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.

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.

Identifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learning.

To use unsupervised machine learning to identify potential subphenotypes of preterm infants with patent ductus arteriosus (PDA). The study was conducted retrospectively at a neonatal intensive care unit in Brazil. Patients with a gestational age < 28 weeks who had undergone at least one echocardiogram within the first two weeks of life and had PDA size > 1.5 or LA/AO ratio > 1.5 were included. Agglomerative hierarchical clustering on principal components was used to divide the data into different clusters based on common characteristics. Two distinct subphenotypes of preterm infants with hemodynamically significant PDA were identified: "inflamed," characterized by high leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio, and "respiratory acidosis," characterized by low pH and high pCO2 levels.    Conclusions: This study suggests that there may be two distinct subphenotypes of preterm infants with hemodynamically significant PDA: "inflamed" and "respiratory acidosis." By dividing the population into different subgroups based on common characteristics, it is possible to get a more nuanced understanding of the effectiveness of PDA interventions. What is Known: • Treatment of PDA in preterm infants has been controversial. • Stratification of preterm infants with PDA into subgroups is important in order to determine the best treatment. What is New: • Unsupervised machine learning was used to identify two subphenotypes of preterm infants with hemodynamically significant PDA. • The 'inflamed' cluster was characterized by higher values of leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio. The 'respiratory acidosis' cluster was characterized by lower pH values and higher pCO2 values.

Automated Operational Modal Analysis for Rotating Machinery Based on Clustering Techniques.

Many parameters can be used to express a machine's condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required.

Sistema de Cribado Primario para la Sarcopenia en Personas Adultas Mayores Basado en Inteligencia Artificial / Primary Screening System for Sarcopenia in Elderly People Based on Artificial Intelligence

Resumen Este estudio propone un sistema de cribado primario para diagnosticar la sarcopenia en adultos mayores a través de medidas antropométricas. Esta investigación exploratoria involucró inicialmente a 150 personas de edad avanzada, de las cuales 122 fueron seleccionadas después de un proceso de depuración de datos. Empleando técnicas de aprendizaje automático como el agrupamiento jerárquico y los árboles de decisión, se redujeron las 13 medidas antropométricas originales a cinco características clave. Se crearon tres sistemas de clasificación: el primero basado en parámetros previamente establecidos (masa muscular apendicular, velocidad de marcha y fuerza de agarre); el segundo consideró medidas de las extremidades superiores (masa muscular promedio de ambos brazos, fuerza de agarre, velocidad de marcha y porcentaje de grasa corporal); y el tercero se centró en las medidas de las extremidades inferiores (masa muscular promedio de ambas piernas, fuerza de agarre, velocidad de marcha y porcentaje de grasa corporal). Estos sistemas de clasificación se validaron clínicamente en un grupo de 57 pacientes previamente diagnosticados por especialistas, de los cuales 10 recibieron un diagnóstico positivo de sarcopenia. Los resultados mostraron eficiencias similares en los tres sistemas, con ocho de los diez diagnósticos positivos conocidos clasificados en el mismo grupo. Además, el estudio proporciona puntos de corte específicos para cada sistema, facilitando así el diagnóstico clínico de la sarcopenia por parte de profesionales médicos.

Abstract This study proposes a primary screening system for diagnosing sarcopenia in older adults through anthropometric measures. This exploratory research initially involved 150 elderly individuals, of whom 122 were selected after a data purification process. Using machine learning techniques such as hierarchical clustering and decision trees, the original set of 13 anthropometric measures was reduced to five key features. Three classification systems were created: the first based on previously established parameters (appendicular muscle mass, walking speed, and grip strength); the second considered upper limb measures (average muscle mass of both arms, grip strength, walking speed, and body fat percentage); and the third focused on lower limb measures (average muscle mass of both legs, grip strength, walking speed, and body fat percentage). These classification systems were clinically validated in a group of 57 patients previously diagnosed by specialists, of which 10 received a positive sarcopenia diagnosis. The results showed similar efficiencies in all three systems, with eight of the ten known positive diagnoses classified in the same group. Additionally, the study provides specific cut-off points for each system, thus facilitating the clinical diagnosis of sarcopenia by medical professionals.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

Clustering Analysis of the Multi-Microbial Consortium by Lactobacillus Species Against Vaginal Dysbiosis Among Ecuadorian Women.

The vaginal microbiota plays vital protection in women. This probiotic activity is caused not only by individual Lactobacillus species but also by its multi-microbial interaction. However, the probiotic activity promoted by multi-microbial consortia is still unknown. The aim of this study was the individual and collective analysis on the prevalence of five vaginal lactobacilli (Lactobacillus iners, Lactobacillus crispatus, Lactobacillus gasseri, Lactobacillus jensenii, and Lactobacillus acidophilus) among healthy women and women with bacterial vaginosis (BV) or aerobic vaginitis (AV). PCR assays were realized on 436 vaginal samples from a previous study. Chi-square, univariable, and multivariable logistic regression analyses with the Benjamini-Hochberg adjustment evaluated associations between these lactobacilli and vaginal microbiota. Multi-microbial clustering model was also realized through Ward's Minimum Variance Clustering Method with Euclidean squared distance for hierarchical clustering to determine the probiotic relationship between lactobacilli and vaginal dysbiosis. Concerning the individual effect, L. acidophilus, L. jensenii, and L. crispatus showed the highest normalized importance values against vaginal dysbiosis (100%, 79.3%, and 74.8%, respectively). However, only L. acidophilus and L. jensenii exhibited statistical values (p = 0.035 and p = 0.050, respectively). L. acidophilus showed a significant prevalence on healthy microbiota against both dysbioses (BV, p = 0.041; and AV, p = 0.045). L. jensenii only demonstrated significant protection against AV (p = 0.012). Finally, our results evidenced a strong multi-microbial consortium by L. iners, L. jensenii, L. gasseri, and L. acidophilus against AV (p = 0.020) and BV (p = 0.009), lacking protection in the absence of L. gasseri and L. acidophilus.

Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds.

Molecular docking is a useful and powerful computational method for the identification of potential interactions between small molecules and pharmacological targets. In reverse docking, the ability of one or a few compounds to bind a large dataset of proteins is evaluated in silico. This strategy is useful for identifying molecular targets of orphan bioactive compounds, proposing new molecular mechanisms, finding alternative indications of drugs, or predicting drug toxicity. Herein, we describe a detailed reverse docking protocol for the identification of potential targets for 4-hydroxycoumarin (4-HC). Our results showed that RAC1 is a target of 4-HC, which partially explains the biological activities of 4-HC on cancer cells. The strategy reported here can be easily applied to other compounds and protein datasets.

Muscle fatigue analysis during dynamic contractions based on biomechanical features and Permutation Entropy.

Muscle fatigue is an important field of study in sports medicine and occupational health. Several studies in the literature have proposed methods for predicting muscle fatigue in isometric con-tractions using three states of muscular fatigue: Non-Fatigue, Transition-to-Fatigue, and Fatigue. For this, several features in time, spectral and time-frequency domains have been used, with good performance results; however, when they are applied to dynamic contractions the performance decreases. In this paper, we propose an approach for analyzing muscle fatigue during dynamic contractions based on time and spectral domain features, Permutation Entropy (PE) and biomechanical features. We established a protocol for fatiguing the deltoid muscle and acquiring surface electromiography (sEMG) and biomechanical signals. Subsequently, we segmented the sEMG and biomechanical signals of every contraction. In order to label the contraction, we computed some features from biomechanical signals and evaluated their correlation with fatigue progression, and the most correlated variables were used to label the contraction using hierarchical clustering with Ward's linkage. Finally, we analyzed the discriminant capacity of sEMG features using ANOVA and ROC analysis. Our results show that the biomechanical features obtained from angle and angular velocity are related to fatigue progression, the analysis of sEMG signals shows that PE could distinguish Non-Fatigue, Transition-to-Fatigue and Fatigue more effectively than classical sEMG features of muscle fatigue such as Median Frequency.

Dynamic changes of total acid and bacterial communities during the traditional fermentation of Hong Qu glutinous rice wine

Background: Hong Qu glutinous rice wine (HQGRW) is brewed under non-aseptic fermentation conditions, so it usually has a relatively high total acid content. The aim of this study was to investigate the dynamics of the bacterial communities and total acid during the fermentation of HQGRW and elucidate the correlation between total acid and bacterial communities. Results: The results showed that the period of rapid acid increase during fermentation occurred at the early stage of fermentation. There was a negative response between total acid increase and the rate of increase in alcohol during the early fermentation stage. Bacterial community analysis using high-throughput sequencing technology was found that the dominant bacterial communities changed during the traditional fermentation of HQGRW. Both principal component analysis (PCA) and hierarchical clustering analysis revealed that there was a great difference between the bacterial communities of Hong Qu starter and those identified during the fermentation process. Furthermore, the key bacteria likely to be associated with total acid were identified by Spearman's correlation analysis. Lactobacillus, unclassified Lactobacillaceae, and Pediococcus were found, which can make significant contributions to the total acid development (| r| N 0.6 with FDR adjusted P b 0.05), establishing that these bacteria can associate closely with the total acid of rice wine. Conclusions: This was the first study to investigate the correlation between bacterial communities and total acid during the fermentation of HQGRW. These findings may be helpful in the development of a set of fermentation techniques for controlling total acid.

Influence of physicochemical factors on environmental availability and distribution of semiochemicals that affect Varroa destructor and phylogenetically close organisms: classification by VHWOC PCA-clustering.

Varroa destructor parasites Apis mellifera larvae following the interception of the semiochemicals involved in bee communication; thus, the semiochemical availability and distribution pathways take place within different physicochemical environments. The structure of 172 molecules with semiochemical activity on Varroa destructor was used to compute the representative physicochemical descriptors of the thermodynamic partition among different physicochemical environments: vapor pressure (V), Henry's coefficient (H), water solubility constant (W), octanol-water partition coefficient (O) and organic carbon partition coefficient (C); VHWOC. The principal component analysis (PCA) and hierarchical clustering of VHWOC descriptors allowed us to establish the trend in availability and distribution of the semiochemicals resulting in a 4 classes model of physicochemical environments: Class 1, Soluble/Volatile; Class 2, Soluble; Class 3, Contact; Class 4, Adsorbed/Volatile. Our results suggest that semiochemicals can transit between different thermodynamic equilibrium phases depending on environment conditions. The classification prediction of the model was tested on 6 new molecules obtained from ketonic extracts of L5 Apis mellifera drone larvae; locating them in class 4, which was consistent with their molecular structure. This study can be the starting point for the design of synthetic semiochemicals or for the control of Varroa destructor. In addition, the method can be used in the analysis of other semiochemical groups.

Ecological Insights to Track Cytotoxic Compounds among Maytenus ilicifolia Living Individuals and Clones of an Ex Situ Collection.

Biodiversity is key for maintenance of life and source of richness. Nevertheless, concepts such as phenotype expression are also pivotal to understand how chemical diversity varies in a living organism. Sesquiterpene pyridine alkaloids (SPAs) and quinonemethide triterpenes (QMTs) accumulate in root bark of Celastraceae plants. However, despite their known bioactive traits, there is still a lack of evidence regarding their ecological functions. Our present contribution combines analytical tools to study clones and individuals of Maytenus ilicifolia (Celastraceae) kept alive in an ex situ collection and determine whether or not these two major biosynthetic pathways could be switched on simultaneously. The relative concentration of the QMTs maytenin (1) and pristimerin (2), and the SPA aquifoliunin E1 (3) were tracked in raw extracts by HPLC-DAD and ¹H-NMR. Hierarchical Clustering Analysis (HCA) was used to group individuals according their ability to accumulate these metabolites. Semi-quantitative analysis showed an extensive occurrence of QMT in most individuals, whereas SPA was only detected in minor abundance in five samples. Contrary to QMTs, SPAs did not accumulate extensively, contradicting the hypothesis of two different biosynthetic pathways operating simultaneously. Moreover, the production of QMT varied significantly among samples of the same ex situ collection, suggesting that the terpene contents in root bark extracts were not dependent on abiotic effects. HCA results showed that QMT occurrence was high regardless of the plant age. This data disproves the hypothesis that QMT biosynthesis was age-dependent. Furthermore, clustering analysis did not group clones nor same-age samples together, which might reinforce the hypothesis over gene regulation of the biosynthesis pathways. Indeed, plants from the ex situ collection produced bioactive compounds in a singular manner, which postulates that rhizosphere environment could offer ecological triggers for phenotypical plasticity.

Phenotypic Characterization and Genetic Diversity of Sugarcane Varieties Cultivated in Northern Karnataka of India based on Principal Component and Cluster Analyses

Abstract: Sugarcane is a major commercial crop grown in India and across the world. Hence, several elite varieties have been developed now-a-days to overcome many obstacles including abiotic stresses and diseases. The present study was undertaken to screen genetic variation among twenty four sugarcane varieties that are commonly cultivated across Northern Karnataka, India with reference to physicochemical characters. Experiment was conducted in triplicate following randomized complete block design (RCBD) at S. Nijalingappa Sugar Institute, Belagavi, Karnataka, India during February 2016-17. Physiological parameters such as internode length, stalk height, plant height, stalk girth, number of internodes, single cane weight, single cane volume of juice, cane yield and recovery were investigated. Further, statistical techniques such as principal component analysis and agglomerative hierarchical clustering were performed to characterize the twenty four varieties. Among twenty four sugarcane varieties studied, Co 86032 and CoC 671 were found to be elite varieties with respect to sugar recovery and cane yield, whereas varieties such as Co 86032 and Com 0265 were found to be best with respect to cane yield only. Based on the results obtained, eight varieties, viz., Co SNK 09232, Com 0265, Co 86032, Co SNK 09293, Co SNK 07680, CoC 671, Co 13006 and Co 2001-15 were found to be good with respect to overall qualities. Further studies need to be involved with molecular marker that would help in identification of elite varieties which could substantially contribute to construction of genetic resources library that may in turn find maximum use in molecular breeding.

Abundance and distribution of beach litter along the Atlantico Department, Caribbean coast of Colombia.

A total of 5993 litter items divided into 13 categories were found at 25 beaches located along the Atlantico Department coastline, Caribbean of Colombia, with an average litter abundance of 7 items/m. Agglomerative Hierarchical Clustering (AHC), Multidimensional Scaling (MDS) and Principal Components Analysis (PCA) were applied with the objective of highlighting similarities and contrasts between litter categories and abundances. Results indicated two specific groups of beaches in terms of amounts of litter. The first group is composed of 17 "dirty beaches" (urban, resort and village) while the second group includes 8 "clean beaches" (village and resort). This division was confirmed by means of the EA/NALG (2000) grading system, which highlighted that 68% of beaches belonging to the Atlantico Department coastline are in an unacceptable condition of cleanness. Current patterns of litter abundance and accumulation are related to sources as well as beach characteristics such as degree of exposition and morphodynamic state.

Biological predictors shared by dementia and bullous pemphigoid patients point out a cross-antigenicity between BP180/BP230 brain and skin isoforms.

Bullous pemphigoid (BP) following dementia diagnosis has been reported in the elderly. Skin and brain tissues express BP180 and BP230 isoforms. Dementia has been associated with rs6265 (Val66Met) polymorphism of the brain-derived neurotrophic factor (BDNF) gene and low serum BDNF. Here we investigated a possible cross-antigenicity between BP180/BP230 brain and skin isoforms. We assessed antibodies against BP180/BP230 and BDNF levels by ELISA and BDNF Val66Met SNP by PCR in three groups: 50 BP patients, 50 patients with dementia, and 50 elderly controls. Heatmap hierarchical clustering and data mining decision tree were used to analyze the patients' demographic and laboratorial data as predictors of dementia-BP association. Sixteen percent of BP patients with the lowest serological BDNF presented dementia-BP clinical association. Anti-BP180/230 positivity was unexpected observed among dementia patients (10%, 10%) and controls (14%, 1%). Indirect immunofluorescence using healthy human skin showed a BP pattern in two of 10 samples containing antibodies against BP180/BP230 obtained from dementia group but not in the control samples. Neither allelic nor genotypic BDNF Val66Met SNP was associated with dementia or with BP (associated or not with clinical manifestation of dementia). Heatmap analysis was able to differentiate the three studied groups and confirmed the ELISA results. The comprehensive data mining analysis revealed that BP patients and dementia patients shared biological predictors that justified the dementia-BP association. Autoantibodies against the BP180/BP230 brain isoforms produced by dementia patients could cross-react with the BP180/BP230 skin isoforms, which could justify cases of dementia preceding the BP disease.

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy.

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC "hot-spots" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data.

Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and non-linear registrations and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and to analyze stability and lateralization results.

Identification and quantification of sesquiterpenes and polyacetylenes in Atractylodes lancea from various geographical origins using GC-MS analysis

A convenient and sensitive GC-MS method was developed to identify thirteen sesquiterpenes and polyacetylenes (e.g. caryophyllene, γ-elemene, α-caryophyllene, β-selinene, isoledene, germacrene B, elixene, atractylone, hinesol, β-eudesmol, atrctylodin, atractylenolide II and acetylatractylodinol) in Atractylodes lancea (Thunb.) DC., Asteraceae. Among those compounds, four major components including atractylone, hinesol, β-eudesmol and atrctylodin were quantified with standards; contents of other components were estimated by using calibration curve of hinesol. In this study, we presented that the concentrations of those thirteen components varied drastically in A. lancea samples from different producing areas. Among those components, atractylenolide II and acetylatractylodinol were identified by GC-MS for the first time. A hierarchical clustering analysis based on relative peak areas of those thirteen components in total ion current (TIC) profiles was used to characterize A. lancea samples from different producing areas. Further clustering analysis showed that a simplified method with only four major bioactive components could be used to serve the same aim.

Recent advances in gene expression data clustering: a case study with comparative results

Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.