A unique approach for protein secondary structure comparison under TOPS representation.

To unravel the intricate connection between protein function and protein structure, it is imperative to comprehensively evaluate protein secondary structure similarity from various perspectives. While numerous techniques have been suggested for comparing protein secondary structure elements (SSE), there continues to be a substantial need for finding alternative ways of comparing the same. In this paper, Topology of Protein Structure (TOPS) representations of protein secondary structures are considered to offer a new alignment-free method for evaluating similarities/dissimilarities of protein secondary structures. Initially, a two-dimensional numerical representation of the SSE is created, associating each point with a mass reflecting its frequency of occurrence. Then the means of coordinate values are determined by averaging weighted sums, and these mean values are subsequently used to calculate moments-of-inertia. Next, a four-component descriptor is generated out of the eigenvalues of the matrix and the mean values of the represented coordinates. Thereafter, Manhattan distance measure is used to obtain the distance matrix. This is finally applied to obtain the phylogenetic trees under the use of NJ method. SSE considered in the proposed method comprises 36-elements from the Chew-Kedem database giving five different taxa: globin, alpha-beta, tim-barrel, beta, and alpha. Phylogenetic trees were created for these SSE through the application of various methods: Clustal-Omega, LZ-Complexity, SED, TOPS + and TOC, to facilitate comparative analysis. Phylogenetic tree of the proposed method outperformed results of the previous methods when applied to the same SSE. Therefore, the method effectively constructs phylogenetic tree for analyzing protein secondary structure comparison.Communicated by Ramaswamy H. Sarma.

Positional difference and Frequency (PdF) based alignment-free technique for genome sequence comparison.

In the field of computational biology, genome sequence comparison among different species is essential and has applications in both the research and scientific fields. Owing to the lengthy processing time and large number of data sets, the alignment-based approaches are unsuitable and ineffective. Therefore, alignment-free techniques have obtained popularity for acquiring proper sequence clustering and evolutionary relationship among species. In this paper, a complete bipartite graph based Positional difference and Frequency (PdF) vector descriptor is introduced. Positional difference and Frequency, two parameters, are applied to the genome sequence to create a 16- dimensional vector descriptor using the di-nucleotide representation of genome sequence. Subsequently, a distance matrix is calculated to construct the phylogenetic trees for different data sets of mammals and viruses. The achieved outcomes are compared with the phylogenetic trees of the earlier methods viz. the FFP method, the ClustalW method, the MEV method, the PCNV method and the FIS method. In most instances, the proposed method produces more precise outcomes than the preceding techniques and has potential for genome sequence comparison on both the equal and unequal length of data-sets.Communicated by Ramaswamy H. Sarma.

Biochemical Property Based Positional Matrix: A New Approach Towards Genome Sequence Comparison.

The growth of the genome sequence has become one of the emerging areas in the study of bioinformatics. It has led to an excessive demand for researchers to develop advanced methodologies for evolutionary relationships among species. The alignment-free methods have been proved to be more efficient and appropriate related to time and space than existing alignment-based methods for sequence analysis. In this study, a new alignment-free genome sequence comparison technique is proposed based on the biochemical properties of nucleotides. Each genome sequence can be distributed in four parameters to represent a 21-dimensional numerical descriptor using the Positional Matrix. To substantiate the proposed method, phylogenetic trees are constructed on the viral and mammalian datasets by applying the UPGMA/NJ clustering method. Further, the results of this method are compared with the results of the Feature Frequency Profiles method, the Positional Correlation Natural Vector method, the Graph-theoretic method, the Multiple Encoding Vector method, and the Fuzzy Integral Similarity method. In most cases, it is found that the present method produces more accurate results than the prior methods. Also, in the present method, the execution time for computation is comparatively small.

Socio-demographic, behavioural and clinical factors influencing control of diabetes and hypertension in urban Mysore, South India: a mixed-method study conducted in 2018.

BACKGROUND: Inadequate control of diabetes and hypertension is a major concern in India because of rising mortality and morbidity. Few studies in India have explored factors that influence control of diabetes and hypertension. The current study aimed to improve the understanding of multifactorial influence on the control of diabetes and hypertension among patients in Primary Health Care Settings(PHC) of urban Karnataka. METHODS: We used a mixed-method study design, within a project aiming to improve non-communicable disease (NCD) continuum of care across PHC in Mysore city, India, conducted in 2018. The quantitative study was conducted among 399 patients with diabetes and/or hypertension and a logistic regression model was used to assess the factors responsible for biological control levels of diabetes and hypertension measured through Glycated Haemoglobin(HbA1c) and blood pressure. Further, in-depth interviews(IDI) were conducted among these patients and the counsellors at PHCs to understand the barriers and enablers for better control. RESULT: The quantitative assessment found odds of poor control amongst diabetics' increased with older age, longer duration of disease, additional chronic conditions, and tobacco consumption. For hypertensives, odds of poor control increased with higher body mass index(BMI), alcohol consumption, and belongingness to lower social groups. These findings were elaborated through qualitative assessment which found that the control status was affected by stress as a result of family or financial worries. Stress, poor lifestyle, and poor health-seeking behaviour interplay with other factors like diet and exercise leading to poor control of diabetes and hypertension. CONCLUSION: A better understanding of determinants associated with disease control can assist in designing focused patient outreach plans, customized communication strategies, need-based care delivery plans, and specific competency-based capacity-building models for health care workers. Patient-centric care focusing on biological, social and behavioural determinants is pivotal for appropriate management of NCDs at community level in low-middle income countries.

Chronic respiratory disease mortality and its associated factors in selected Asian countries: evidence from panel error correction model.

BACKGROUND: Chronic Respiratory Diseases (CRDs) in Asian countries are a growing concern in terms of morbidity and mortality. However, a systematic understanding of the increasing age-adjusted mortality rate of chronic respiratory disease (CRD) and its associated factors is not readily available for many Asian countries. We aimed to determine country-level factors affecting CRD mortality using a panel error correction model. METHODS: Based on data from the Global Burden of Disease Study 2017, we estimated the trends and distribution of CRD mortality for selected Asian countries from 2010 to 2017. Furthermore, we evaluated the relationship between CRD mortality and Gross Domestic Product (GDP) per capita, average years of schooling, urbanization, and pollutant emission (PM2.5 concentration) using a fixed-effect model. We corrected the estimates for heteroscedasticity and autocorrelation through Prais-Winsten adjustment along with robust standard error. RESULTS: Between 2010 and 2017, approximately 21.4 million people died from chronic respiratory diseases in the countries studied. Age-standardized crude mortality rate from CRDs in the period had minimum and maximum values of 8.19 (Singapore in 2016) and 155.42 (North Korea in 2010) per 100,000 population, respectively. The coefficients corrected for autocorrelation and heteroskedasticity based on the final model of our study (Prais-Winsten), showed that all explanatory variables were statistically significant (p < 0.001). The model shows that the 1% increase in GDP per capita results in a 20% increase (0.203) in the CRD mortality rate and that a higher concentration of air pollution is also positively associated with the CRD deaths (0.00869). However, an extra year of schooling reduces the mortality rate by 4.79% (- 0.0479). Further, rate of urbanization is negatively associated with the CRD death rate (- 0.0252). CONCLUSIONS: Our results indicate that both socioeconomic and environmental factors impact CRD mortality rates. Mortality due to CRD increases with rising GDP per capita and decreases with the percentage of the total population residing in urban areas. Further, mortality increases with greater exposure to PM2.5. Also, higher years of schooling mitigate rising CRD mortality rates, showing that education can act as a safety net against CRD mortality. These results are an outcome of sequential adjustments in the final model specification to correct for heteroscedasticity and autocorrelation.