Principal Component Analysis Scatter Plot Research Articles

Surface-enhanced Raman spectroscopy for the characterization of xylanases enzyme

Xylanases are essential hydrolytic enzymes which break down the plant cell wall polysaccharide, xylan composed of D-xylose monomers. Surface-enhanced Raman Spectroscopy (SERS) was utilized for the characterization of interaction of xylanases with xylan at varying concentrations. The study focuses on the application of SERS for the characterization of enzymatic activity of xylanases causing hydrolysis of Xylan substrate with increase in its concentration which is substrate for this enzyme in the range of 0.2% to 1.0%. SERS differentiating features are identified which can be associated with xylanases treated with different concentrations of xylan. SERS measurements were performed using silver nanoparticles as SERS substrate to amplify Raman signal intensity for the characterization of xylan treated with xylanases. Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) were applied to analyze the spectral data to analyze differentiation between the SERS spectra of different samples. Mean SERS spectra revealed significant differences in spectral features particularly related to carbohydrate skeletal mode and O-C-O and C–C–C ring deformations. PCA scatter plot effectively differentiates data sets, demonstrating SERS ability to distinguish treated xylanases samples and the PC-loadings plot highlights the variables responsible for differentiation. PLS-DA was employed as a quantitative classification model for treated xylanase enzymes with increasing concentrations of xylan. The values of sensitivity, specificity, and accuracy were found to be 0.98%, 0.99%, and 100% respectively. Moreover, the AUC value was found to be 0.9947 which signifies the excellent performance of PLS-DA model. SERS combined with multivariate techniques, effectively characterized and differentiated xylanase samples as a result of interaction with different concentrations of the Xylan substrate. The identified SERS features can help to characterize xylanases treated with various concentrations of xylan with promising applications in the bio-processing and biotechnology industries.

Gas chromatography-mass spectrometry pilot study to identify volatile organic compound biomarkers of childhood obesity with dyslipidemia in exhaled breath.

Childhood obesity affects multiple organs in the body and is associated with both significant morbidity and ultimately premature mortality. Childhood obesity, especially dyslipidemia, can lead to early atherosclerosis and premature cardiovascular disease (CVD) in adulthood. The detection of exhaled volatile organic compounds (VOCs) in the breath offers the opportunity for the discovery of novel disease-specific biomarkers. This study aimed to identify VOCs that correlate with childhood obesity accompanied by dyslipidemia. A total of 82 overweight or obese children between the ages of 8 and 12 years were recruited from the exercise on obesity adolescents in Peking (EXCITING) study (NCT04984005). The breath VOCs of the participants were measured by gas chromatography-mass spectrometry (GC-MS). The classification was performed using principal component analysis (PCA) of the relative abundance of VOCs. The difference between the obese and overweight groups with or without dyslipidemia was analyzed. Among the 82 children, 25 were overweight, of whom 10 had dyslipidemia. The other 57 children were obese, and 17 of them had dyslipidemia. Obese children with dyslipidemia had higher triglycerides and elevated non-high-density lipoprotein-cholesterol compared to overweight children without dyslipidemia. We confirmed 13 compounds based on database well matches (average score > 80) for mass spectra and refractive index. These 13 VOCs were grouped into three chemical functional groups: saturated hydrocarbons, aromatic hydrocarbons and unsaturated aldehydes. For obese children with dyslipidemia, the PCA scatter plot of the three chemical groups was obviously separated from the other groups. Some of the candidates, including heptadecane, naphthalene, and cis-6-nonnenol, were significantly higher in obese children with dyslipidemia than in overweight groups with or without dyslipidemia. A suite of VOCs from three chemical function groups, saturated hydrocarbons, aromatic hydrocarbons, and unsaturated aldehydes, were separated in the obese children with dyslipidemia. Heptadecane, naphthalene, and cis-6-nonenol were significantly elevated in obese children with dyslipidemia. Our findings underscore the potential value of the candidate VOCs for future risk categorization.

A novel survival prediction signature outperforms PAM50 and artificial intelligence-based feature-selection methods

The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution ‘progression’ information using SPS, dividing survival outcomes into several clinically relevant stages (‘good’, ‘intermediate’, and ‘bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these ‘progression’ annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies.

In-depth morphological assessment revealed significant genetic variability in common buckwheat (Fagopyrum esculentum) and tartary buckwheat (Fagopyrum tataricum) germplasm

AbstractBuckwheat (Fagopyrum spp.) is an important crop in the high-altitude regions of the Northwest Indian Himalayas. The agro-climatic heterogeneity of this region offers a great deal of diversity in the agro-morphology of buckwheat species. In this study, a total of 61 accessions of Fagopyrum esculentum and Fagopyrum tataricum were characterized for 17 morphological (8 qualitative and 9 quantitative) traits. Significant differences (P < 0.0001) among all the traits were revealed by one-way analysis of variance. Further, significant phenotypic variability in both qualitative as well as quantitative traits was also observed. Both positive and negative correlations were observed between the traits of agronomic relevance. The principal component analysis (PCA) reveals about 69% variability among the first six components. The accessions were divided into two key clusters with numerous subclusters by considering the unweighted pair group method with arithmetic mean dendrogram. A cluster of 19 accessions was formed utilizing a PCA scatter plot indicating accessions with maximum values for important quality traits like plant height, leaf blade width, stem colour (red), primary branches, inflorescence length, flower colour (greenish-yellow), seed anthocyanin colour (green), seed shape (ovate) and seed weight. These accessions can be of vital significance for future buckwheat breeding programmes. The findings from the current study will form a favourable base for genetic resource management, improved cultivation and applications of buckwheat at the commercial level in the northwestern Himalayas of India.

Diagnosis of HCV infection using attenuated total Reflection-FTIR spectra of Freeze-Dried sera

Globally, hepatitis C virus (HCV) infection is the leading cause for liver related morbidity and mortality. Mortality rate linked with HCV can be effectively lowered by prompt and early diagnosis. In this research work, we aimed to evaluate the potential of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to discriminate healthy sera samples from HCV infected freeze-dried sera samples in order to develop a new and quick reagent free diagnostic tool. Clear variation was observed between the ATR-FTIR spectra of 87 HCV infected and 83 healthy freeze-dried sera samples. Major variation was observed in the two spectral regions i.e., 3500–2800 and 1800–900 cm−1. Multivariate classification algorithms including principal component analysis (PCA) and linear discriminant analysis (LDA) were used. PCA scatter plot of first two PCs showed 97% variation between the two sample classes. When the four PCs were projected to the LDA algorithm for developing PCA-LDA diagnostic model it discriminated the HCV and healthy classes with 100% accuracy. This study provides an initial insight on the potential to use ATR-FTIR spectroscopy in conjugation with multivariate data classification tools for effective HCV diagnosis.

Use of multivariate analysis to evaluate antigenic relationships between US BVDV vaccine strains and non-US genetically divergent isolates

Bovine viral diarrhea virus (BVDV) comprises two species, BVDV-1 and BVDV-2. But given the genetic diversity among pestiviruses, at least 22 subgenotypes are described for BVDV-1 and 3-4 for BVDV-2. Genetic characterization is generally accomplished through complete or partial sequencing and phylogeny, but it is not a reliable method to define antigenic relationships. The traditional method for evaluating antigenic relationships between pestivirus isolates is the virus neutralization (VN) assay, but interpretation of the data to define antigenic relatedness can be difficult to discern for BVDV isolates within the same BVDV species. Data from this study utilized a multivariate analysis for visualization of VN results to analyze the antigenic relationships between US vaccine strains and field isolates from Switzerland, Italy, Brazil, and the UK. Polyclonal sera were generated against six BVDV strains currently contained in vaccine formulations, and each serum was used in VNs to measure the titers against seven vaccine strains (including the six homologous strains) and 23 BVDV field isolates. Principal component analysis (PCA) was performed using VN titers, and results were interpreted from PCA clustering within the PCA dendrogram and scatter plot. The results demonstrated clustering patterns among various isolates suggesting antigenic relatedness. As expected, the BVDV-1 and BVDV-2 isolates did not cluster together and had the greatest spatial distribution. Notably, a number of clusters representing antigenically related BVDV-1 subgroups contain isolates of different subgenotypes. The multivariate analysis may be a method to better characterize antigenic relationships among BVDV isolates that belong to the same BVDV species and do not have distinct antigenic differences. This might be an invaluable tool to ameliorate the composition of current vaccines, which might well be important for the success of any BVDV control program that includes vaccination in its scheme.

<p><strong>Palynological observations on the genus <em>Gundelia </em>L. (Asteraceae) growing in Turkey</strong></p>

The pollen morphology of 17 taxa of Gundelia, of which 13 taxa are endemic to Turkey, was investigated by light microscopy (LM) and scanning electron microscopy (SEM). In this study, the pollen morphology of 16 taxa except G. tournefortii was investigated for the first time. The quantitative data were analyzed by descriptive statistics and multivariate statistics. The pollen grains are oblate spheroidal and prolate spheroidal in shape with the polar diameter of 37.01–61.2 μm and the equatorial diameter of 31.2–63.4 μm. The smallest pollen grains were observed in G. anatolica, G. rosea and G. tournefortii, while the largest pollen grains were observed in G. vitekii, G. komagenensis and G. colemerikensis. Pollen ornamentation is echinate in LM and echinate-microperforate in SEM. Statistically, the relationships between pollen characters of the examined taxa were identified using Principal Component Analysis (PCA) and Clustering Analyzis. In addition, a dendrogram was constructed by applying UPGMA based on the determined 15 pollen morphological characters, and the degree of their relationship was discussed. Polar axis, equatorial diameter and colpus length were the major explanations of the pollen morphological characters’ total variation in PCA. Pair group method was used to divide the main pollen taxa into three groups based on the pollen morphology by using arithmetic averages analysis (PCA scatterplot and cluster analysis). As a result, the pollen shape, polar axis, equatorial diameter and spine length were important characters distingushing the taxa from each other. We found some matches and mismatches between pollen and morphological characters, whose significance requires further study.

Analysis of phenotypic diversity of apricot (Prunus armeniacaL.) accessions from Jammu and Kashmir, India

AbstractApricot (Prunus armeniacaL.) is a world-wide highly appreciated fruit, with its attractive colour, soft texture and typical flavour. In the current investigation, 68 apricot accessions collected from Jammu and Kashmir, India were analysed to determine the measure of variation using 12 qualitative and 16 quantitative traits with an aim to identify superior apricot accessions with excellent fruit quality traits. High phenotypic variability was observed among the studied apricot accessions with significant differences among most of the qualitative and quantitative traits. Fruit-related characteristics including fruit weight, fruit length, fruit ratio, fruit firmness, fruit shape, fruit suture, stone weight, kernel weight and stone shape were the most diverse with a high coefficient of variation (>30%). One way analysis of variance showed significant differences (P< 0.0001) among all the quantitative traits. Significant positive and negative correlations were observed between all the agronomically important fruit quality traits. The principal component analysis (PCA) revealed that 75.34% of the variability was defined by the first eight components. The unweighted pair group method with arithmetic mean (UPGMA) dendrogram, based on all traits measured grouped the accessions into two main clusters with several sub-clusters. Both UPGMA dendrogram and PCA scatter plot formed a cluster of 14 accessions, having the highest values regarding most important fruit quality traits such as fruit weight, fruit length and width, fruit ratio, stone weight and kernel weight can be treated as potentially superior accessions. These accessions can be used directly for cultivation and in future apricot breeding programmes. The present findings are promising for genetic resource management, cultivar improvement and commercial applications of apricot in Jammu and Kashmir, India.

ATR-FTIR spectroscopy based differentiation of typhoid and dengue fever in infected human sera

In this study, we have evaluated the potential of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to differentially diagnose bimolecular markers in Salmonella typhi and dengue infected freeze-dried sera samples along with healthy freeze-dried sera samples. Spectral signatures were recorded from 18 S. typhi infected, 23 dengue infected and 28 healthy freeze-dried sera samples. In order to achieve enhanced classification accuracy between the three sample classes, multivariate data classification algorithms principal component analysis (PCA) and linear discriminant analysis (LDA) were used. Outcomes of the PCA scatter plot showed 95% inter-class variation. A classification accuracy of 100% was achieved using LDA; therefore, we propose that ATR-FTIR coupled with chemo metric data classification algorithms holds substantial potential for bimolecular differentiation between S. typhi and dengue infected freeze-dried sera samples.

High-density single nucleotide polymorphism chip-based conservation genetic analysis of indigenous pig breeds from Shandong Province, China.

ObjectiveShandong indigenous pig breeds are important Chinese pig resources. Their progressive population decline in recent decades has attracted attention towards their conservation. Conservation genetics of these indigenous breeds are essential for developing a conservation and utilization scheme.MethodsA high-density single nucleotide polymorphism (HD-SNP) chip-based comparative analysis of genetic characteristics was performed for seven Shandong indigenous pig breeds in the context of five Western commercial breeds.ResultsThe results showed that Shandong indigenous pig breeds varied greatly in genetic diversity, effective population size, inbreeding level, and genetic distance with the Western commercial breeds. Specifically, Laiwu and Dapulian displayed low genetic diversity, and had a genetically distant relationship with the Western commercial breeds (average F statistics [FST] value of 0.3226 and 0.2666, respectively). Contrastingly, the other five breeds (Yantai, Licha, Yimeng, Wulain, and Heigai) displayed high genetic diversity within breed and had some extent of mixture pattern with the Western commercial breeds, especially Duroc and Landrace (FST values from 0.1043 to 0.2536). Furthermore, intensive gene flow was discovered among the seven Shandong indigenous breeds, particularly Wulian, Licha, and Heigai, as indicated by the large cluster formed in the principal component analysis scatterplot and small population differentiation (average of 0.1253) among them.ConclusionOur study advances the understanding of genetic characteristics of Shandong indigenous breeds and provides essential information for developing an appropriate conservation and utilization scheme for these breeds.

Use of ATR-FTIR for detection of Salmonella typhi infection in human blood sera

Evaluation of diagnostic capability of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to classify differentially expressed bio-molecular markers in S. typhi infected and healthy freeze-dried sera samples. Using 25 typhoid infected and 25 healthy freeze-dried sera samples, ATR-FTIR spectra were recorded. For evaluating the sample discrimination amid selected wave number regions of FTIR spectra; chemometric data classification algorithms i.e. principal component analysis (PCA), partial least square regression (PLSR) and hierarchical cluster analysis (HCA) were employed. PCA scatter plot showed 90% variation between the S. typhi infected and healthy freeze-dried sera samples. Infection signs were successfully predicted using PLSR model with R2 = 0.9757. HCA dendogram yielded sensitivity and specificity 100% and 96% respectively. ATR-FTIR spectroscopy when coupled with chemometric analysis holds significant potential for biomolecular decimation between S. typhi infected and healthy; freeze-dried sera samples.

A fluorescent sensor array based on silver nanoclusters for identifying heavy metal ions

The rapid, simple and reliable identification of heavy metal ions has attracted increasing attention of researchers. This work constructed a fluorescent sensor array by using 4 kinds of polymer templated silver nanoclusters (Ag NCs) as the sensing elements. This sensor array can be used for identifying 7 kinds of heavy metal ions including Pb2+, Fe3+, Cu2+, Cd2+, Cr3+, Co2+ and Ni2+. The fluorescence intensity ratios of Ag NCs after and before adding metal ions were applied as the output signals. We used principal component analysis (PCA) to reduce the dimensions of data variables and obtained a PCA scatter plot that could clearly distinguish every kind of 7 metal ions. This sensor array had a good performance when the ions concentration was in the range of 15–800 μM at pH of 7. The sensor array could also clearly discriminate these 7 heavy metal ions spiked in tap water. The Ag NCs-based sensor array could also be used as a fluorescence lifetime sensor array for the discrimination of metal ions. Taken all together, the developed sensor array holds great promise for practical analysis in the environment.

Multivariate analysis as a method to evaluate antigenic relationships between BVDV vaccine and field strains

Bovine viral diarrhea virus (BVDV) is comprised of two species, BVDV-1 and BVDV-2, but given the genetic diversity among pestiviruses, at least 21 subgenotypes are described for BVDV-1 and 4 for BVDV-2. Genetic characterization can be achieved through complete or partial sequencing and phylogeny, but antigenic characterization can be difficult to determine due to the antigenic diversity and cross-neutralization that exists among isolates. The traditional method for evaluating antigenic relationships between pestivirus isolates is the virus neutralization (VN) assay, but interpretation of the data to determine antigenic difference can be unclear. Data from this study utilized a multivariate analysis for visualization of VN results to analyze the antigenic relationships between vaccine strains and multiple field isolates. Polyclonal sera were generated against 6 BVDV strains currently contained in vaccine formulations, and each serum was used in VN’s to measure the neutralizing antibody titers against 15 BVDV field isolates characterized as prevalent and divergent subgenotypes in the USA. Principal component analysis (PCA) were performed on the VN assay datasets, and results were interpreted from PCA clustering within the PCA dendrogram and scatter plot. The results demonstrated clustering patterns among isolates suggestive of antigenic differences. While expected, the BVDV-1 and BVDV-2 isolates did not cluster together and had the greatest spatial distribution. In addition, other BVDV isolates had distinct spatial patterns suggesting antigenically divergent isolates. This analysis provides an alternative and more efficient means to analyze large VN datasets to visualize antigenic relationships between pestivirus isolates. This analysis could be beneficial for vaccine development and evaluation of efficacy, since most vaccines cannot fully protect animals from the broad range diversity of BVDV viruses.

Geo-Tracing of Black Pepper Using Metal Oxide Semiconductor (MOS) Gas Sensors Array

Malaysia, Sarawak state in particular produces superior quality black pepper compared to other producing countries. While Sarawak black pepper deserves higher price in the global market, from year 2015 onwards, its price has been plummeting due to global oversupply of pepper. Physical, chemical and biological profiling of Sarawak pepper is conducted to differentiate Sarawak pepper against adulteration and to justify its higher market price. However, the analysis of pepper was performed using time-consuming, non-portable, expensive and invasive laboratory equipment and methodologies. This paper reports an attempt to develop a black pepper geo-tracing system using metal oxide semiconductor (MOS) gas sensors array. The pneumatic system to contain and purge the sample volatile organic compounds (VOCs) is designed, simulated, fabricated and proven to be contain and purge sample VOCs effectively. A total of 200 black pepper samples from Malaysia and India are sampled using non-invasive method for the MOS gas sensors response and the sensors response are analyzed using Principal Component Analysis (PCA). The PCA scatter plots shows that Malaysia and India black pepper can be discriminated based on the MOS gas sensor response towards different VOCs composition in the black pepper samples. The PCA result are then used for geo-tracing of a total of 40 samples (20 from Malaysia; 20 from India), which shows an overall accuracy of 92.5%. The geo-tracing system developed performs rapidly with 260–600 s sampling cycle compared to gas chromatography-mass spectroscopy (GC-MS) equipment which requires at least 30 minutes sampling time, and is portable, cheaper and non-invasive.

Visualization of transcriptome data

As an important method to study the phenotype and function of organisms, transcriptome has become one of hot topics in current research. The transcriptomics research usually accompanies with massive data. With the increase of the amount of data, the rules and features hidden in it are not easy to be found. Transforming big data into visual graphics is the most undoubtedly intuitive way to display the hidden information of big data. Several graphs commonly used in transcriptome study were introduced in this paper, such as Venn diagram, heat map, principal component analysis scatter plot, enrichment analysis plot, and time series analysis plot, in order to help readers to choose suitable graphics in future studies.

Organ-oriented phytochemical profiling and radical scavenging activity of Alcea spp. (Malvaceae) from Iran

Polyphenol content and antioxidant activity of flowers and herbage of three species of the genus Alcea were determined. Flowers showed high total phenolic content and yielded values ranging from 26.83 (Alcea aucheri var. lobata) to 82.59 (A. aucheri var. aucheri) mg GAE/g DW. Total anthocyanin content was highest (934.98 mg/ml) in the flowers of A. auchrei var. aucheri (A12), whereas the content was minimal (1.34 mg/ml) in the herbage of Alcea koelzii (A2). The highest total flavonoid content was recorded in A. koelzii (16.15 mg Q/g DW), while the lowest value was achieved in A. aucheri var. lobata (8.06 mg Q/g DW). The highest concentration of mucilage was recorded in the flowers from Alcea arbelensis (32%), whereas those of A. aucheri var. lobata yielded the lowest value (1.05%). Principal component analysis scatter plot, derived from all data for flowers in 13 Alcea specimens revealed a different quantitative phytochemical profile. The strongest radical scavenging activity, measured by DPPH method, was recorded in A. aucheri var. aucheri with an IC50 of 34.06 μg/ml.

Assessment of genetic diversity in Moroccan fig (Ficus carica L.) collection by combining morphological and physicochemical descriptors

There are not many exhaustive works emphasizing the amount of genetic diversity based on multivariate analysis of a large sample of fig (Ficus carica L.) cultivars in Morocco. In this study, 96 local and 44 introduced fig cultivars were assessed using 41 descriptors. The studied genotypes exhibited a highly significant variability based on morphological, colorimetric and physicochemical traits. Most of characters showed a high coefficient of variations (> 30%) revealing a high level of phenotypic diversity among the genotypes. Principal component analysis showed that the first 10 components explained 60.26% of the total inertia. Fruit weight, caliber, stalk, neck size, fruit shape, and skin color coordinates contributed to most of the total variation. Matrix of correlations showed significant positive and negative correlations among variables particularly fruit sizes, shape and skin color. Cluster analysis using the Unweighted Pair Group Method with Arithmetic Averages procedure for studied accessions revealed two major clusters and five subclusters. Principal component analysis scatter plot expressed a low level of variance compared to the cluster. It confirmed the same classification of the fig accessions, where fruit dimensions and skin color coordinates were the most discriminant descriptors. The results highlight a high phenotypic diversity within studied fig germplasm, which is probably more genetically related. The accessions panel can be recognized as an important gene pool for future breeding programs. The present study is of importance for planning fig genetic resources inventory, preserving the existing genetic variability and establishing national collections.

Detection of Peanut Oil Adulteration Mixed with Rapeseed Oil Using Gas Chromatography and Gas Chromatography–Ion Mobility Spectrometry

In this study, the feasibility of gas chromatography (GC) and gas chromatography–ion mobility spectrometry (GC–IMS) systems for detecting peanut oil (PO) adulterated with rapeseed oil (RO) in different ratios was evaluated. In the GC analysis, the oils were categorized on the basis of fatty acid compositions. In the GC–IMS analysis, the oils were distinguished on the basis of volatile fractions. To demonstrate the high quality of the GC–IMS results, principal component analysis (PCA) and cluster analysis (CA) were conducted. The results showed that fatty acid profiles in PO exhibited higher amounts of palmitic acid (10.70 ± 0.18%), linoleic acid (32.87 ± 0.52%), and arachidic acid (2.30 ± 0.05%). RO had higher levels of linolenic acid (7.49 ± 0.36%) and erucic acid (8.02 ± 0.39%), which were not detected in PO. The linear regression analysis showed that the change in C18:2, C18:3, C20:1, and C22:1 content was highly correlated with the adulteration proportion of RO. However, only significant differences were visible in C18:3 and C20:1 when the adulteration proportion was a minimum of 5%. Satisfactory results of adulteration determination were obtained using the GC–IMS combined with the PCA and CA. Each oil type had unique characteristic compounds, and signals of some volatile compounds changed with the increase in the adulteration proportion especially at run times ranging from 100 to 300 s. For example, the intensity of peak signals of 89–92 increased with the addition of RO while peak intensity of 104 to 107 weakened. These individual signals revealed the volatile compounds responsible for the differences between PO and RO and could help to screen PO adulterated with RO. The established PCA scatter plots and CA could correctly differentiate authentic PO from adulterated samples with at least 1% of RO. Consequently, the GC–IMS shows higher efficiency and feasibility compared with the GC and may serve as a basis for the detection of adulteration of various substances.

Induced Mutation Proves a Potential Target for TB Therapy: A Molecular Dynamics Study on LprG.

Molecular dynamics (MD) simulations of wild-type and V91W mutant Mycobacterium tuberculosis-LprG (Mtb-LprG) were performed with the goal to provide a comprehensive understanding of the Mtb-LprG as a potential antimycobacterial target. A long-range MD simulations and post-MD analyzes led us to various results that plainly explained the impact of V91W mutation on Mtb-LprG. Herein, the results revealed that the wild-type is less stable compared to V91W mutant. This was further supported by root mean square fluctuation, where the V91W mutant showed a higher degree of flexibility compared to the wild-type. Dynamic cross-correlation analysis revealed that induced mutation leads to higher residual flexibility in the mutant structure as compared to the wild-type structure thus resulting in the existence of negatively correlated motions. The difference in principal component analysis scatter plot across the first two normal modes suggests a greater mobility of the V91W mutant conformation compared to the wild-type. Thermodynamic calculations revealed that the van der Waals (Evdw) forces contribute the most towards binding free energy in a case of the V91W mutant as compared to the wild-type LprG complex. In addition, the residue interaction networks revealed more of Evdw interaction existence among residues in case of the V91W mutant. This study supports the Mtb-LprG as a potential antimycobacterial target and also serves as a cornerstone to identifying new potential targets that have no inhibitors.

SERS Investigation of Cancer Cells Treated with PDT: Quantification of Cell Survival and Follow-up

In this study Surface Enhanced Raman Spectroscopy (SERS) data recorded from mouse mammary glands cancer cells (4T1 cell line) was used to assess information regarding differences between control, death and viable cells after Photodynamic Therapy (PDT) treatment. The treatment used nanoemulsions (NE/PS) loaded with different chloroaluminumphthalocyanine (ClAlP) photosensitizer (PS) contents (5 and 10 µmol × L−1) and illumination (660 nm wavelength) at 10 J × cm−2 (10 minutes). The SERS data revealed significant molecular alterations in proteins and lipids due to the PDT treatment. Principal Component Analysis (PCA) was applied to analyze the data recorded. Three-dimensional and well reproductive PCA scatter plots were obtained, revealing that two clusters of dead cells were well separated from one another and from control cluster. Overlap between two clusters of viable cells was observed, though well separated from control cluster. Moreover, the data analysis also pointed out necrosis as the main cell death mechanism induced by the PDT, in agreement with the literature. Finally, Raman modes peaking at 608 cm−1 (proteins) and 1231 cm−1 (lipids) can be selected for follow up of survival rate of neoplastic cells after PDT. We envisage that this finding is key to contribute to a quick development of quantitative infrared thermography imaging.