Multivariate Data Analysis Methods Research Articles

Assessment of carcinogenic and non-carcinogenic risk of exposure to potentially toxic elements in tea infusions: Determination by ICP-OES and multivariate statistical data analysis

BackgroundThe perennial evergreen tea (Camellia sinensis) plant is one of the most popular nonalcoholic drinks in the world. Fertilizers and industrial, agricultural, and municipal activities are the usual drivers of soil contamination, contaminating tea plants with potentially toxic elements (PTEs). These elements might potentially accumulate to larger amounts in the leaves of plants after being taken up from the soil. Thus, frequent monitoring of these elements is critically important. MethodsThe present study intended to determine PTEs (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in both tea leaves and infusions using ICP-OES. Various multivariate data analysis methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to elucidate the potential sources of PTEs contamination, whether from anthropogenic activities or natural origins. Additionally, Pearson's correlation coefficient (PCC) was calculated to assess the relationships between the variables under study. ResultsThe mean contents (mg/L) of all studied elements in tea infusions decreased in order Mn (150.59 ± 1.66) > Fe (11.39 ± 0.99) > Zn (6.62 ± 0.89) > Cu (5.86 ± 0.62) > Co (3.25 ± 0.64) > Ni (1.69 ± 0.23) > Pb (1.08 ± 0.16) > Cr (0.57 ± 0.09) > Cd (0.46 ± 0.09) > Al (0.05 ± 0.008), indicating that Mn exhibits the highest abundance. The mean concentration trend in tea leaf samples mirrored that of infusions, albeit with higher concentrations of PTEs in the former. The tolerable dietary intake (TDI) value for Ni and provisional tolerable monthly intake (PTMI) value for Cd surpassed the standards set by the WHO and EFSA. Calculated hazard index (HI < 1) and cumulative cancer risk (CCR) values suggest negligible exposure risk. ConclusionElevated levels of PTEs in commonly consumed tea products concern the public and regulatory agencies.

Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells.

In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

DAMPAK GAYA KEPEMIMPINAN BERORIENTASI PRESTASI DAN IKLIM ORGANISASI TERHADAP KOMITMEN AFEKTIF DAAITV MELALUI KEPUASAN KERJA

This study aims to ascertain the impact of independent factors like achievement-oriented leadership style and organizational climate. Affective commitment is a dependent variable, whereas job satisfaction is an intervening variable. The primary source of data for this quantitative study was an instantaneous Likert scale questionnaire delivered to each research participant. Employees of PT. X Jakarta and Medan make up the research population. A sample of 136 participants was selected using non-probability sampling techniques in conjunction with the purposive sampling approach. This study makes use of multivariate data analysis methods with the statistical model SEM-PLS. The results of the analysis show that there is a positive and significant effect of achievement-oriented leadership style on job satisfaction, affective commitment and affective commitment through job satisfaction. There is also a positive and significant effect of organizational climate on job satisfaction, affective commitment and affective commitment through job satisfaction. And there is a positive and significant effect of job satisfaction on affective commitment. These results provide managerial implications regarding how to increase employee affective commitment by paying attention to the achievement-oriented leadership style of the leaders and the organizational climate formed and increasing employee job satisfaction.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

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.

Lipidomic analysis of facial skin surface lipids in acne in young women.

Alterations in the secretion and composition of skin surface lipids (SSL) are closely associated with the development of acne. Lipidomics is a useful tool to analyse the SSL of different types of acne. Our previous study found that phosphatidylserine and triacylglycerols dominate SSL changes in male acne and infantile acne, respectively. However, skin surface lipids as well as specific lipids in female acne patients remain to be investigated. To analyse and compare the SSL profile of acne women and healthy women and to discuss the involvement of differential lipids in acne development. Systematic lipidomics approach (high-throughput UPLC-QTOF-MS technology in combination with multivariate data analysis methods) was used to analyse the variations of SSL between acne and healthy groups. Analysis revealed significant differences in lipid content and composition between the two groups. Further analysis showed that levels of 13 individual lipids were significantly different and followed the same trend as the main class and subclasses. The largest individual contributor to the subgroup was triglycerides (TG) and phosphatidylinositol (PI). In addition, female acne patients exhibited reduced ceramide chain length (CCL) and increased levels of unsaturated fatty acids (UFAs), The changes of CCL in female acne are identical to male acne. There was a significantly higher level of TG and PI in the SSL of female acne patients. A reduction in CCL and an increase in UFAs content might contribute to the reduced skin barrier function in acne patients. The results suggest that female acne may have different pathogenesis than male acne.

Green Banking Practices and Green Financing of Listed Deposit Money Banks in Nigeria

This study examined the nexus between green banking practices and green financing sources of listed deposit money banks in Nigeria. The study anchored on the institutional theory and cross sectional survey research design was adopted with a population consisting of listed banks in Nigeria. The study used stratified random sampling of seven hundred and fifty (750) bank employees with questionnaire as the primary source of data collection from the respondents while only five hundred (500) were used for data analysis using univairate, bivariate and multivariate methods of data analysis. The results from the regression analysis disclosed that banks’ employee-related practices of green banking positively and significantly influence sources of green financing of deposit money banks in Nigeria; banks’ daily operations-related practices of green banking positively and significantly affect sources of green financing of deposit money banks in Nigeria; banks’ customers-related practices of green banking positively but significantly influence sources of green financing of deposit money banks in Nigeria; banks’ policy-related practices of green banking positively and significantly influence sources of green financing of deposit money banks in Nigeria and banks’ green investment related practices of green banking positively and significantly impact on sources of green financing of deposit money banks in Nigeria. Consequently, on the basis of the findings the study concluded that green banking practices positively influences the sources of green financing of deposit money banks in Nigeria. Therefore, the study recommends amongst others that banks’ in Nigeria should adopt contemporary banking practices that are ecofriendly as a means of enhancing the sources of green financing in Nigeria.

Endosperm structure and Glycemic Index of Japonica Italian rice varieties.

Given that rice serves as a crucial staple food for a significant portion of the global population and with the increasing number of individuals being diagnosed with diabetes, a primary objective in genetic improvement is to identify and cultivate low Glycemic Index (GI) varieties. This must be done while ensuring the preservation of grain quality. 25 Italian rice genotypes were characterized calculating their GI "in vivo" and, together with other 29 Italian and non-Italian genotypes they were studied to evaluate the grain inner structure through Field Emission Scanning Electron Microscopy (FESEM) technique. Using an ad-hoc developed algorithm, morphological features were extracted from the FESEM images, to be then inspected by means of multivariate data analysis methods. Large variability was observed in GI values (49 to 92 with respect to glucose), as well as in endosperm morphological features. According to the percentage of porosity is possible to distinguish approximately among rice varieties having a crystalline grain (< 1.7%), those intended for the preparation of risotto (> 5%), and a third group having intermediate characteristics. Waxy rice varieties were not united by a certain porosity level, but they shared a low starch granules eccentricity. With reference to morphological features, rice varieties with low GI (<55) seem to be characterized by large starch granules and low porosity values. Our data testify the wide variability of Italian rice cultivation giving interesting information for future breeding programs, finding that the structure of the endosperm can be regarded as a specific characteristic of each variety.

Combining indicators analysis and chemometrics to trace the geographical origin of crude oil

Geographic traceability is crucial to global oil trade security. This study discusses the possibility of using multivariate statistical methods combined with multi-indicator analysis to identify samples of crude oil imports from five major countries to China. The physicochemical properties and trace elements of crude oil were detected by Petroleum product standards and inductively coupled plasma atomic emission spectrometry (ICP-AES). Eight indexes (moisture, density, sulfur content, acid value, organochlorine, carbon residual, V, and Ni) were analyzed. Principal component analysis (PCA), hierarchical clustering analysis (HCA), Orthogonal projections to lateen structures-discriminant analysis (OPLS-DA), and other multivariate data analysis methods were used to determine the geographical origin of crude oil samples. Satisfying results have been obtained using PCA to reduce the dimensions of the indicators of crude oil from different origins. It allows the reduction of 8 variables to 3 principal components and accounts for 80.06% of the total variance. The HCA shows five clusters corresponding to five sources of crude oil. This will help to improve the utilization rate of crude oil with different characteristics, improve the quality of crude oil trade, and ensure the high quality of crude oil trade. For the sample set used for modeling, the model's accuracy was 97.19% after OPLS-DA optimization. These results show that the combination of multi-index analysis and stoichiometry is an effective tool for identifying crude oil origin, which fills the technical gap in the rapid identification of crude oil origin.

Distribution and morphological variation of tree ferns (Cyatheaceae) along an elevation gradient.

Knowing how species and communities respond to environmental change is fundamental in the context of climate change. The search for patterns of abundance and phenotypic variation along altitudinal gradients can provide evidence on adaptive limits. We evaluated the species abundance and the variation in morphometric and stomatal characters in five tree ferns species (Cyathea fulva, C. divergens, C. myosuroides, Alsophila firma and Gymnosphaera salvinii) distributed along an elevation gradient in a well-preserved Mexican cloud forest. Variation at the community and species level was assessed using exploratory and multivariate data analysis methods. We wanted to explore if the species abundance is environmentally determined, to determine the degree of variation along the elevation gradient, to test for differences between zones and associations with elevation, humidity and soil nutrients, and to assess contribution of the intra- and interspecific variation to the community response to elevation and soil nutrients. The studied fern community showed strong species turnover along the elevation gradient, with some influence of soil nutrient concentration, supporting environmental determinism. All measured characters displayed variation along the gradient. Stomatal characters (size and density) had significantly less variation than morphometric characters (trunk diameter, stipe length and blade length), but stomatal density also shows interesting intraspecific patterns. In general, patterns within the fern community suggest a strong influence of species identity, especially of species inhabiting the lower edge of the cloud forest, which showed the clearest morphometric and stomatal patterns, associated to contrasting environments rather than to changes in elevation. The coincidence between morphometric and stomatal patterns in this area suggest hydraulic adjustments in response to contrasting environments. Our results provide evidence that tree ferns species respond to environmental changes through adjustments of morphometric plasticity and stomatal density, which is relevant to predict possible responses to variation in environmental conditions resulting from climate change.

Application of self-organizing maps to coal elemental data

Understanding the modes of occurrence of elements in coal is important, not only to help properly evaluate the impacts of potentially toxic elements on the environment and human health but also to provide technical guidance for recovering critical elements from coal ash. Statistical and multivariate data analysis methods have widely been used, together with physical and chemical methods, to determine the modes of occurrence of elements in coal. However, some of the statistical methods, e.g., average linkage hierarchical clustering algorithm have some disadvantages (e.g., statistical errors and poor visualization). A self-organizing map is an unsupervised artificial neural network, and it is known for its high data mining capability and excellent data visualization. In contrast to the average linkage hierarchical clustering algorithm that is commonly used for analyzing the modes of occurrence of elements in coal, the self-organizing map algorithm can provide a topological relationship among elements instead of merely providing the groups to which the elements belong. This paper focuses on the application of self-organizing map to coal elemental data for analyzing the modes of occurrence of elements in coal. Samples used in this study are from the Adaohai, Hailiushu, and Datanhao mines, all located in the Daqingshan Coalfield, Inner Mongolia, China. The results obtained from the self-organizing map algorithm are compared with those produced by average linkage hierarchical clustering algorithm. Based on the previous investigations (mainly direct methods) and further analysis, it can be concluded that the results from the self-organizing map algorithm in this investigation are more consistent with the geochemical nature and previous investigations by direct methods than those from average linkage hierarchical clustering algorithm. Consequently, the self-organizing map algorithm is a new reliable and intuitive method for analyzing the modes of occurrence of elements in coal.

Assessment of chewing efficiency with artificial intelligence

BACKGROUND: Artificial intelligence (AI) is a system based on machine learning of neural networks. AI structure resembles nerve tissue, having the so called neurons, i.e. mathematical codes. A neural network has three levels, including the input layer (information enters the system), the hidden layer (multidimensional data is analyzed), and the output layer (the system generates conclusions). Current neural networks use a perceptron, i.e. a neuron consisting of a large number of interconnected input and hidden layers, which makes the system capable of self-learning and analysis of non-linear data and generalization and processing of incomplete information, including the method of projection onto latent structures.&#x0D; AIM: To develope a program based on multivariate data analysis to determine the chewing efficiency at the stages of prosthetics.&#x0D; METHODS: In 2016, the Department of Orthopedic Dentistry and Orthodontics developed and tested a program for determining chewing efficiency based on the analysis of digital occlusiograms obtained by scanning dental prints on a wax plate. The results were processed by mathematical methods of multivariate data analysis using the projection on latent structures (the partial least-squares [PLS-2] model), which allowed assessing the relationship between the value of chewing efficiency and the area and brightness characteristics of the areas corresponding to occlusal contacts. The program compared the measurement results with the reference occlusiograms in the database and gave a conclusion. The experiments yielded statistically significant results for the efficiency of the program compared to traditional chewing tests. Due to the relevance of implementing AI in orthopedic treatment, the decision was made to improve the training methods of the program to update the existing array of reference data. Starting in 2019, 24 occlusiograms were added to the previously received data with dental defects from 9 to 12 teeth. Using an expanded database, the program, when analyzing the occlusiogram of a new patient, allowed to consider changes in chewing efficiency obtained earlier with the Trezubov chewing test for various defects of the dentition and compare the reference and minimally achievable values of chewing efficiency. The program algorithm was verified by the researchers using the classical Trezubov chewing test. Chewing efficiency was measured as a percentage.&#x0D; RESULTS: The obtained combinations of the digital algorithm parameters for assessing chewing efficiency resulted in increased accuracy in the range of 4%6% compared to the traditional Rubinov and Ryakhovsky chewing tests.&#x0D; CONCLUSIONS: A digital algorithm for assessing chewing efficiency allows for quick and accurate assessment without the use of time-consuming analog tests.

Continuous densification of wood with a belt press: how knot features impact the densification outcome

ABSTRACT Densification, i.e. the transverse compression of sawn timber has been studied and commercialised for well over 100 years but remains an expensive niche product with low annual production volumes. One reason for this is the reliance on time-consuming batch processes in a hot press. To solve this, a continuous densification process using a belt press, capable of densifying full-sized sideboards was developed. However, there is insufficient knowledge about the effect of knots on the densification outcome. The objective of this study was to assess how different knot parameters affect the densified wood in terms of damage and deformation to the knot itself and the surrounding wood material. Multivariate data analysis methods were applied to a dataset of 171 knots, described by 23 variables. The data showed that it is possible to densify knots in a continuous process without causing damage. Especially sound knots are often unproblematic, even at relatively large sizes, while densifying dead knots often resulted in unacceptable damage to the knot or the surrounding wood. From a material selection standpoint, any knots bleeding into the board edge and dead knots greater than 20 mm in diameter should be avoided altogether.

Relationship between indoor noise perception and remote work during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has had a major influence on working patterns worldwide, given the various lockdown periods and the shift to remote working. As people's noise perception is known to be closely linked with their work performance and job satisfaction, investigating the noise perception in indoor spaces, especially in situations where people work from home, is crucial; however, studies on this aspect are limited. Thus, here, this study aimed to investigate the relationship between indoor noise perception and remote work during the pandemic. The study assessed how people who worked from home perceived indoor noise, and how it related with their work performance and job satisfaction. A social survey was conducted with respondents who worked from home during the pandemic in South Korea. A total of 1,093 valid responses were used for data analysis. Structural equation modeling was used as a multivariate data analysis method to simultaneously estimate multiple and interrelated relationships. The results showed that indoor noise disturbance significantly affected annoyance and work performance. Annoyance with indoor noise affected job satisfaction. Job satisfaction was found to have a significant impact on work performance, particularly on two dimensions of the work performance that are crucial for achieving organizations' goals. Moreover, one dimension of the work performance had a significant impact on annoyance. The study proposed that reducing negative perception of indoor noise and improvement of job satisfaction can lead to the maximization of one's work performance when working from home.

Morpho-biometric characterization of indigenous chicken ecotypes in north-western Ethiopia.

Morphological characterization of Animal Genetic Resources is the first step to documenting diversity and designing breed specific breeding programs. The current study characterized the morpho-biometric variation of indigenous chicken ecotypes prevailing in northwestern Ethiopia. A multi-stage purposive, stratified, and random sampling method was employed to select the study areas and chickens. A total of 1200 adult chickens were sampled and characterized for 12 qualitative and 11 quantitative traits. Univariate and multivariate data analysis methods were employed to analyze the data using SAS and R statistical software. Red plumage colour (33.2%), white and red earlobe colour (73.8%) and yellow shank colour (57.0%) were the most predominant colour trait categories. Sex, agro-ecology, location, and the interaction of sex and location had a highly significant (p<0.001) effect on all body measurements. Shank traits were found to have the highest discriminating power in both sexes. The overall classification rates for the female and male sample populations were 57.47% and 69.97%, respectively. The squared Mahalanobis distances between sites were significant (p<0.001) for both sexes. The longest distance was obtained between North Achefer and Banja (19.25) and between North Achefer and Dembecha (16.80) in female and male chickens, respectively. In female chickens, canonical variates 1 (CAN 1) and 2 (CAN 2) explained 82% of total variation and distinctly separated the sample populations of North Achefer and Jawi from others. In male chickens, 90% of the total variance is explained by CAN1, CAN2, and CAN3, which distinctly separate the sample populations of the North Achefer, Sinan, and Jawi, among others. Using cluster analysis, the indigenous chickens found in the study area could be classified into four ecotypes: ecotype 1 (Banja, Dembecha, and Aneded), ecotype 2 (North Achefer), ecotype 3 (Sinan), and ecotype 4 (Jawi).

Exploring the stabilizing effect on the i-motif of neighboring structural motifs and drugs

Cytosine-rich DNA sequences may fold into a structure known as i-motif, with potential in vivo modulation of gene expression. The stability of the i-motif is residual at neutral pH values. To increase it, the addition of neighboring moieties, such as Watson-Crick stabilized loops, tetrads, or non-canonical base pairs have been proposed.Taking a recently described i-motif structure as a model, the relative effect of these structural moieties, as well as several DNA ligands, on the stabilization of the i-motif has been studied. To this end, not only the original sequence but different mutants were considered. Spectroscopic techniques, PAGE, and multivariate data analysis methods have been used to model the folding/unfolding equilibria induced by changes of pH, temperature, and the presence of ligands.The results have shown that the duplex is the moiety that is responsible of the stabilization of the i-motif structure at neutral pH. The T:T base pair, on the contrary, shows little stabilization of the i-motif. From several selected DNA-binding ligands, the G-quadruplex ligand BA41 is shown to interact with the duplex moiety, whereas non-specific interaction and little stabilization has been observed within the i-motif.

Photoluminescence Probes in Data-Enabled Sensing.

This review summarizes the current status of development in photoluminescent probes, multidimensional photoluminescence detection, and multivariate data analysis methods. It then highlights reports featuring multivariate analysis of multidimensional measurements of photoluminescent probes published between June 2015 and June 2022, emphasizing work in the last 5 years. Important trends include the development of probe arrays, which provide fingerprint responses to the analyte(s) of interest and facilitate the analysis of complex samples; the application of neural networks and deep learning to pattern recognition and feature selection in photoluminescence images; and the application of multiway multivariate analysis to mining matrices, three-way arrays, and higher-order measurements, including hyperspectral intensity and lifetime images. These examples illustrate the increase in information extraction provided by the combination of multidimensional measurements and multivariate analysis.

Detection of pyrimidine-rich DNA sequences based on the formation of parallel and antiparallel triplex DNA and fluorescent silver nanoclusters

In this work, the use of DNA-stabilized fluorescent silver nanoclusters for the detection of target pyrimidine-rich DNA sequences by formation of parallel and antiparallel triplex structures is studied by molecular fluorescence spectroscopy. In the case of parallel triplexes, the probe DNA fragments are Watson-Crick stabilized hairpins, and whereas in the case of antiparallel triplexes, the probe fragments are reverse-Hoogsteen clamps. In all cases, the formation of the triplex structures has been assessed by means of polyacrylamide gel electrophoresis, circular dichroism, and molecular fluorescence spectroscopies, as well as multivariate data analysis methods. The results have shown that it is possible the detection of pyrimidine-rich sequences with an acceptable selectivity by using the approach based on the formation of antiparallel triplex structures.

Predictive liver lipid biomarker signature of Acetyl-coenzyme A carboxylase inhibitor related developmental toxicity in non-pregnant female Han Wistar rats – Lipidomics biomarker discovery and validation

IntroductionAcetyl-coenzyme A carboxylase (ACCase) inhibition is an attractive herbicide target. However, issues with fetal developmental toxicity identified at the late stages of the development process can halt progression of previously promising candidates. ObjectivesTo select and verify predictive lipid biomarkers of ACCase inhibition activity in vivo using liver samples obtained from early stage 7 day repeat dose studies in non-pregnant female Han Wistar rats that could be translated to developmental toxicity endpoints discovered during late-stage studies to provide an early screening tool. MethodsLiver samples from eight rat repeat dose studies, exposed to six ACCase inhibitors from three different chemistries and one alternative mode of action (MoA) that also perturbs lipid biochemistry, were analysed using liquid chromatography – high resolution accurate mass – mass spectrometry. Multivariate and univariate data analysis methods were used for biomarker discovery and validation. ResultsA biomarker signature consisting of sixteen lipids biomarkers were selected. Verification of the signature as indicative of ACCase inhibition was established by demonstrating consistent perturbations in the biomarkers using two different ACCase inhibitor chemistries and the lack thereof with an alternate MoA. The fold change profile pattern was predictive of which test substance doses would or would not cause developmental toxicity. ConclusionA strategy for selecting and verifying a robust signature of lipid biomarkers for predicting a toxicological end point has been described and demonstrated. Differences in lipidomic profiles correlated with developmental toxicity suggesting that indicators of a molecular initiation event resulting in pup developmental toxicity can be predicted from short term, toxicity studies conducted in non-pregnant adult female Han Wistar rats.

Identification of Turkish extra virgin olive oils produced in different regions by using NMR (1H and 13C) and IRMS (13C/12C)

AbstractIsotope ratio mass spectroscopy (IRMS) and nuclear magnetic resonance (NMR) spectroscopy techniques are two of the analytical methods that are used to characterize food products. The aim of this study is to classify extra virgin olive oil (EVOO) samples collected from different regions of Turkey based on 1H and 13C NMR spectra along with IRMS δ13C carbon isotope ratio data by using chemometrics multivariate data analysis methods. A total of 175 EVOO samples were analyzed in 2014/15 and 2015/16 harvest seasons. Multivariate classification and clustering models were used to identify geographical and botanical origins of the EVOOs. IRMS results showed that there was no significant difference in terms of δ13C values between the years in terms of harvest year (p &gt; 0.05), only extraction phase and variety were statistically significant factors (p &lt; 0.05). The interactions of the factors showed that the harvest year × variety interaction is important. The outcomes of this research clearly indicated that considering the partial least squares discriminant analysis result with NMR spectra, the percent success of the model in the South Marmara, North Aegean, and South Aegean region samples were 95%, 95.7%, and 96.4% in the model set, respectively. The results showed that by using classification and clustering models, geographic marking and labeling of these oils can be carried out regardless of differences in year and production systems (2 and 3 phase extraction system) according the NMR analysis.