HDL subspecies : association with low HDL-C, obesity, and metabolic syndrome

AIMS An independent, powerful coronary heart disease (CHD) predictor is a low level of high-density lipoprotein cholesterol (HDL-C). Discoidal preβ-HDL particles and large HDL2 particles are the primary cholesterol acceptors in reverse cholesterol transport, a key anti-atherogenic HDL mechanism. The quality of HDL subspecies may provide better markers of HDL functionality than does HDL-C alone. We aimed I) to study whether alterations in the HDL subspecies profile exist in low-HDL-C subjects II) to explore the relationship of any changes in HDL subspecies profile in relation to atherosclerosis and metabolic syndrome; III) to elucidate the impact of genetics and acquired obesity on HDL subspecies distribution. SUBJECTS The study consisted of 3 cohorts: A) Finnish families with low HDL-C and premature CHD (Study I: 67 subjects with familial low HDL-C and 64 controls; Study II: 83 subjects with familial low HDL-C, 65 family members with normal HDL-C, and 133 controls); B) a cohort of 113 low- and 133 high-HDL-C subjects from the Health 2000 Health Examination Survey carried out in Finland (Study III); and C) a Finnish cohort of healthy young adult twins (52 monozygotic and 89 dizygotic pairs) (Study IV). RESULTS AND CONCLUSIONS The subjects with familial low HDL-C had a lower preβ-HDL concentration than did controls, and the low-HDL-C subjects displayed a dramatic reduction (50-70%) in the proportion of large HDL2b particles. The subjects with familial low HDL-C had increased carotid atherosclerosis measured as intima-media-thickness (IMT), and HDL2b particles correlated negatively with IMT. The reduction in both key cholesterol acceptors, preβ-HDL and HDL2 particles, supports the concept of impaired reverse cholesterol transport contributing to the higher CHD risk in low-HDL-C subjects. The family members with normal HDL-C and the young adult twins with acquired obesity showed a reduction in large HDL2 particles and an increase in small HDL3 particles, which may be the first changes leading to the lowering of HDL-C. The low-HDL-C subjects had a higher serum apolipoprotein E (apoE) concentration, which correlated positively with the metabolic syndrome components (waist circumference, TG, and glucose), highlighting the need for a better understanding of apoE metabolism in human atherosclerosis. In the twin study, the increase in small HDL3b particles was associated with obesity independent of genetic effects. The heritability estimate, of 73% for HDL-C and 46 to 63% for HDL subspecies, however, demonstrated a strong genetic influence. These results suggest that the relationship between obesity and lipoproteins depends on different elements in each subject. Finally, instead of merely elevating HDL-C, large HDL2 particles and discoidal preβ-HDL particles may provide beneficial targets for HDL-targeted therapy.

Immediate hypersensitivity to Parthenium hysterophorus. I. Association of HLA antigens and Parthenium rhinitis

Lymphocytes collected from rhinitis subjects with strong positive skin reactions to the pollen allergens of Parthenium hysterophorus (American feverfew) having moderate to high titres of Parthenium-specific serum IgE were analysed for association of HLA-antigens covering 13 specificities of HLA-A, 17 specificities of HLA-B and eight specificities of HLA-DR loci by the NIH two-stage microlymphocytotoxicity assay. Comparison of the phenotypic frequencies of HLA-A and B antigens between Parthenium rhinitis subjects (n= 22) and control subjects (n= 137) did not suggest any significant association when tested for these antigen specificities. A significant correlation in the association of HLA-DR3 antigen with a relative risk of 11·33, however, was observed in Parthenium rhinitis subjects (n= 30) when compared to controls (n= 50).

Human Ecology in India Some Historical Perspectives

Before the spread of extensive settled cultivation, the Indian subcontinent would have been inhabited by territorial hunter–gatherers and shifting cultivators with cultural traditions of prudent resource use. The disruption of closed material cycles by export of agricultural produce to centres of non-agricultural population would have weakened these traditions. Indeed, the fire-based sacrificial ritual and extensive agricultural settlements might have catalysed the destruction of forests and wildlife and the suppression of tribal peoples during the agricultural colonization of the Gangetic plains. Buddhism, Jainism and later the Hindu sects may have been responses to the need for a reassertion of ecological prudence once the more fertile lands were brought under cultivation. British rule radically changed the focus of the country's resource use pattern from production of a variety of biological resources for local consumption to the production of a few commodities largely for export. The resulting ecological squeeze was accompanied by disastrous famines and epidemics between the 1860s and the 1920s. The counterflows to tracts of intensive agriculture have reduced such disasters since independence. However, these are quite inadequate to balance the state-subsidized outflows of resources from rural hinterlands. These imbalances have triggered serious environmental degradation and tremendous overcrowding of the niche of agricultural labour and marginal cultivator all over the country.

Uncoupling Protein 2 and 3 Gene Polymorphisms and Their Association with Type 2 Diabetes in Asian Indians

Background: This study examined the association of -866G/A, Ala55Val, 45bpI/D, and -55C/T polymorphisms at the uncoupling protein (UCP) 3-2 loci with type 2 diabetes in Asian Indians. Methods: A case-control study was performed among 1,406 unrelated subjects (487 with type 2 diabetes and 919 normal glucose-tolerant NGT]), chosen from the Chennai Urban Rural Epidemiology Study, an ongoing population-based study in Southern India. The polymorphisms were genotyped using polymerase chain reaction-restriction fragment length polymorphism and direct sequencing. Haplotype frequencies were estimated using an expectation-maximization algorithm. Linkage disequilibrium was estimated from the estimates of haplotypic frequencies. Results: The genotype (P = 0.00006) and the allele (P = 0.00007) frequencies of Ala55Val of the UCP2 gene showed a significant protective effect against the development of type 2 diabetes. The odds ratios (adjusted for age, sex, and body mass index) for diabetes for individuals carrying Ala/Val was 0.72, and that for individuals carrying Val/Val was 0.37. Homeostasis insulin resistance model assessment and 2-h plasma glucose were significantly lower among Val-allele carriers compared to the Ala/Ala genotype within the NGT group. The genotype (P = 0.02) and the allele (P = 0.002) frequencies of -55C/T of the UCP3 gene showed a significant protective effect against the development of diabetes. The odds ratio for diabetes for individuals carrying CT was 0.79, and that for individuals carrying TT was 0.61. The haplotype analyses further confirmed the association of Ala55Val with diabetes, where the haplotypes carrying the Ala allele were significantly higher in the cases compared to controls. Conclusions: Ala55Val and -55C/T polymorphisms at the UCP3-2 loci are associated with a significantly reduced risk of developing type 2 diabetes in Asian Indians.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

Performance of the generalized delta rule in structural damage detection

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

Role of hydrophobic effect and surface charge in surfactant-DNA association

Ethidium bromide is one of the best known DNA intercalator. Upon intercalation inside DNA, the fluorescence due to ethidium bromide gets enhanced by many orders of magnitude. In this paper, we employed ethidium bromide as a probe for studying surfactant-DNA complexation using fluorescence spectroscopy and agarose gel electrophoresis. Surfactants of different charge types and chain lengths were used and the results were compared with that of the related small organic cations or salts under comparable conditions. The cationic surfactants induced destabilization of the ethidium bromide-DNA complex at concentrations in orders of magnitude lower than that of the small organic cations or salts. In contrast however, the anionic surfactants failed to promote any such destabilization of probe-DNA complex. DNA loses its ethidium bromide stainability in the presence of high concentration of cationic surfactant aggregates as revealed from agarose gel electrophoresis experiments. Inclusion of surfactants and other additives into the DNA generally enhanced the DNA double-strand to single strand transition melting temperatures by a few degrees, in a concentration-dependent manner and at high surfactant concentration melting profiles got broadened.

Effect of molecular association and reactivity on substitution in chromium(III)-salprn complexes

A new chromium(III)-Schiff base complex, [Cr(5-chlorosalprn)(H2O)(2)]ClO4, where salprn=N,N'-propylenebis(salicylideneimine) has been prepared and characterized by electrospray ionization mass spectrometric (ESIMS) analysis and other spectroscopic techniques. Single crystal X-ray data reveal that the complex assumes a trans-diaquo structure, [Cr(C17H18Cl2N2O4)]ClO4.H2O. The effect of phenyl ring substituents on the rate of formation of [O=Cr-V Schiff base](+) has been investigated. The bimolecular rate constant for the formation of O=Cr-V species by the [Cr(Schiff base)(H2O)(2)]ClO4, where the Schiff base=salprn, (1) and 5-chlorosalprn, (2) with PhOI was compared. In the case of (2) the rate was found to be faster by an order of magnitude at pH=4 compared to (1). The introduction of a chloro-substituent on the phenyl ring not only influences the rate of redox reactivity but also the pKa values of aquo ligands of the complexes, indicating the difference in the electronic environment around the metal ion in both (1) and (2).

Tree structure for efficient data mining using rough sets

In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.

Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

Rule-based modelling of iron homeostasis in tuberculosis

To establish itself within the host system, Mycobacterium tuberculosis (Mtb) has formulated various means of attacking the host system. One such crucial strategy is the exploitation of the iron resources of the host system. Obtaining and maintaining the required concentration of iron becomes a matter of contest between the host and the pathogen, both trying to achieve this through complex molecular networks. The extent of complexity makes it important to obtain a systems perspective of the interplay between the host and the pathogen with respect to iron homeostasis. We have reconstructed a systems model comprising 92 components and 85 protein-protein or protein-metabolite interactions, which have been captured as a set of 194 rules. Apart from the interactions, these rules also account for protein synthesis and decay, RBC circulation and bacterial production and death rates. We have used a rule-based modelling approach, Kappa, to simulate the system separately under infection and non-infection conditions. Various perturbations including knock-outs and dual perturbation were also carried out to monitor the behavioral change of important proteins and metabolites. From this, key components as well as the required controlling factors in the model that are critical for maintaining iron homeostasis were identified. The model is able to re-establish the importance of iron-dependent regulator (ideR) in Mtb and transferrin (Tf) in the host. Perturbations, where iron storage is increased, appear to enhance nutritional immunity and the analysis indicates how they can be harmful for the host. Instead, decreasing the rate of iron uptake by Tf may prove to be helpful. Simulation and perturbation studies help in identifying Tf as a possible drug target. Regulating the mycobactin (myB) concentration was also identified as a possible strategy to control bacterial growth. The simulations thus provide significant insight into iron homeostasis and also for identifying possible drug targets for tuberculosis.

Spatial Data Mining and Modeling for visualisation of Rapid Urbanisation

Rapid urbanisation in India has posed serious challenges to the decision makers in regional planning involving plethora of issues including provision of basic amenities (like electricity, water, sanitation, transport, etc.). Urban planning entails an understanding of landscape and urban dynamics with causal factors. Identifying, delineating and mapping landscapes on temporal scale provide an opportunity to monitor the changes, which is important for natural resource management and sustainable planning activities. Multi-source, multi-sensor, multi-temporal, multi-frequency or multi-polarization remote sensing data with efficient classification algorithms and pattern recognition techniques aid in capturing these dynamics. This paper analyses the landscape dynamics of Greater Bangalore by: (i) characterisation of direct impervious surface, (ii) computation of forest fragmentation indices and (iii) modeling to quantify and categorise urban changes. Linear unmixing is used for solving the mixed pixel problem of coarse resolution super spectral MODIS data for impervious surface characterisation. Fragmentation indices were used to classify forests – interior, perforated, edge, transitional, patch and undetermined. Based on this, urban growth model was developed to determine the type of urban growth – Infill, Expansion and Outlying growth. This helped in visualising urban growth poles and consequence of earlier policy decisions that can help in evolving strategies for effective land use policies.