactive compounds, and nutrients. Three Amaranth species, A. caudatus, A. cruentus, and A. tricolor, were grown in OMET and non-OMET growing conditions. Growth attributes, irrigation water, bioactive compounds, and nutrients were determined. Non-OMET growing conditions and A. cruentus showed the highest total phenolics and flavonols followed by A. caudatus and A. tricolor, whilst tannins were predominant in A. caudatus followed by A. cruentus and A. tricolor. The use of unsupervised PCA showed major metabolic variation based on Amaranth species and the use of supervised OPLS-DA showed clear metabolic variation based on OMET and non-OMET. OMET and A. cruentus showed the highest plant height, stem diameter, biomass, and some nutrients (%protein, Ca, P, Cu, Se, Zn), followed by A. caudatus based only on growth and biomass, and A. tricolor based only on some nutrients (Mg, K, Mn, and Fe). The use of the OMET system on A. cruentus and A. tricolor can be recommended to mitigate climate change effects on nutritional composition using relatively low levels of irrigation water.

Nowadays, nutritional foods have a great impact on healthy diets. In particular, maca, oatmeal, broad bean, soybean, and algarrobo are widely used in different ways in the daily diets of many people due to their nutritional components. However, many of these foods share certain physical similarities with others of lower quality, making it difficult to identify them with certainty. Few studies have been conducted to find any differences using practical techniques with minimal preparation and in short durations. In this work, Principal Component Analysis (PCA) and Near Infrared Spectroscopy (NIR) were used to classify and distinguish samples based on their chemical properties. The spectral data were pretreated to further highlight the differences among the samples determined via PCA. The results indicate that the raw spectral data of all the samples had similar patterns, and their respective PCA analysis results could not be used to differentiate them. However, pretreated data differentiated the foods in separate clusters according to score plots. The main difference was a C-O band that corresponded to a vibration mode at 4644 cm−1 associated with protein content. PCA combined with spectral analysis can be used to differentiate and classify foods using small samples through the chemical properties on their surfaces. This study contributes new knowledge toward the more precise identification of foods, even if they are combined.

Malta, a country renowned for its honey, has not been extensively mentioned in studies based on honey. In addition to many parameters, the collection period affects honey quality, precisely due to the different floral composition that exists during a certain season. Therefore, the significance of this study refers to the provision of data on honey from Malta collected during the autumn, spring, and summer seasons. Melissopalynological analysis, determination of physicochemical parameters, and the use of analytical chromatographic methods enabled detailed analysis of this honey. Principal component analysis (PCA) provided the differentiation of Maltese honey depending on the harvest season. Lotus pollen, followed by Eucalyptus, predominated in all honey samples. Characteristic compounds for summer honey were pinocembrin, galangin, kaempferol, chrysin, p-hydroxybenzoic acid, vanillic acid and maltotriose, while quercetin 3-O-galactoside, ferulic acid, ellagic acid, protocatechuic acid, luteolin 7-O-glucoside and melibiose were specific for autumn honey. A higher amount of p-coumaric acid, genistein, catechin, as well as the content of many sugars were found in spring samples. To the best of our knowledge, this is the first scientific work dealing with a detailed chemical analysis of Maltese honey

The application of high performance thin layer chromatography (HPTLC)-based non-targeted metabolomics as a holistic approach to compare fingerprints of metabolite changes during Artemisia annua development is described. Images of HPTLC chromatograms obtained after derivatization with anisaldehyde-sulphuric acid reagent were used as a dataset for multivariate analysis. Principal component analysis and orthogonal partial least squares discriminant analysis confirmed the differentiation of samples belonging to vegetative phase, flowering stage, and seed formation stage of the plant development. The obtained results showed that the HPTLC-based metabolomics approach can be a very reliable technique for the investigation of metabolic changes during plant development, complementary to gas chromatography–mass spectrometry and nuclear magnetic resonance-based metabolomics.

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure–Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.

Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.

Androgen deprivation therapy (ADT) has been the mainstay of prostate cancer (PCa) treatment, with success in developing more effective inhibitors of androgen synthesis and antiandrogens in clinical practice. However, hormone deprivation and AR ablation have caused an increase in ADT-insensitive PCas associated with a poor prognosis. Resistance to ADT arises through various mechanisms, and most castration-resistant PCas still rely on the androgen axis, while others become truly androgen receptor (AR)-independent. Our research identified the human tousled-like kinase 1 (TLK1) as a crucial early mediator of PCa cell adaptation to ADT, promoting androgen-independent growth, inhibiting apoptosis, and facilitating cell motility and metastasis. Although explicit, the growing role of TLK1 biology in PCa has remained underrepresented and elusive. In this review, we aim to highlight the diverse functions of TLK1 in PCa, shed light on the molecular mechanisms underlying the transition from androgen-sensitive (AS) to an androgen-insensitive (AI) disease mediated by TLK1, and explore potential strategies to counteract this process. Targeting TLK1 and its associated signaling could prevent PCa progression to the incurable metastatic castration-resistant PCa (mCRPC) stage and provide a promising approach to treating PCa.

Kyasanur Forest Disease virus (KFDV), a neglected human pathogenic virus, is a Flavivirus that causes severe hemorrhagic fever in humans. KFDV is transmitted to humans by the bite of the hard tick (Haemaphysalis spinigera), which acts as a reservoir of KFDV. The recent expansion of the endemic area of KFDV is of concern and requires the development of new preventive measures against KFDV. Currently, there is no antiviral therapy against KFDV, and the existing vaccine has limited efficacy. To develop a new antiviral therapy against KFDV, we focused on the nonstructural proteins NS2B and NS3 of KFDV, which are responsible for serine protease activity. Viral proteases have shown to be suitable therapeutic targets in the development of antiviral drugs against many diseases. However, success has been limited in flaviviruses, mainly because of the important features of the active site, which is flat and highly charged. In this context, the present study focuses on the dynamics of NS2B and NS3 to identify potential allosteric sites in the NS2B/NS3 protease of KDFV. To our knowledge, there are no reports on the dynamics of NS2B and NS3 in KFDV, and the crystal structure of the NS2B/NS3 protease of KFDV has not yet been solved. Overall, we created the structure of the NS2B/NS3 protease of KFDV using AlphaFold and performed molecular dynamics simulations with and without NS2B cofactor to investigate structural rearrangements due to cofactor binding and to identify alternative allosteric sites. The identified allosteric site is promising due to its geometric and physicochemical properties and druggability and can be used for new drug development. The applicability of the proposed allosteric binding sites was verified for the best-hit molecules from the virtual screening and MD simulations.

Kopi merupakan bahan minuman yang sangat terkenal bukan hanya di Indonesia melainkan di seluruh dunia, jenis yang sering dijumpai yaitu arabika dan robusta. Tingginya harga dan permintaan kopi banyak produsen memalsukan atau mencampur kopi dengan bahan lain. Adulterasi adalah upaya menambah atau mengganti bahan makanan dengan tujuan memperoleh, sehingga memberikan dampak buruk pada konsumen. Tujuan dari jurnal ini adalah untuk mengetahui ada atau tidaknya adulteran pada kopi luwak yang beredar dipasaran. Metode analisis FTIR digunakan untuk membuat pola sidik jari dari ekstrak kopi melalui analisis kemometrik dengan metode Principal Component Analysis (PCA). Ekstraksi dilakukan dengan cara maserasi menggunakan pelarut etanol 96%. Pengukuran spektrum inframerah menggunakan alat FT-IR, pada bilangan gelombang 4000-650cm-1 dan resolusi 4 cm-1. Klasifikasi dari kopi yang diadulteran dengan arabika dan kopi luwak menggunakan data PC-1 dan PC-2 dengan nilai berturut-turut 82% dan 14%. Hasil menunjukkan nilai scores menggunakan PC-1 dan PC-2 sampel kopi A berada dekat kuadran kopi luwak, sampel kopi B berada di antara kuadran kopi arabika (adulteran) dan luwak, dan kopi sampel C berada dekat kuadran arabika (adulteran). Metode FTIR dapat mendeteksi dengan batas deteksi 15% (b/b)

Previous researches showed that fatty acids analysis might be a useful tool to support the taxonomic investigation. In this approach the fatty acids content of ten Malvoideae species was analyzed and its chemotaxonomic significance has been investigated. The aerial parts of the species were collected in the Northeast of Brazil and their fatty acid methyl esters were analyzed by gas chromatography with flame ionization detector. The chemometric analysis consisted of principal component analysis (PCA) and hierarchical clustering analysis (HCA) with the euclidean distance between the samples given by the Ward.D2 algorithm. This is the first report of fatty acids from Wissadula peripocifolia, Herissantia crispa, Bakeridesia pickelii, Sidastrum micranthum, Pavonia cancellata and Pavonia malacophylla. The results showed the predominance of palmitic (C16:0), oleic (C18:1) and linoleic (C18:2) acids in the studied species. By the PCA and HCA analysis, the fatty acid composition was able to distinguish the species Herissantia crispa and Pavonia malacophylla. Our findings showed a chemotaxonomic proximity among species from different genera reflecting the taxonomic and phylogenetic closeness previously demonstrated by molecular investigations on Malvoidae species. Furthermore, our results demonstrated that the fatty acid analysis may be an interesting tool to support the taxonomic investigations on Malvoideae species.

The aim of this work was to investigate the volatile compositions of four Chinese functional liquors. For this purpose, volatile compounds of four liquors were extracted with head-space solid-phase microextraction (HS-SPME) and analyzed with gas chromatography-mass spectrometry (GC-MS) along with the determination of odor activity value (OAV) and relative odor contribution (ROC). Sixty volatiles were tentatively identified and categorized into the following seven groups: alcohols, esters, fatty acids, carbonyl compound, hydrocarbons, phenols, and other components. The differences in chemical composition of volatile compounds were visualized with heat maps. Odorants were compared with different samples using a statistical analysis of Venn diagrams and a multivariate principal component analysis, and ethyl hexanoate, ethyl acetate, and ethyl octanoate were found to be the key odorants. Besides, abundant phenolic contents and high antioxidant ability of four Chinese functional liquors could potentially bring better health-boosting effects.

Andrographis paniculata is one of the medicinal plants used for the treatment of antidiabetic. Cultivation ages and solvent extraction affected metabolites' composition and concentration that directly cause the plant's efficacies. This research aimed to distinguish A. paniculata based on cultivation ages and solvent extraction using data fusion of UV-Vis and FTIR spectra combined with principal component analysis (PCA). A. paniculata with 2, 3, and 4 months post-planting were extracted by water, 50% ethanol, 70% ethanol, and ethanol. In each extract, we measured UV-Vis and FTIR spectra. Then, we used the data fusion from both spectra. We used UV-Vis and FTIR absorbance from 200–400 nm and 1800–400 cm–1, respectively. Each extract gives a similar pattern of UV-Vis and FTIR spectra, only differ in their intensities. PCA score plot in two and three-dimensional showed A. paniculata extracts could be distinguished based on cultivation ages and solvent extraction with a total variance of 86 and 92%, respectively. Furthermore, this study confirms the data fusion of UV-Vis and FTIR spectra could distinguished A. paniculata extracts combined with chemometrics based on cultivation ages and solvent extraction.

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R2 = 0.691, cross-validation parameter Q2cv = 0.528 and the external validation parameter R2test = 0.794.

Prostate cancer (PCa) continues to rank as the second leading cause of cancer-related mortality in western countries, despite the golden treatment using androgen deprivation therapy (ADT) or anti-androgen therapy. With decades of research, scientists have gradually realized that the existence of prostate cancer stem cells (PCSCs) successfully explains tumor recurrence, metastasis and therapeutic failure of PCa. Theoretically, eradication of this small population may improve the efficacy of current therapeutic approaches and prolong PCa survival. However, several characteristics of PCSCs make their diminishment extremely challenging: inherent resistance to anti-androgen and chemotherapy treatment, over-activation of the survival pathway, adaptation to tumor micro-environments, escape from immune attack and being easier to metastasize. For this end, a better understanding of PCSC biology at the molecular level will definitely inspire us to develop PCSC targeted approaches. In this review, we comprehensively summarize signaling pathways responsible for homeostatic regulation of PCSCs and discuss how to eliminate these fractional cells in clinical practice. Overall, this study deeply pinpoints PCSC biology at the molecular level and provides us some research perspectives.

This study focuses on faults in the thrusters of unmanned surface vehicles, which are fatal to the integrity of their missions. As for the fault conditions, the breakage of the thruster blade and the entanglement of floating objects were selected, and a data-driven method was used to diagnose the faults. In the data-driven method, it is important to select the sensitive fault feature. In this study, vibration, current consumption, rotational speed and input voltage were selected as fault features. An experiment was conducted in an engineering water tank to obtain and analyze data on fault conditions to verify the validity of the selected features. In addition, a new fault diagnosis algorithm combining principal component analysis and Shannon entropy was applied for analyzing the correlations among fault features. This algorithm reduces the dimensionality of data while preserving their structure and characteristics, and diagnoses faults by quantifying entropy values. A fault is detected by comparing the entropy value and a predetermined threshold value, and is diagnosed by analyzing the entropy value and visualized 2D or 3D principal component results. Moreover, the fault diagnosis performance of the unmanned surface vehicle’s thruster was verified by analyzing the results for each fault condition.

Artificial intelligence applications are becoming increasingly popular and are producing better results in many areas of research. The quality of the results depends on the quantity of data and its information content. In recent years, the amount of data available has increased significantly, but this does not always mean more information and therefore better results. The aim of this work is to evaluate the effects of a new data preprocessing method for machine learning. This method was designed for sparce matrix approximation, and it is called semi-pivoted QR approximation (SPQR). To best of our knowledge, it has never been applied to data preprocessing in machine learning algorithms. This method works as a feature selection algorithm, and in this work, an evaluation of its effects on the performance of an unsupervised clustering algorithm is proposed. The obtained results are compared to those obtained using, as preprocessing algorithm, principal component analysis (PCA). These two methods have been applied to various publicly available datasets. The obtained results show that the SPQR algorithm can achieve results comparable to those obtained using PCA without introducing any transformation of the original dataset.

Seed purity has an important impact on the yield and quality of maize. Studying the spectral characteristics of hybrid maize and exploring the rapid and non-destructive detection method of seed purity are conducive to the development of maize seed breeding and planting industry. The near-infrared spectral data of five hybrid maize seeds were collected in the laboratory. After eliminating the obvious noises, the multiple scattering correction (MSC) was applied to pretreat the spectra. PLS-DA, KNN, NB, RF, SVM-Linear, SVM-Polynomial, SVM-RBF, and SVM-Sigmaid were used as pattern recognition methods to classify five different types of maize seeds. The recognition accuracy of the models established by different algorithms was 84.4%, 97.6, 100%, 96.4, 99.2%, 100%, 98.4%, and 91.2%, respectively. The results indicated that hyperspectral imaging technology could be used for variety classification and the purity detection of maize seeds. To improve the calculation speed, using the principal component analysis (PCA) to reduce the dimension of hyperspectral data, we then established classification models based on characteristic wavelengths. The recognition accuracy of the models established by different algorithms was 80.8%, 86.8%, 98%, 94%, 96.8%, 98.4%, 94.4%, and 88.2%, respectively. The results showed that the selected sensitive wavelengths could be used to detect the purity of maize seeds. The overall results indicated that it was feasible to use near-infrared hyperspectral imaging technology for the variety identification and purity detection of maize seeds. This study also provides a new method for rapid and non-destructive detection of seed purity.

The most important painter from Urbino in the last decades of the 15th century (1439 ab.–1494) was Giovanni Santi, the father of Raphael. The lack of scientific literature about Santi’s practice and the possible peculiar role of Urbino in the development of painting techniques in northern Italy suggested in-depth investigations of the entire corpus of his paintings. A well-established sequence of multispectral imaging, spectroscopic and microscopic investigations was performed on 24 wood panel paintings and 2 canvases attributed by most scholars to Giovanni Santi (1439 ab.–1494) to collect a large set of significant data. This systematic research allowed his painting technique to be defined, starting from the type of supports he used and from the features of the underdrawing, which quite frequently included characteristic regular hatching. The pigments used were widely investigated by means of ED-XRF and reflectance spectroscopy (vis-RS); a meaningful multivariate statistical method (PCA and HCA analysis) was also applied to the ED-XRF dataset acquired for representative hues. In particular, the vis-RS technique proved to be a simple and effective diagnostic tool to detect many pigments, including indigo, and to distinguish between two different types of red lakes, avoiding sampling and more complex analyses. Santi used lead white, Fe-Mn-based pigments, vermilion, red lake, natural blue ultramarine, azurite, copper-based green pigments (particularly verdigris), lead-tin yellow, scarcely ever orpiment and, in a few green mixtures, also indigo. Despite the palette being linked to tradition, the master appeared to introduce some peculiarities, such as the addition of glass powder, and mixing pigments both in a traditional way and using them to create chromatic effects unusual for his time. This research confirmed that the systematic use of the integrated non-invasive methods is highly representative, and the results of this wide diagnostic campaign provided a significant dataset which allowed the implementation of a scientific database related to central Italy Renaissance paintings and materials.

Seventeen water-soluble substances (of sodium, ammonium, potassium, magnesium, calcium, formate, methanesulfonate, glyoxylate, chloride, nitrite, nitrate, glutarate, succinate, malate, malonate, sulfate and oxalate) in 94 samples of particle matter in the ambient air, collected over ten months, in a suburb of Belgrade (Serbia), were determined by ion chromatography. To apportion the sources of the air pollution, the log-transformed data were processed by applying multivariate techniques. Principal component and factor analysis identified three main factors controlling the data variability: stationary combustion processes with the highest loadings of oxalate, malonate and malate; landfill emission and secondary inorganic aerosol characterized by high levels of ammonium, nitrate and sulfate; a contribution of mineral dust composed of magnesium, calcium and chloride. The hierarchical cluster analysis pointed out a differentiation of the samples into five groups belonging to different variables inputs. For the classification of ambient air samples using nine selected ions, the recognition ability of linear discriminant analysis, k-nearest neighbors, and soft independent modeling of class analogy were 87.0, 94.6, and 97.8 %, respectively. Time-series analysis showed that the traffic emission is more pronounced in winter in contrast to the mineral dust influence, while the effect of waste combustion exhibits no trend.

Oregano is one of the most used culinary herb and it is often adulterated with cheaper plants. In this study, GC–MS was used for identification and quantification of metabolites from 104 samples of oregano (Origanum vulgare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggygria) and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after the two-step derivatization, involving methoxyamination and silanization, were subjected to multivariate data analysis to reveal markers of adulteration and to build the regression models on the basis of the oregano-to-adulterants mixing ratio. Orthogonal partial least squares enabled detection of oregano adulterations with olive, Venetian sumac and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal partial least squares discriminant analysis enabled discrimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite.

Phytochemical study of the chloroform extract of the aerial parts of Inula germanica L., I. ensifolia L., I. conyza (Griess.) DC. and I. salicina L. led to the identification of 33 terpenoids. β- and α-amyrin, lupeol, taraxasterol, ψ-taraxasterol and their 3-O-acetates and 3-O-palmitates were identified by GC/MS. In addition, the structures of 3-O-palmitates of mainaladiol, arnidiol, faradiol and 16-hydroxylupeol were confirmed by NMR. ent-Kaur-16-en-19- -oic acid and its 15α-(3-methylpentanoyloxy) and 15α-(3-methylbutanoyloxy) derivatives were isolated from I. conyza. Ten closely related sesquiterpene lactones (germacranolides and melampolides) were found in I. germanica and their structural identification was performed by spectral analyses. I. ensifolia and I. salicina were free of sesquiterpene lactones and diterpenoids. All triterpenoids and diterpenoids, grazielia acid, desacetylovatifolin and 8-(2-methylbutanoyloxy)- 1(10),4,11(13)-germacrutrien-6,12-olide-14-oic acid are described for the first time in the studied species. The principal component analysis was used to find a relationship between thе investigated up to now Inula species, growing in Bulgaria.

Present study was intendant to assess heavy metals (HMs) concentration and associated health risk in processed fruits’ products sold in the local markets of North Pakistan. In total seven metals viz. cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb) and zinc (Zn) were quantified in 345 samples of different brands categorized into eight groups (Sauces, Ketchup, Juices, Jams, canned fruits, tomato paste, marmalades and pickles). On the comparative basis, Fe was dominating with highest concentration in pickles, canned fruits and sauces at 143.3 ± 43.2, 83.64 ± 23.19 and 50.17 ± 15.1 mg/kg, respectively), followed by Cd in sauces (22.94 ± 6.91 mg/kg), Cr in juices (12.97 ± 3.91 mg/kg) and Pb in pickles (12.53 ± 3.77 mg/kg). Measured levels of these metals varied significantly and were relatively higher than their permissible limits. Univariate and multivariate analysis depicted strong association among Cr, Co, Pb and Fe and confirmed HMs contamination through natural and anthropogenic sources in processed foods. Health risk index (HRI) for Cd, Cr and Pb was greater than unity (

Diabetes mellitus type 2 (DMT2) is a severe and complex health problem. It is the most common type of diabetes. DMT2 is a chronic metabolic disorder that affects the way your body metabolizes sugar. With DMT2, your body either resists the effects of insulin or does not produce sufficient insulin to continue normal glucose levels. DMT2 is a disease that requires a multifactorial approach of controlling that includes lifestyle change and pharmacotherapy. Less than ideal management increases the risk of developing complications and comorbidities such as cardiovascular disease and numerous social and economic penalties. That is why the studies dedicated to the pathophysiological mechanisms and the treatment of DMT2 are extremely numerous and diverse. In this study, exploratory data analysis approaches are applied for the treatment of clinical and anthropometric readings of patients with DMT2. Since multivariate statistics is a well-known method for classification, modeling and interpretation of large collections of data, the major aim of the present study was to reveal latent relations between the objects of the investigation (group of patients and control group) and the variables describing the objects (clinical and anthropometric parameters). In the proposed method by the application of hierarchical cluster analysis and principal component analysis it is possible to identify reduced number of parameters which appear to be the most significant discriminant parameters to distinguish between four patterns of patients with DMT2. However, there is still lack of multivariate statistical studies using DMT2 data sets to assess different aspects of the problem like optimal rapid monitoring of the patients or specific separation of patients into patterns of similarity related to their health status which could be of help in preparation of data bases for DMT2 patients. The outcome from the study could be of custom for the selection of significant tests for rapid monitoring of patients and more detailed approach to the health status of DMT2 patients.

Volatile oils from flowers and leaves of C. creticus L. and C. salviifolius L. were extracted by two extraction methods; namely, hydrodistillation and solid-phase micro-extraction (SPME). The chemical composition of essential oils was analyzed by GC and GC–MS. The volatile extracted from leaves and flowers of C. criticus using SPME was dominated by monoterpenes and sesquiterpenes hydrocarbon with α-pinene, camphene and α-cubebene as major components. In hydrodistillation, the oil extracted from leaves was dominated by oxygenated diterpenes and diterpenes hydrocarbon with manoyl oxide and sclarene as major components, whereas, the oil extracted from flowers was dominated by oxygenated diterpenes and diterpenes hydrocarbon with manoyl oxide and abietatriene as major components. The volatile from flowers and leaves of C. salviifolius obtained by SPME were dominated by monoterpenes and sesquiterpenes with δ-3-carene, α-pinene, β-pinene, and E-caryophyllene as major constituents. On the other hand, the oils from flowers and leaves of C. salviifolius obtained by hydrodistillation were dominated by oxygenated diterpenes, diterpenes hydrocarbon and esters with dehydro abietol, abietol, manoyl oxide and methyl octadecenoate as major components. In the leaves, the major components of the oil were manoyl oxide, E-ethyl cinnamate, and Z-ethyl cinnamate. These oils showed weak antioxidant activity when compared to the positive controls α-tocopherol, ascorbic acid, and EDTA, while the crude extracts aq. MeOH, butanol, and water showed good antioxidant activity. Discriminating between the studied plants based on the extraction method was also possible upon applying Principle component analysis (PCA) to the obtained GC–MS data.

Lignocellulosic biomass has great potential as a renewable energy source due to abundance both as vegetation and in residues from agriculture and forestry. It can be utilized for biofuels and for value-added chemicals and materials. The challenge concerning lignocellulosic biomass lies in its heterogenous nature. The chemical composition may vary according to species, location, harvest season and botanic fractions. Therefore, it is crucial to assess the composition prior to any biofuel conversion method. Vibrational spectroscopy has been used extensively for rapid predictions of the chemical properties of biomass. In this study, calibration models based on infrared and near-infrared spectra has been compared using the same calibration set, 36 samples comprising wood, bark, needles, and twigs of three different wood species. PLS was performed to correlate EMSC pretreated infrared and near-infrared spectra to the chemical contents of the samples. R2 for the standard curve of the infrared model is 0,896 and for the near-infrared standard curve 0,921, using 3 PLS components. The effect of heterogeneity was tested by comparing calibration models based on finely and coarsely ground sample, where the R2 of the coarsely ground sample was 0,825, lower than 0,896, but still significant. The results also show that there is more variation in the lignin content between the fractions from a single tree than between similar fractions from different species. The calibration models that have been developed will be useful for frequent, rapid determination of lignin content in wood biorefining feedstocks.

In the present work, the rapid identification of elements and their relative chemical composition in various Peganum harmala seed samples were investigated using a calibration-free laser-induced breakdown spectroscopy technique (CF-LIBS). A pulsed Nd:YAG laser-source with a 5 ns pulse-duration, and 10 Hz pulse repetition rates providing 400 and 200 mJ energy at 1064 and 532 nm wavelength, respectively, was focused on the Peganum harmala seed samples for ablation. A LIBS 2000+ spectrometer within the wavelength range (200 to 720 nm), emission-spectra were recorded. The measured spectra of the Peganum harmala sample gives spectral lines of Carbon (C), Magnesium (Mg), Lithium (Li), Sodium (Na), Calcium (Ca), Silicon (Si), Iron (Fe), Strontium (Sr), Copper (Cu), Potassium (K), and Lead (Pb). A CF-LIBS technique has been employed for the compositional study of the elements exist in the Peganum harmala seed samples. The measured results demonstrate that C, Mg, and Ca are found to be major elements in the Peganum harmala seed samples with compositions of ~36.64%, ~24.09%, and ~19.03%, respectively. Along with the major elements, the elements including Li, Na, Si, K, Fe, and Sr were identified as minor elements with compositions of ~2.87%, ~2.33%, ~3.72%, ~7.17%, ~2.83%, and ~1.14%, respectively. Besides Cu (~8.07 μg/g), and Pb (~1.10 μg/g) elements were observed as trace elements exist in the Peganum harmala seed samples. Furthermore, the electron number density including the plasma excitation-temperature were calculated using the stark-broadening line profile method and the Saha–Boltzmann plot method, respectively. The plasma parameters versus laser-irradiance and the distance from the sample were further investigated. Moreover, a principal component analysis (PCA) method was also utilized to the spectral data obtained by using LIBS to discriminate various seed samples with four classes, namely, α, β, γ, and Δ. Three principal-components (PCs) calculated from eigenvalues of score matrix described 87.6%, 4.6%, and 2.5% of total variance for PC1, PC2, and PC3, respectively. The LIBS spectral data variance covered by the initial 3 PCS was found as ~94.7% of total variance. The PCA results have successfully demonstrated the different classes of the Peganum harmala seed samples based on the different doping compositional ratios of the Zn element. This study confirmed the feasibility and ability of LIBS and PCA for the rapid analysis of Peganum harmala seed samples. Finally, the results achieved using CF-LIBS were incorporated with those obtained from the XRF and EDX analytical techniques.

Gas-chromatography/mass-spectrometry and sensory descriptive analyses were applied to provide new data on volatile and olfactory properties of Aglianico and Primitivo (Zinfandel), Italian red wines of growing interest. The relationships between data sets were investigated by multivariate statistical analyses: Principal Component and Hierarchical Clustering Analyses (PCA, HCA). A total of 35% of the volatiles varied significantly (ANOVA) between the two wines, mostly showing higher amounts in the Aglianico samples. Multivariate analyses showed intra-varietal similarity and inter-varietal diversity in terms of aromatic characteristics. PCA indicated that Aglianico wines were mainly related to the main fermentative alcohols, with a sensory impact, and to terpenols, suggesting a potential discriminating power at a compositional level. Primitivo wines formed two groups, one of which correlated to the floral aroma vector linked to beta-phenethyl acetate and beta-ionone. These findings may be valuable for updating the information on these wines and for future research to improve and obtain more targeted production and communication approaches.

Cytokines have been investigated extensively in elderly people, with conflicting results. We performed a comprehensive analysis of the plasma levels of 62 cytokines and growth factors involved in the regulation of the immune system, in healthy centenarians, and middle-aged controls. We confirmed the previously observed increase in the levels of several pro-inflammatory cytokines, such as TNF-α and IL-6, and found that several other cytokines, directly or indirectly involved in inflammation (such as IFN-α, IL-23, CCL-5), were present at higher levels in centenarians. We did not observe any increase in the levels of anti-inflammatory cytokines, with the notable exception of the Th2-shifting cytokine IL-19. No relevant difference was observed in cytokines regulating T cell immunity. Several growth factors having a role in regulating immunity, such as G-CSF, GM-CSF, EGF, and VEGF, were upregulated in centenarians, too. Principal component analysis of the cytokine dataset showed that pro and anti-inflammatory cytokines were the variables that contributed the most to the variability of the data we observed.

Results from studying the effect of an applied electric voltage on the Raman spectrum of graphene deposited on a lithium niobate crystal substrate with a ferroelectric domain structure are presented. The use of the principal component method for data processing in combination with correlation analysis made it possible to reveal the contribution to the change in the spectra associated with the linear deformation of the substrate due to the inverse piezoelectric effect. An effect of the graphene coating peeling was found. Furthermore, bending deformations of the graphene coating associated with the presence of a relief on the substrate were found. An analysis of the change in the spectra of graphene under the application of an electric voltage made it possible to determine the height of this relief.

The main challenge in Wastewater Treatment Plants (WWTP) by activated sludge process is the reduction of the energy consumption that varies according to the pollutant load of influent. However, this energy is fundamentally used for aerators in a biological process. The modeling of energy consumption according to the decision parameters deemed necessary for good control of the active sludge process namely the removal yields of parameters pollutant such as Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended solids (SS) and Ammoniac (NH4+) that must meet the required standards. To achieve this objective, a coupling of two approaches, the principal components analysis (PCA) method and the Adaptatif Neural Fuzzy Inference System (ANFIS) model was envisaged, to improve the performance of fuzzy reasoning. Indeed, PCA as a factorization tool allowing the reducing of the variable that allows the reduction of the complexity of the studied phenomenon. The neuro-fuzzy learning from the data projected on the principal axes allows the improvement of the model, both in learning and validation periods. The comparative study between ANFIS model, the regression PCA model, and the coupling PCA-ANFIS method applied to the raw data was effected. The results indicate a significant improvement in the validation criteria obtained in the coupling PCA-ANFIS model compared to the other models for the learning and validation periods. The result shows that the coupling PCA-ANFIS can be used to extract information from data and to describe the nonlinearity of complex wastewater treatment processes.

Skin aging is a condition caused by reactive oxygen species (ROS) and advanced glycation end products (AGEs). Indonesian gingers (Zingiber officinale), which consists of Gajah (GG), Red (MM), and Emprit (EE) ginger, are thought to produce anti-skin aging compounds through enzyme inhibition. The enzymes used in the molecular docking study were collagenase, hyaluronidase, elastase, and tyrosinase. This study aimed to determine the compounds contained in Indonesian ginger rhizome ethanolic extracts using liquid chromatography–mass spectrometry/mass spectrometry to differentiate metabolites contained in the different Indonesian ginger rhizome extracts. A principal component analysis (PCA) and a heat map analysis were used in order to determine which compounds and extracts contained potential anti-skin aging properties based on a molecular docking study. Ascorbic acid was used as a control ligand in the molecular docking study. Ninety-eight compounds were identified in three different ginger rhizomes extracts and were grouped into three separate quadrants. The most potent compound for anti-skin aging in the Indonesian ginger rhizome extracts was octinoxate. Octinoxate showed a high abundance in the EE ginger rhizome extract. Therefore, the EE ginger extract was the Indonesian ginger rhizome extract with the greatest potential for anti-skin aging.