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PubMed1996

Analysis, clustering and prediction of the conformation of short and medium size loops connecting regular secondary structures.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using knowledge-based procedures. While the core of a protein can be straight forwardly modelled from the structurally conserved regions of homologues of known structure, loops must be modelled from a selected homologue or from a loop chosen from outside the family. Here we present a loop prediction procedure that attempts to identify the conformational class of the loop rather than to select a specific loop from a database of fragments. The structures of some 2083 loops of one to eight residues in length were extracted from a database of 225 protein and protein domain structures. For each loop, the relative disposition of its bounding secondary structures is described by the separation between the tips of their axes, the angle and dihedral angle between their axes. From the clustering of the loops according to the root mean square deviation of their spatial fit, a total of 162 loop conformational classes, including 79% of loops, were identified. One-hundred and eight of these, involving 66% of the loops, were populated by at least four non-homologous loops or four loops sharing a low sequence identity. Another 54 classes, including 13% of the loops, were populated by at least three loops of low sequence similarity from three or fewer non-homologous groups. Most of the previously described loop conformations were found among the populated classes. For each class a template was constructed containing both sequence preferences and the relative disposition of bounding secondary structures among member loops. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible.

Amino Acid Sequence↗
PubMed2006

Tic symptom profiles in subjects with Tourette Syndrome from two genetically isolated populations.

BACKGROUND: Tourette Syndrome (TS) has a complex etiology and wide variability in phenotypic expression. Identifying underlying symptom patterns may be useful for etiological and outcome studies of TS. METHODS: Lifetime tic and related symptom data were collected between 1996 and 2001 in 121 TS subjects from the Central Valley of Costa Rica and 133 TS subjects from the Ashkenazi Jewish (AS) population in the US. Subjects were grouped by tic symptoms using an agglomerative hierarchical cluster analysis. Cluster membership was tested for association with available ancillary information (age of onset, tic severity, comorbid disorders, medication treatment and family history). RESULTS: Cluster analysis identified two distinct groups in each sample, those with predominantly simple tics (cluster 1), and those with multiple complex tics (cluster 2). Membership in cluster 2 was correlated with increased tic severity, global impairment, medication treatment, and presence of comorbid obsessive-compulsive symptoms in both samples, and with family history of tics, lower verbal IQ, earlier age of onset, and comorbid obsessive-compulsive disorder and attention-deficit/hyperactivity disorder in the AS sample. CONCLUSIONS: This study provides evidence for consistent and reproducible symptom profiles in two independent TS study samples. These findings have implications for etiological studies of TS.

Adolescent↗
PubMed2005

Dietary patterns of young, low-income US children.

OBJECTIVE: The purpose of this study was to identify dietary patterns of young, low-income US children, describe differences in diet quality between identified patterns, and make targeted food recommendations to improve diet quality. DESIGN: Dietary patterns were assessed using dietary variables from the Pyramid Servings Database within the Continuing Survey of Food Intakes by Individuals 1994-1996, 1998. Healthy Eating Index (HEI) scores were used to validate identified dietary patterns. SUBJECTS/SETTING: Two subsamples of low-income children, aged 2 to 3 years (n = 1,242) and 4 to 8 years (n = 1,506), were selected from the Continuing Survey of Food Intakes by Individuals data. STATISTICAL ANALYSIS: Cluster analysis was performed to determine unique dietary patterns within the two subsamples. Linear regression analyses were used to compare energy intake, discretionary fat, added sugars, and HEI scores across cluster groups. Descriptive statistics were computed for each cluster. RESULTS: Cluster analysis identified six and seven distinct dietary patterns for the younger and older children, respectively. Four patterns were similar for both age groups. For the 2- to 3-year-old children, energy intake, overall HEI scores, and nine of the 10 HEI component scores differed among the four most prevalent dietary patterns. Among the older children, energy intake and six of the HEI component scores differed between the four most prevalent clusters but not overall HEI. CONCLUSIONS: Neither age group had a cluster of children who followed a balanced/moderate diet pattern consistent with Food Guide Pyramid recommendations. Children consuming almost every pattern identified could benefit by reducing added sugars and discretionary fat and increasing low-fat, low-sugar options from the vegetables, fruits, meat, and milk groups.

Child↗
PubMed2000

Data evaluation for soft drink quality control using principal component analysis and back-propagation neural networks.

This work describes an alternative for chemical data research, with the aim of evaluating finished product quality. Analytical data for additives in soft drinks are interpreted by the use of multivariate data analysis: principal component analysis (PCA), factor analysis, cluster analysis, and artificial neural networks. Taking into account various chemical components like sorbic, benzoic, and ascorbic acids; saccharose; caffeine; Na, K, Ca, Mg, Fe, Zn, Cu, P, and B, soft drinks were characterized and classified. The ratios of Na, K, Ca + Mg, P, and K/Na have been studied. The application of PCA, cluster analysis, and artificial neural networks showed that combination of these chemometric tools offers effective means for modeling and classifying soft drinks in accordance with their contents in additives and heavy metals.

Beverages↗
PubMed2026

Clustering patterns of behavioral and metabolic risk factors for noncommunicable diseases in Iran: findings from a national STEPS survey.

BACKGROUND: Noncommunicable diseases (NCDs) are the leading cause of mortality in Iran, driven by behavioral and metabolic risk factors that frequently co-occur. OBJECTIVE: To identify patterns of co-occurring behavioral and metabolic NCD risk factors among Iranian adults and characterize their demographic and socioeconomic correlates. METHODS: This cross-sectional study analyzed data from 16,618 adults aged ≥25 years who participated in Iran's 2021 nationally representative STEPS survey. Thirteen behavioral and metabolic variables, including physical activity, nutrition score, smoking frequency, alcohol intake, salt intake, body mass index, blood pressure, fasting plasma glucose, and lipid markers, were entered into a K-means clustering analysis. Clusters were characterized by their risk profiles and demographic/socioeconomic attributes. Multinomial logistic regression examined associations between cluster membership and sociodemographic factors. RESULTS: Five distinct behavioral-metabolic clusters emerged. The smokers-drinkers (SD) cluster (3.1%) comprised mostly older, less-educated men with high smoking and alcohol use. The healthy-low-risk (HLR) cluster (40.3%) showed favorable profiles and included younger, more educated individuals. The physically active (PA) cluster (6.6%) was characterized mainly by younger men with markedly high physical activity levels. The dyslipidemic (DLP) cluster (26.0%) exhibited high dyslipidemia and overweight prevalence, while the hypertensive-diabetic (HTD) cluster (24.0%) had the highest obesity, hypertension, and diabetes rates, common among older urban adults. CONCLUSION: Behavioral and metabolic NCD risk factors in Iran formed five distinct co-occurrence patterns. Nearly half of adults belonged to metabolically high-risk clusters, highlighting the need for targeted prevention strategies that combine lifestyle interventions with screening and management of obesity, hypertension, diabetes, and dyslipidemia.

Humans↗
PubMed2002

Characterization of selected Spanish table wine samples according to their biogenic amine content from liquid chromatographic determination.

Pattern recognition techniques, such as principal component analysis, cluster analysis, and linear discriminant analysis, have been applied to samples of red, white, and rosé wines to determine whether some biogenic amines could be considered as chemical descriptors. Eight amines (tryptamine, phenylethylamine, putrescine, cadaverine, histamine, tyramine, spermidine, and spermine) were determined by RP-HPLC, after derivatization with dabsyl chloride. However, only putrescine, cadaverine, histamine, tyramine, spermidine, and spermine were found in the wines analyzed. From the association between variables obtained by principal component analysis and clustering and from the relationship found by linear discriminant analysis, it can be deduced that the amines generated during malolactic fermentation (putrescine, histamine, and tyramine) could be used as chemical descriptors to characterize table wine samples.

Analysis of Variance↗
PubMed1990

[Analysis of structural relationship between drug consumption and its influencing factors using multivariate statistical methods].

Based upon various multivariate statistical methods consumption data of antihypertensive drugs and their causal or functional relationship with different influencing factors were investigated, exploratively and exemplarily. Several analytical examinations (principal components analysis, factor analysis, cluster analysis) were performed aiming at the reflection of these factors in a classified and quantified form.

Antihypertensive Agents↗
PubMed2004

Toxicological classification of urine samples using pattern recognition techniques and capillary electrophoresis.

In toxicology, hazardous substances detected in organisms may often lead to different pathological conditions depending on the type of exposure and level of dosage; hence, further analysis on this can suggest the best cure. Urine profiling may serve the purpose because samples typically contain hundreds of compounds representing an effective metabolic fingerprint. This paper proposes a pattern recognition procedure for determining the type of cadmium dosage, acute or chronic, administrated to laboratory rats, where urinary profiles are detected using capillary electrophoresis. The procedure is based on the composition of a sample data matrix consisting of areas of common peaks, with appropriate pre-processing aimed at reducing the lack of reproducibility and enhancing the potential contribution of low-level metabolites in discrimination. The matrix is then used for pattern recognition including principal components analysis, cluster analysis, discriminant analysis and support vector machines. Attention is particularly focussed on the last of these techniques, because of its novelty and some attractive features such as its suitability to work with datasets that are small and/or have low samples/variable ratios. The type of cadmium administration is detected as a relevant feature that contributes to the structure of the sample matrix, and samples are classified according to the class membership, with discriminant analysis and support vector machines performing complementarily on a training and on a test set.

Animals↗
PubMed2005

Fourier transform infrared microscopy and imaging: detection of fungi in wood.

FTIR microscopy was used to detect and discriminate the two wood decaying fungi Trametes versicolor and Schizophyllum commune in experimentally infected beech wood blocks. The distribution of fungal mycelium in wood was locally resolved and semiquantitatively recorded using FTIR microscopy combined with a focal plane array detector and image analysis. Cluster analysis revealed major differences between FTIR spectra recorded from wood fibers and empty vessel lumina and spectra from mycelium of both fungal species, irrespective of whether the fungi were grown on the surface of wood or inside vessel lumina. Species-specific clustering of spectra of fungal mycelium grown on the wood surface and inside vessel lumina demonstrated the potential of FTIR microscopy to discriminate among fungal species decaying wood.

Cluster Analysis↗
PubMed1992

An investigation of tumor 1H nuclear magnetic resonance spectra by the application of chemometric techniques.

1H nuclear magnetic resonance (NMR) spectra of tumors and normal tissue include signals from all hydrogen-containing metabolites and can therefore be considered multicomponent multivariate mixtures. We have obtained 1H spectra from perchloric acid extracts of three normal tissues (liver, kidney, and spleen) and five rat tumors (GH3 prolactinoma, Morris hepatomas 7777 and 9618a, LBDS1 fibrosarcoma, and Walker 256 carcinosarcoma). We have applied several different chemometric methods to analyze the data. First, we used principal component analysis, cluster analysis, and an optimized artificial neural network to develop a classification rule from a training set of samples of known origin or class. The classification rule was then assessed using a set of unknown samples. We were able to successfully determine the class of each unknown sample. Second, we used the chemometric techniques of factor analysis followed by target testing to investigate the underlying biochemical differences that are detected between the classes of samples.

Adenocarcinoma↗