Characterization of Pleural Mesothelioma by Hierarchical Clustering Analyses Using Immune Cells within Tumor Microenvironment.

INTRODUCTION: Over the past decade, classifications using immune cell infiltration have been applied to many types of tumors; however, mesotheliomas have been less frequently evaluated. METHODS: In this study, 60 well-characterized pleural mesotheliomas (PMs) were evaluated immunohistochemically for the characteristics of immune cells within tumor microenvironment (TME) using 10 immunohistochemical markers: CD3, CD4, CD8, CD56, CD68, CD163, FOXP3, CD27, PD-1, and TIM-3. For further characterization of PMs, hierarchical clustering analyses using these 10 markers were performed. RESULTS: Among the immune cell markers, CD3 (p < 0.0001), CD4 (p = 0.0016), CD8 (p = 0.00094), CD163+ (p = 0.042), and FOXP3+ (p = 0.025) were significantly associated with an unfavorable clinical outcome. Immune checkpoint receptor expressions on tumor-infiltrating lymphocytes such as PD-1 (p = 0.050), CD27 (p = 0.014), and TIM-3 (p = 0.0098) were also associated with unfavorable survival. Hierarchical clustering analyses identified three groups showing specific characteristics and significant associations with patient survival (p = 0.016): the highest number of immune cells (ICHigh); the lowest number of immune cells, especially CD8+ and CD163+ cells (ICLow); and intermediate number of immune cells (ICInt). ICHigh tumors showed significantly higher expression of PD-L1 (p = 0.00038). Cox proportional hazard model identified ICHigh [hazard ratio (HR) = 2.90] and ICInt (HR = 2.97) as potential risk factors compared with ICLow. Tumor CD47 (HR = 2.36), tumor CD70 (HR = 3.04), and tumor PD-L1 (HR = 3.21) expressions were also identified as potential risk factors for PM patients. CONCLUSION: Our findings indicate immune checkpoint and/or immune cell-targeting therapies against CD70-CD27 and/or CD47-SIRPA axes may be applied for PM patients in combination with PD-L1-PD-1 targeting therapies in accordance with their tumor immune microenvironment characteristics.

Characterization of colorectal cancer by hierarchical clustering analyses of five immune cell markers.

The present study analyzed the expression of five independent immunohistochemical markers, CD4, CD8, CD66b, CD68, and CD163, on immune cells within the colorectal cancer (CRC) tumor microenvironment (TME). Using hierarchical clustering, patients were successfully classified according to significant associations with clinicopathological features and/or survival. Patients with mismatch repair-proficient (pMMR) CRC were categorized into four groups with survival differences (p = 0.0084): CD4Low , CD4High , MΦHigh , and CD8Low . MΦHigh tumors showed significantly higher expression of CD47 (p < 0.0001), a phagocytosis checkpoint molecule. These tumors contained significantly greater numbers of PD-1+ (p < 0.0001), TIM-3+ (p < 0.0001), and SIRPA+ (p < 0.0001) immune cells. Notably, 10% of the patients with pMMR CRC expressed PD-L1 (CD274) on tumor cells with significantly worse survival (p = 0.00064). The Cox proportional hazards model identified MΦ High (hazard ratio [HR] = 2.02, 95%, p = 0.032), CD8Low (HR = 2.45, p = 0.011), and tumor PD-L1 expression (HR = 2.74, p = 0.0061) as potential risk factors. PD-L1-PD-1 and/or CD47-SIRPA axes targeting immune checkpoint therapies might be considered for patients with pMMR CRC according to their tumor cells and tumor immune microenvironment characteristics.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

Comparison of the binding characteristics of SARS-CoV and SARS-CoV-2 RBDs to ACE2 at different temperatures by MD simulations.

Temperature plays a significant role in the survival and transmission of SARS-CoV (severe acute respiratory syndrome coronavirus) and SARS-CoV-2. To reveal the binding differences of SARS-CoV and SARS-CoV-2 receptor-binding domains (RBDs) to angiotensin-converting enzyme 2 (ACE2) at different temperatures at atomic level, 20 molecular dynamics simulations were carried out for SARS-CoV and SARS-CoV-2 RBD-ACE2 complexes at five selected temperatures, i.e. 200, 250, 273, 300 and 350 K. The analyses on structural flexibility and conformational distribution indicated that the structure of the SARS-CoV-2 RBD was more stable than that of the SARS-CoV RBD at all investigated temperatures. Then, molecular mechanics Poisson-Boltzmann surface area and solvated interaction energy approaches were combined to estimate the differences in binding affinity of SARS-CoV and SARS-CoV-2 RBDs to ACE2; it is found that the binding ability of ACE2 to the SARS-CoV-2 RBD was stronger than that to the SARS-CoV RBD at five temperatures, and the main reason for promoting such binding differences is electrostatic and polar interactions between RBDs and ACE2. Finally, the hotspot residues facilitating the binding of SARS-CoV and SARS-CoV-2 RBDs to ACE2, the key differential residues contributing to the difference in binding and the interaction mechanism of differential residues that exist at all investigated temperatures were analyzed and compared in depth. The current work would provide a molecular basis for better understanding of the high infectiousness of SARS-CoV-2 and offer better theoretical guidance for the design of inhibitors targeting infectious diseases caused by SARS-CoV-2.

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling.

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

Identification of Mid-Polar and Polar AhR Agonists in Cetaceans from Korean Coastal Waters: Application of Effect-Directed Analysis with Full-Scan Screening.

Major aryl hydrocarbon receptor (AhR) agonists were identified in extracts of blubber, liver, and muscle from six long-beaked common dolphins (Delphinus capensis) and one fin whale (Balaenoptera physalus) collected from Korean coastal waters using effect-directed analysis. Results of the H4IIE-luc bioassay indicated that the polar fractions of blubber and liver extracts from the fin whale exhibited relatively high AhR-mediated potencies. Based on full-scan screening with high-resolution mass spectrometry, 37 AhR agonist candidates, spanning four use categories: pharmaceuticals, pesticides, cosmetics, and natural products, were selected. Among these, five polar AhR agonists were newly identified through toxicological confirmation. Concentrations of polar AhR agonists in cetaceans were tissue-specific, with extracts of blubber and liver containing greater concentrations than muscle extracts. Polar AhR agonists with great log KOA values (>5) were found to biomagnify in the marine food chain potentially. Polar AhR agonists contributed 8.9% of the observed AhR-mediated potencies in blubber and 49% in liver. Rutaecarpine and alantolactone contributed significantly to the total AhR-mediated potencies of blubber, whereas hydrocortisone was a major AhR contributor in the liver of the fin whale. This study is the first to identify the tissue-specific accumulation of polar AhR agonists in blubber and liver extracts of cetaceans.

[Simultaneous determination of eleven volatile components in Cinnamomi Oleum by GC-MS].

A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.

[Comparison on volatile components between Artemisiae Verlotori Folium and Artemisiae Argyi Folium based on GC-MS and chemometrics].

Artemisiae Argyi Folium is commonly used in clinical practice. Artemisiae Verlotori Folium, the dried leaves of Artemisia verlotorum, is often used as a folk substitute for Artemisiae Argyi Folium in Lingnan area. In this study, gas chromatography-triple quadrupole mass spectrometry(GC-MS) was used to detect the volatile oil components of 27 samples of Artemisiae Verlotori Folium and 13 samples of Artemisiae Argyi Folium, and the volatile components were compared between the two species. The internal standard method was combined with multi-reaction monitoring mode(MRM) to determine the content of six major volatile components. Hierarchical clustering analysis(HCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were carried out for the content data. The results showed that the Artemisiae Argyi Folium samples had higher content and more abundant volatile oils than the Artemisiae Verlotori Folium samples. Artemisiae Argyi Folium mainly had the components with lower boiling points, while Artemisiae Verlotori Folium mainly had the components with higher boiling points. Terpenoids were the main volatile components in Artemisiae Verlotori Folium(mainly sesquiterpenoids) and Artemisiae Argyi Folium(monoterpenoids). In addition, Artemisiae Argyi Folium had higher content of oxygen-containing derivatives than Artemisiae Verlotori Folium. Furthermore, the stoichiometric analysis showed that the two species could be distinguished by both HCA and OPLS-DA, indicating that the volatile components of the two were significantly different. This study can provide a scientific basis for the quality evaluation and data support for the local rational application of Artemisiae Verlotori Folium in Lingnan.

Clustering of Chromatin Remodeling Enzymes Predicts Prognosis and Clinical Benefit of Therapeutic Strategy in Pancreatic Cancer.

In recent years, translational research and pharmacological targeting of epigenetic modifications have become the focus of personalized therapy for patients with pancreatic cancer. Preclinical and clinical trials targeting post-translational modifications have been evaluated as monotherapy or in combination with standard chemotherapy. In this study, we selected 43 genes from seven families of chromatin-modifying enzymes and investigated the influences of epigenetic modifications and their interactions on pancreatic ductal adenocarcinoma (PDAC) using hierarchical clustering analysis. Our analysis also evaluated their effects on treatment modalities and regimens of chemotherapy for PDAC. RNA-seq data for a total of 177 patients with pancreatic cancer, obtained from The Cancer Genome Atlas database, were analyzed. Our results suggested that high-risk patients of survival significant chromatin remodeling-associated gene cluster (gene cluster 2), composed of histone methyltransferases, histone acetyltransferases, histone deacetylases, histone demethylases, and 10-11 translocation family, demonstrated inferior progression-free survival and overall survival in patients with PDAC, especially in men. Our novel biomarker, survival significant chromatin remodeling-associated gene cluster, showed superior prediction performance compared with the conventional TNM system. Overall, these findings suggest that epigenetic modifications and interactions play an important role in the prognosis and therapeutic response of patients with PDAC.

Dental Caries Clusters among Adolescents in England, Wales, and Northern Ireland in 2013: Implications for Proportionate Universalism.

Management of dental caries in adolescents presents a population health challenge; thus, it is important to use national epidemiological data to inform policy and action to improve oral health and address inequalities. The aims of this research were to examine dental caries clusters among 15-year-olds, taking account of caries thresholds, and explore associated factors to inform public health action. Secondary analysis of the oral health data on 2,160 15-year-olds from the 2013 Children's Dental Health Survey in England, Wales, and Northern Ireland was performed. Hierarchical cluster analysis of dental caries experience was conducted across all surfaces and at 4 decay diagnostic thresholds (clinical: International Caries Detection and Assessment System [ICDAS] 1-6, cavitated: ICDAS 3-6, obvious: ICDAS 4-6, and extensive obvious: ICDAS 5-6 decay). Ordered logistic regression was used to estimate the association of behavioural and psychosocial factors with the clusters generated in relation to both clinical and obvious decay experience which are of clinical and epidemiological relevance. A 4-cluster decay pattern representing "low" to "extremely high" decay experience was observed under each of the dental caries diagnostic criteria. For clinical decay, which includes visual enamel caries, 28.70% had low, 39.77% medium, 26.71% high, and 4.81% extremely high caries status. In the adjusted model, significant risk factors for clinical decay included non-modifiable (sex, region, school type, and area deprivation) and modifiable (higher sugar intake at 4 or more times per day and suboptimal dental attendance) factors. This study suggests 4 distinct dental caries patterns among adolescent children nationally. Dental caries clusters demonstrate the importance of embracing proportionate universalism in addressing dental caries in the population oral health strategy.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

Simultaneous Determination and Comprehensive Evaluation of Mineral Elements in Yellow Croaker Ear-Stone on Sale in China.

Yellow Croaker Ear-stone or Yunaoshi, is actually two kinds of fish otolith in China and has received increased attention in recent years as important folk medicine. For better understanding of this crude drug, a chaotic market circulation status investigation was carried out and seventeen samples with different varieties or producing areas were collected. In this study, pharmacodynamic components of nineteen varieties mineral elements of the seventeen samples were simultaneously determined by Inductively coupled plasma mass spectrometry (ICP-MS) method. The detected elements were categorized into the beneficial (Na, Mg, Ca, K, Fe, Mn, Zn, Sr, B) and unbeneficial elements (Cu, As, Cd, Hg, Al, Pb, Co, Ba, Cr and Ni) kinds and their concentrations were quantified. Then the principal component analysis (PCA) and hierarchical clustering analysis (HCA) were further applied to launch an exploratory analysis for Yunaoshi samples. The results showed that samples 1-3, 15-8, 15-3 ranked the top three from the perspective of beneficial elements and samples 1-3, 1-4, 15-2 ranked the top three based on the unbeneficial elements sides. Combined with HCA results, all samples can be used as the substitutes for Yunaoshi except for samples 1-3, 1-4 and 15-2 only judging from the perspective of mineral elements concentrations. In conclusion, simultaneous determination of mineral elements accompanied with PCA and HCA can not only provide pharmacogenetic reference for the medicinal material of Yunaoshi, but also establish a feasibility for exploring new crude resources or substitutes to this medicine.

1H NMR Combined with Multivariate Statistics for Discrimination of Female and Male Flower Buds of Populus tomentosa.

1H Nuclear Magnetic Resonance (1H NMR) combined with multivariate statistics was adopted to discriminate female and male flower buds of Populus tomentosa in the study. Samples of 11 female and 16 male flower buds of P. tomentosa were collected in Beijing, China. 1H NMR spectra were acquired on a 400 MHz spectrometer. In total, 30 chemical compounds were identified with standards and literature according to chemical shifts, peak areas, and multiplicity. Principal component analysis (PCA), hierarchical clustering analysis (HCA), and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to discriminate female and male flower buds. An apparent grouping trend (R2X, 0.809; Q2, 0.903) between female and male groups was exhibited with PCA and HCA. The two groups were also well discriminated with OPLS-DA (R2X, 0.808; R2Y, 0.976; Q2, 0.960). Combined with variable importance in projection (VIP) > 1.0 and p < 0.05 of OPLS-DA, it was found that the content of daucosterol, ß-sitosterol, ursolic acid, and betulonic acid in male group was higher than that in female, which should be the key differences of chemical constituents in female and male flower buds of P. tomentosa. The study demonstrated that 1H NMR combined with multivariate statistics could be used to discriminate female and male plants and clarify differences, which provided a novel method to identify the gender of dioecious plants.

Characterization of Potential Protein Biomarkers for Major Depressive Disorder Using Matrix-Assisted Laser Desorption Ionization/Time-of-Flight Mass Spectrometry.

Matrix-assisted laser desorption ionization/time-of-flight (MALDI-TOF) mass spectrometry is a sensitive analytical tool for characterizing various biomolecules in biofluids. In this study, MALDI-TOF was used to characterize potential plasma biomarkers for distinguishing patients with major depressive disorder (MDD) from patients with schizophrenia and healthy controls. To avoid interference from albumin-the predominant protein in plasma-the plasma samples were pretreated using acid hydrolysis. The results obtained by MALDI-TOF were also validated by electrospray ionization-quadrupole time-of-flight (ESI-QTOF) mass spectrometry. The analytical results were further treated with principal component analysis (PCA), hierarchical clustering analysis (HCA), and receiver operating characteristic (ROC) curve analysis. The statistical analyses showed that MDD patients could be distinguished from schizophrenia patients and healthy controls by the lack of apolipoprotein C1 (Apo C1), which, in fact, was detected in healthy controls and schizophrenia patients. This protein is suggested to be a potential plasma biomarker for distinguishing MDD patients from healthy controls and schizophrenia patients. Since sample preparation for MALDI-TOF is very simple, high-throughput plasma apolipoprotein analysis for clinical purposes is feasible.

Heavy metal accumulation and health risk assessment of crayfish in the middle and lower reaches of Yangtze River during 2015-2017.

This study performed statistical analysis and risk assessment of five heavy metals (As, Cd, Cr, Hg, Pb) in crayfish samples collected from six provinces in the middle and lower reaches of Yangtze River during 2015-2017. The Spearman correlation test and the results of hierarchical cluster analysis (HCA) indicated that As, Pb, and Cd in crayfish were significantly correlated, and the results of HCA showed that Jiangxi, Jiangsu, and Zhejiang were clustered into one group; Hubei, Hunan, and Anhui were clustered into another group; and provinces in the same group had spatial similarities in heavy metals. The pollution index (PI) values of five heavy metals in all provinces were below 1, implying that crayfish samples in this area were not highly contaminated. The target hazard quotient (THQ) values of five heavy metals were mainly below 1 except Hg in Anhui (2.9709), which was far beyond 1, indicating that the health risk posed by Hg exposure should not be ignored in Anhui.

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model.

Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver's age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.

Rapid tool for identification of bacterial strains using Fourier transform infrared spectroscopy on genomic DNA.

AIMS: We developed a new rapid and reliable method for identifying bacteria using a combination of Fourier transform infrared (FT-IR) spectroscopy of bacterial genomic DNA and multivariate analysis. METHODS AND RESULTS: FT-IR spectra of genomic DNA from four type strains of Pseudomonas spp., three type strains of Escherichia spp. and two type strains of Bacillus spp. were analysed in the 4000-400 cm-1 region. Spectral differences were found in the frequency regions of N-H stretching (amide I), C=O stretching vibrations (amide II) and PO2 - ionized asymmetric and symmetric stretching. Partial least squares discriminant analysis of the FT-IR spectra showed that the microbial strains could be discriminated by hierarchical clustering analysis. CONCLUSIONS: FT-IR spectral analysis of bacterial genomic DNA has potential for the rapid identification of bacteria at the genus and species levels. SIGNIFICANCE AND IMPACT OF THE STUDY: This study reports a new bacterial identification method using multivariate analysis of FT-IR spectra of bacterial genomic DNA.

Simultaneous determination of eight flavonoids in Sedum sarmentosum Bunge from different areas by UHPLC with triple quadrupole MS/MS.

Sedum sarmentosum Bunge (SSB) is a traditional Chinese herbal medicine containing multiple components that has been extensively used clinically to treat chronic viral hepatitis and some inflammatory diseases. Total flavonoids are major pharmacologically active components of SSB. To gain a deeper understanding of SSB resources, we analyzed eight chemical constituents in 33 batches of SSB from 11 regions in China. An accurate, precise and sensitive ultra-high-performance liquid chromatography coupled with triple quadrupole electrospray tandem mass spectrometry method was developed for the determination of eight flavonoids in SSB. Under the optimized chromatographic conditions, good separation for the eight target components was obtained on an Agilent Zobax SB C18 (50 × 2.1 mm, 5 µm) column within 4 min. The established methods were validated with good linearity (r ≥ 0.9988), precision (RSD ≤ 2.68%), stability (1.43-3.28%) and repeatability (1.14-2.89%). Moreover, the average recoveries were 95.91-100.68%, and the RSDs were 1.50-3.80%. In addition, the analytical conditions of UPLC-ESI-MS/MS provided better sensitivity with a shorter analysis time when compared with the HPLC-DAD method. Hierarchical clustering analysis and principal component analysis were performed to estimate and classify these samples based on the contents of the eight chemical constituents. This study provided the theoretical basis and scientific evidence for the development and utilization of SSB resources.

Sensory Characteristics and Antioxidant Activity of Zanthoxylum bungeanum Maxim. Pericarps.

Zanthoxylum bungeanum extracts were prepared using seven solvents: water, methanol, ethanol, acetic acid, ethyl acetate, chloroform, and benzene. The volatile composition in the extracts was qualitatively analyzed using headspace solid-phase microextraction coupled with gas chromatography mass spectrometry detection, and the alkylamide composition was determined using high-performance liquid chromatography. The extract compositions differed with respect to the solvents. A total of 49 volatile components belonging to four groups, terpenoids, alcohols, esters, and ketones, were identified in the extracts. The Z. bungeanum extracts were either ester or terpenoid type, dominated by linalyl acetate. The extracts were divided into three distinct groups based on principal component analysis and hierarchical clustering analysis. Water, methanol, and ethanol extracts could be applied in the food and pharmaceutical industries.

Applying MSSIM combined chaos game representation to genome sequences analysis.

Converting DNA sequence to image by using chaos game representation (CGR) is an effective genome sequence pretreatment technology, which provides the basis for further analysis between the different genes. In this paper, we have constructed 10 mammal species, 48 hepatitis E virus (HEV), and 10 kinds of bacteria genetic CGR images, respectively, to calculate the mean structural similarity (MSSIM) coefficient between every two CGR images. From our analysis, the MSSIM coefficient of gene CGR images can accurately reflect the similarity degrees between different genomes. Hierarchical clustering analysis was used to calculate the class affiliation and construct a dendrogram. Large numbers of experiments showed that this method gives comparable results to the traditional Clustal X phylogenetic tree construction method, and is significantly faster in the clustering analysis process. Meanwhile MSSIM combined CGR method was also able to efficiently clustering of large genome sequences, which the traditional multiple sequence alignment methods (e.g. Clustal X, Clustal Omega, Clustal W, et al.) cannot classify.