LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling.

INTRODUCTION: This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation. METHOD: A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS). RESULTS: The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure. CONCLUSIONS: The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis.

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

Association between multiple vitamins and bone mineral density: a cross-sectional and population-based study in the NHANES from 2005 to 2006.

BACKGROUND: Bone mineral density (BMD) alterations in response to multivitamin exposure were rarely studied. Our study assessed the association of coexposure to six types of vitamins (i.e., vitamins B12, B9, C, D, A and E) with BMD measurements in adults in the US. METHODS: Data were collected from participants aged ≥ 20 years (n = 2757) in the U.S. National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2006. Multiple linear regression, restricted cubic splines, principal component analysis (PCA) and weighted quantile sum (WQS) regression were performed for statistical analysis. RESULTS: The circulating levels of vitamins B12 and C were positively associated with BMDs, and an inverted L-shaped exposure relationship was observed between serum vitamin C and BMDs. PCA identified two principal components: one for 'water-soluble vitamins', including vitamins B12, B9 and C, and one for 'fat-soluble vitamins', including vitamins A, D and E. The former was positively associated with total femur (ß = 0.009, 95%CI: 0.004, 0.015) and femoral neck (ß = 0.007, 95%CI: 0.002, 0.013) BMDs, and the latter was negatively associated with BMDs with non-statistical significance. The WQS index constructed for the six vitamins was significantly related to total femur (ß = 0.010, 95%CI: 0.001, 0.018) and femoral neck (ß = 0.008, 95%CI: 0.001, 0.015) BMDs, and vitamins B12 and C weighted the most. The WQS index was inversely related to BMDs with non-statistical significance, and vitamins E and A weighted the most. CONCLUSION: Our findings suggested a positive association between water-soluble vitamin coexposure and BMD, and the association was mainly driven by vitamins B12 and C. Negative association between fat-soluble vitamin coexposure and BMD was indicated, mainly driven by vitamins E and A. An inverted L-shaped exposure relationship was found between vitamin C and BMD.

Biochemical Composition, Antioxidant Activity and Antiproliferative Effects of Different Processed Garlic Products.

Garlic (Allium sativum L.) is a type of agricultural product that is widely used as a food spice, herb and traditional medicine. White garlic (WG) can be processed into several kinds of products, such as green garlic (GG), Laba garlic (LAG) and black garlic (BG), which have multiple health effects. In this study, GC-MS (gas chromatography-mass spectrometry), DPPH (1,1'-diphenyl-2-propionyl hydrazide) radical scavenging, hydroxyl radical scavenging and MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) in vitro assays were used to compare the composition, antioxidant and antiproliferation effects of different processed garlic extracts. The relationship between the constituents and the bioactivities was analyzed using the principal components analysis (PCA) and heatmap analysis. BG showed the highest antioxidant activity (IC50 = 0.63 ± 0.02 mg/mL) in DPPH radical assays and the highest antioxidant activity (IC50 = 0.80 ± 0.01 mg/mL) by hydroxyl radical assay. Moreover, GC-MS results showed that 12 organosulfur compounds were detected in the extracts of four garlic products, and allyl methyl trisulfide showed a positive relation with the anticancer activity on SMMC-7721 cells (hepatocellular carcinoma cells). The results suggested that the processing of garlic had a significant influence on the constituents and antioxidant effects and that GG, LAG and BG might be better candidates for the related functional food products compared to WG.

Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part II. Uncertainty Intervals.

Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions.

Scale-Mark-Based Gauge Reading for Gauge Sensors in Real Environments with Light and Perspective Distortions.

Nowadays, many old analog gauges still require the use of manual gauge reading. It is a time-consuming, expensive, and error-prone process. A cost-effective solution for automatic gauge reading has become a very important research topic. Traditionally, different types of gauges have their own specific methods for gauge reading. This paper presents a systematized solution called SGR (Scale-mark-based Gauge Reading) to automatically read gauge values from different types of gauges. Since most gauges have scale marks (circular or in an arc), our SGR algorithm utilizes PCA (principal components analysis) to find the primary eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the gauge center to ascertain the scale marks. Then, the endpoint of the gauge pointer is found to calculate the corresponding angles to the gauge's center. Using OCR (optical character recognition), the corresponding dial values can be extracted to match with their scale marks. Finally, the gauge reading value is obtained by using the linear interpolation of these angles. Our experiments use four videos in real environments with light and perspective distortions. The gauges in the video are first detected by YOLOv4 and the detected regions are clipped as the input images. The obtained results show that SGR can automatically and successfully read gauge values. The average error of SGR is nearly 0.1% for the normal environment. When the environment becomes abnormal with respect to light and perspective distortions, the average error of SGR is still less than 0.5%.

Research on Minimization of Data Set for State of Charge Prediction.

The quick estimation and prediction of lithium-ion batteries' (LIBs) state of charge (SoC) are attracting growing attention, since the LIB has become one of the most essential power sources for daily consumer electronics. Most deep learning methods require plenty of data and more than two LIB parameters to train the model for predicting SoC. In this paper, a single-parameter SoC prediction based on deep learning is realized by cleaning the data for lithium-ion battery parameters and constructing the feature matrix based on the cleaned data. Then, by analyzing the feature matrix's periodicity and principal component to obtain two kinds of the original eigenmatrix's substitution matrices, the two substitutions are fused to obtain an excellent prediction effect. In the end, the minimization method is verified with newly measured lithium battery data, and the results show that the MAPE of the SoC prediction reaches 0.96%, the input data are reduced by 93.33%, and the training time is reduced by 96.68%. Fast and accurate prediction of the SoC is achieved by using only a minimum amount of voltage data.

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model.

Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2 Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis.

Using the right tool for the job: the difference between unsupervised and supervised analyses of multivariate ecological data.

Ecologists often collect data with the aim of determining which of many variables are associated with a particular cause or consequence. Unsupervised analyses (e.g. principal components analysis, PCA) summarize variation in the data, without regard to the response. Supervised analyses (e.g., partial least squares, PLS) evaluate the variables to find the combination that best explain a causal relationship. These approaches are not interchangeable, especially when the variables most responsible for a causal relationship are not the greatest source of overall variation in the data-a situation that ecologists are likely to encounter. To illustrate the differences between unsupervised and supervised techniques, we analyze a published dataset using both PCA and PLS and compare the questions and answers associated with each method. We also use simulated datasets representing situations that further illustrate differences between unsupervised and supervised analyses. For simulated data with many correlated variables that were unrelated to the response, PLS was better than PCA at identifying which variables were associated with the response. There are many applications for both unsupervised and supervised approaches in ecology. However, PCA is currently overused, at least in part because supervised approaches, such as PLS, are less familiar.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites.

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Evolution of bovine brucellosis in Colombia over a 7-year period (2006-2012).

Bovine brucellosis is endemic in Colombia, and is a mandatory notifiable disease, subjected to a control program based on four surveillance procedures: passive surveillance, test-and-remove, certification of disease-free farms, and animal movements. The objective of this study is to estimate the evolution of bovine brucellosis in Colombia over a 7-year period (2006-2012) using data from the official control program. A total of 58 epidemiologic variables were analyzed for each year at the department level. Univariate descriptive analysis and principal components analysis (PCA) were performed to ascertain the behavior of the variables. These programs covered 3% of the census in 2006, increasing to 15% in 2012. The percentage of positive farms averaged 22% in 2006 and 23% in 2012. The highest proportion of positive farms was in the Orinoquía region (24.6 to 49.6%); the lowest was in the Amazon region, (17.9 to 32.7%). The percentage of positive animals presented certain differences between years but without any clear trend (4.7% in 2006 and 4.6% in 2012), indicating that the brucellosis control program had a low impact in Colombia in these years. The results for each surveillance procedure were 6.8% for passive surveillance, 5.9% for test-and-remove, and 4.4% both in disease-free farms and in animal movement tests. The results obtained by PCA led to finding three different clusters: geographic areas with low bovine production and low bovine brucellosis surveillance, areas with medium bovine production and medium surveillance for bovine brucellosis, and areas with a predominant bovine production, applying sanitary measures to control bovine brucellosis.

A pilot study on fingerprinting Leishmania species from the Old World using Fourier transform infrared spectroscopy.

Leishmania species are protozoan parasites and the causative agents of leishmaniasis, a vector borne disease that imposes a large health burden on individuals living mainly in tropical and subtropical regions. Different Leishmania species are responsible for the distinct clinical patterns, such as cutaneous, mucocutaneous, and visceral leishmaniasis, with the latter being potentially fatal if left untreated. For this reason, it is important to perform correct species identification and differentiation. Fourier transform infrared spectroscopy (FTIR) is an analytical spectroscopic technique increasingly being used as a potential tool for identification of microorganisms for diagnostic purposes. By employing mid-infrared (MIR) spectral data, it is not only possible to assess the chemical structures but also to achieve differentiation supported by multivariate statistic analysis. This work comprises a pilot study on differentiation of Leishmania species of the Old World (L. major, L. tropica, L. infantum, and L. donovani) as well as hybrids of distinct species by using vibrational spectroscopic fingerprints. Films of intact Leishmania parasites and their deoxyribonucleic acid (DNA) were characterized comparatively with respect to their biochemical nature and MIR spectral patterns. The strains' hyperspectral datasets were multivariately examined by means of variance-based principal components analysis (PCA) and distance-based hierarchical cluster analysis (HCA). With the implementation of MIR spectral datasets we show that a phenotypic differentiation of Leishmania at species and intra-species level is feasible. Thus, FTIR spectroscopy can be further exploited for building up spectral databases of Leishmania parasites in view of high-throughput analysis of clinical specimens. Graphical abstract For Leishmania species discrimination, sample films of intact parasites and their extracted DNA were analyzed by FTIR micro-spectroscopy. Hyperspectral datasets that comprise mid-infrared fingerprints were submitted to multivariate analysis tools such as principal components analysis (PCA) and hierarchical cluster analysis (HCA).

Neuronal generator patterns at scalp elicited by lateralized aversive pictures reveal consecutive stages of motivated attention.

Event-related potential (ERP) studies have provided evidence for an allocation of attentional resources to enhance perceptual processing of motivationally salient stimuli. Emotional modulation affects several consecutive components associated with stages of affective-cognitive processing, beginning as early as 100-200ms after stimulus onset. In agreement with the notion that the right parietotemporal region is critically involved during the perception of arousing affective stimuli, some ERP studies have reported asymmetric emotional ERP effects. However, it is difficult to separate emotional from non-emotional effects because differences in stimulus content unrelated to affective salience or task demands may also be associated with lateralized function or promote cognitive processing. Other concerns pertain to the operational definition and statistical independence of ERP component measures, their dependence on an EEG reference, and spatial smearing due to volume conduction, all of which impede the identification of distinct scalp activation patterns associated with affective processing. Building on prior research using a visual half-field paradigm with highly controlled emotional stimuli (pictures of cosmetic surgery patients showing disordered [negative] or healed [neutral] facial areas before or after treatment), 72-channel ERPs recorded from 152 individuals (ages 13-68years; 81 female) were transformed into reference-free current source density (CSD) waveforms and submitted to temporal principal components analysis (PCA) to identify their underlying neuronal generator patterns. Using both nonparametric randomization tests and repeated measures ANOVA, robust effects of emotional content were found over parietooccipital regions for CSD factors corresponding to N2 sink (212ms peak latency), P3 source (385ms) and a late centroparietal source (630ms), all indicative of greater positivity for negative than neutral stimuli. For the N2 sink, emotional effects were right-lateralized and modulated by hemifield, with larger amplitude and asymmetry for left hemifield (right hemisphere) presentations. For all three factors, more positive amplitudes at parietooccipital sites were associated with increased ratings of negative valence and greater arousal. Distributed inverse solutions of the CSD-PCA-based emotional effects implicated a sequence of maximal activations in right occipitotemporal cortex, bilateral posterior cingulate cortex, and bilateral inferior temporal cortex. These findings are consistent with hierarchical activations of the ventral visual pathway reflecting subsequent processing stages in response to motivationally salient stimuli.

Variations in chemical fingerprints and major flavonoid contents from the leaves of thirty-one accessions of Hibiscus sabdariffa L.

The leaves of Hibiscus sabdariffa L. have been used as traditional folk medicines for treating high blood pressure and fever. There are many accessions of H. sabdariffa L. throughout the world. To assess the chemical variations of 31 different accessions of H. sabdariffa L., fingerprinting analysis and quantitation of major flavonoids were performed by high-performance liquid chromatography (HPLC). The HPLC method was validated for linearity, sensitivity, precision, repeatability and accuracy. A quadrupole-time-of-flight mass spectrometry (Q-TOF-MS) was applied for the characterization of major compounds. A total of 9 compounds were identified, including 6 flavonoids and 3 phenolic acids. In the fingerprint analysis, similarity analysis (SA) and principal component analysis (PCA) were used to differentiate the 31 accessions of H. sabdariffa L. Based on the results of PCA and SA, the samples No. 15 and 19 appeared much different from the main group. The total content of five flavonoids varied greatly among different accessions, ranging from 3.35 to 23.30 mg/g. Rutin was found to be the dominant compound and the content of rutin could contribute to chemical variations among different accessions. This study was helpful to understand the chemical variations between different accessions of H. sabdariffa L., which could be used for quality control. © 2015 The Authors Biomedical Chromatography Published by John Wiley & Sons Ltd.

Estimating the concentration of urea and creatinine in the human serum of normal and dialysis patients through Raman spectroscopy.

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.

[Determination of six alkaloids in six types of Coptidis Rhizoma pieces by RP-HPLC and spectrum-effect relationships with anti-diabetes pharmacodynamics data].

To establish a method for determining the contents of six alkaloids (jatrorrhizine hydrochloride, columbamine hydrochloride, epiberberine hydrochloride, coptisine hydrochloride, palmatine hydrochloride, berberine hydrochloride) in six types of Coptidis Rhizoma pieces (crude pieces, ginger juice stir-fried pieces, vinegar stir-fried pieces, wine steamed pieces, wine stir-fried pieces, evodiae juice stir-fried pieces) by RP-HPLC, and explore the relationship with the curative effect of traditional Chinese medicine (TCM) and pharmacodynamics results. The chromatographic column was Welch XtimateTM C18 (4.6 mm×250 mm, 5 µm), with 0.1% triethylamine solution (adjust pH at 10 with ammonium bicarbonate and ammonia) as mobile phase A and acetonitrile as mobile phase B for gradient elution (0-15 min, 10%-25%B; 15-25 min, 25%-30%B; 25-40 min, 30%-45%B) at a rate of 1.0 mL•min⁻¹. The column temperature was set at 30 ℃, and the wavelength was set at 270 nm. The six alkaloids showed a good linear relationship within the range of 0.85-16.96 mg•L⁻¹ (r=0.999 7), 1.25-24.96 mg•L⁻¹ (r=0.999 9), 2.05-40.96 mg•L⁻¹ (r=0.999 9), 3.65-72.96 mg•L⁻¹ (r=0.999 9), 2.88-57.60 mg•L⁻¹ (r=0.999 8), and 13.25-264.96 mg•L⁻¹ (r=0.999 6) respectively. The average recoveries (n=9) of the six alkaloids were 102.4% (RSD 1.2%), 101.8% (RSD 1.3%), 100.3% (RSD 1.8%), 100.7%(RSD 1.8%), 101.2% (RSD 1.5%) and 97.90% (RSD 2.0%) respectively, and their average contents were 3.55, 4.49, 9.12, 19.17, 15.69, 62.56 mg•g⁻¹, respectively. This determination method was accurate and repeatable, which could be used for the content determination in six types of Coptidis Rhizoma pieces. Data analysis on contents determination and preliminary pharmacodynamics results was conducted by using principal component analysis (PCA) and hierarchical clustering analysis (HCA). The analysis results showed that three types of Coptidis Rhizoma pieces (wine steamed pieces, wine stir-fried pieces, and evodiae juice stir-fried pieces) had significant differences with crude pieces, and the wine steamed Coptidis Rhizoma pieces showed most difference with crude pieces especially, mainly related to triglyceride (TG) and fasting blood glucose levels (FBG) in serum. In addition, columbamine hydrochloride was most affected among the six alkaloids. Those three types of Coptidis Rhizoma pieces (wine steamed pieces, wine stir-fried pieces, and evodiae juice stir-fried pieces), had more advantages for "anti-diabetes" in TCM clinical application, especially in the treatment of diabetic hyperlipidemia.

Characterizing visible and invisible cell wall mutant phenotypes.

About 10% of a plant's genome is devoted to generating the protein machinery to synthesize, remodel, and deconstruct the cell wall. High-throughput genome sequencing technologies have enabled a reasonably complete inventory of wall-related genes that can be assembled into families of common evolutionary origin. Assigning function to each gene family member has been aided immensely by identification of mutants with visible phenotypes or by chemical and spectroscopic analysis of mutants with 'invisible' phenotypes of modified cell wall composition and architecture that do not otherwise affect plant growth or development. This review connects the inference of gene function on the basis of deviation from the wild type in genetic functional analyses to insights provided by modern analytical techniques that have brought us ever closer to elucidating the sequence structures of the major polysaccharide components of the plant cell wall.

Essential-oil variability of Juniperus deltoides R.P.Adams along the east Adriatic coast - how many chemotypes are there?

The composition of the essential oils isolated from twigs of ten Juniperus deltoides R.P. Adams populations from the east Adriatic coast was determined by GC-FID and GC/MS analyses. Altogether, 169 compounds were identified, representing 95.6-98.4% of the total oil composition. The oils were dominated by monoterpenes (average content of 61.6%), which are characteristic oil components of species of the Juniperus section. Two monoterpenes, α-pinene and limonene, were the dominant constituents, comprising on average 46.78% of the essential oils. Statistical methods were deployed to determine the diversity of the terpene classes and the common terpenes between the investigated populations. These statistical analyses revealed the existence of three chemotypes within all populations, i.e., a α-pinene, limonene, and limonene/α-pinene type.

Structure and Principal Components Analyses Reveal an Intervarietal Fusion in Malaysian Mistletoe Fig (Ficus deltoidea Jack) Populations.

Genetic structure and biodiversity of the medicinal plant Ficus deltoidea have rarely been scrutinized. To fill these lacunae, five varieties, consisting of 30 F. deltoidea accessions were collected across the country and studied on the basis of molecular and morphological data. Molecular analysis of the accessions was performed using nine Inter Simple Sequence Repeat (ISSR) markers, seven of which were detected as polymorphic markers. ISSR-based clustering generated four clusters supporting the geographical distribution of the accessions to some extent. The Jaccard's similarity coefficient implied the existence of low diversity (0.50-0.75) in the studied population. STRUCTURE analysis showed a low differentiation among the sampling sites, while a moderate varietal differentiation was unveiled with two main populations of F. deltoidea. Our observations confirmed the occurrence of gene flow among the accessions; however, the highest degree of this genetic interference was related to the three accessions of FDDJ10, FDTT16 and FDKT25. These three accessions may be the genetic intervarietal fusion points of the plant's population. Principal Components Analysis (PCA) relying on quantitative morphological characteristics resulted in two principal components with Eigenvalue >1 which made up 89.96% of the total variation. The cluster analysis performed by the eight quantitative characteristics led to grouping the accessions into four clusters with a Euclidean distance ranged between 0.06 and 1.10. Similarly, a four-cluster dendrogram was generated using qualitative traits. The qualitative characteristics were found to be more discriminating in the cluster and PCA analyses, while ISSRs were more informative on the evolution and genetic structure of the population.

Characterization of Volatile Compounds of Eleven Achillea Species from Turkey and Biological Activities of Essential Oil and Methanol Extract of A. hamzaoglui Arabaci & Budak.

According to distribution of genus Achillea, two main centers of diversity occur in S.E. Europe and S.W. Asia. Diversified essential oil compositions from Balkan Peninsula have been numerously reported. However, report on essential oils of Achillea species growing in Turkey, which is one of the main centers of diversity, is very limited. This paper represents the chemical compositions of the essential oils obtained by hydrodistillation from the aerial parts of eleven Achillea species, identified simultaneously by gas chromatography and gas chromatography-mass spectrometry. The main components were found to be 1,8-cineole, p-cymene, viridiflorol, nonacosane, α-bisabolol, caryophyllene oxide, α-bisabolon oxide A, ß-eudesmol, 15-hexadecanolide and camphor. The chemical principal component analysis based on thirty compounds identified three species groups and a subgroup, where each group constituted a chemotype. This is the first report on the chemical composition of A. hamzaoglui essential oil; as well as the antioxidant and antimicrobial evaluation of its essential oil and methanolic extract.