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Background: Non-communicable diseases (NCDs) contributed to 71% of the global mortality and affect the ageing population with variable proportions. NCD diagnosis gives an opportunity for patients to change their health behaviors. Secondary prevention strategies like diet modification, physical activity and smoking cessation after NCD diagnosis are important and should be emphasized particularly for middle-aged and older adults. Objectives: 1. To assess the level of health behavioral change adopted by the patients diagnosed with NCDs registered under Rural field practice area. 2. To evaluate the factors influencing the health behavioral change in NCD patients. Methodology: This is a Cross-sectional study design conducted at Rural Health & Training Centre, Government Medical College, Ananthapuramu, from August 2023 to October 2023. Simple random sampling was used to select 150 registered NCD cases from the RHTC data-base. We included participants who were diagnosed with either diabetes, cardiovascular disease, chronic lung disease, or cancer after recruitment. Data were collected by using pre-designed semi-structured questionnaire. Data was analyzed using IBM SPSS version 25.0. Associations were tested using chi-squares and for all comparisons p-value

A 25-year-old woman presented with a history of episodes of fever with night sweats, multiple joint pains and breathlessness for 5 months. Eight months before the present episode, she had developed a generalised rash and transient swellings in the groin on both sides. Physical examination revealed pallor, pedal oedema and a hyperpigmented rash over the chest, back and thighs. Pulse was 126 beats per minute and respirations 38/min; while breathing ambient air, the oxygen saturation by pulse oximetry was 83%. On auscultation of the chest, breath sounds were diminished bilaterally and crepitations were heard in both the lung bases. Neurological examination revealed weakness of proximal muscles of both upper and lower limbs on both sides with a power of grade 3/5. Laboratory investigations revealed elevated total leucocyte count with neutrophilic leucocytosis; band forms and metamyelocytes were seen in the peripheral blood smear. Oxygen inhalation and broad-spectrum intravenous antibiotics were initiated. Echocardiogram showed severe tricuspid regurgitation with moderate pulmonary arterial hypertension. Computed tomography (CT) of the chest revealed patchy consolidation in both the lungs, bilateral pleural effusions and pericardial effusion. Bone marrow biopsy showed normocellular normoblastic marrow with erythroid hyperplasia. 18F-fluorodeoxyglucose positron emission tomography-CT showed metabolically active lymphadenopathy both above and below the diaphragm and bilateral patchy lung infiltrates. Biopsy of the inguinal lymph node along with immunohistochemistry staining confirmed the diagnosis as T-cell lymphoma. [ABSTRACT FROM AUTHOR]

Highly populated cities around the world are facing problems of better traffic management. Technologically advanced cities deploy agent based multiple multimedia sensor based networks to collect and analyse traffic data to provide better solutions for management and prediction of road conditions. However, the collected data reaches a high volume and became very difficult to manage. Moreover, the predictive analysis also demands a high computational power to run the predictive analysis algorithms. The data collections methods include automatic and manual collections of large amount of data and then the analysis is often done in legacy system or manually. Cloud providers are deploying large-scale data centres across the globe to meet Cloud customers' compute, storage and network resource demands, and better efficiency, scalability and performance of the hosted applications. The outcomes of this research also considers the most effective cloud based storage for traffic data with the knowledge of most popular cloud based storage service providers. The accumulation of the data is also followed by a predictive system for road traffic data analysis. Hence, in this work we also explore the use of standard machine learning techniques to identify the most suitable technique with performance consideration. [ABSTRACT FROM AUTHOR]

In this paper, we studied the performances of models built using various SVM implementations during the multiclass classification task of automated evaluation of descriptive answers. The performances were evaluated on five datasets each with 900 samples and with each of the datasets treated using symmetric uncertainty feature selection filter. We quantitatively analyzed the best SVM implementation technique from amongst the 17 different SVM implementation combinations derived by using various SVM classifier libraries, SVM types and Kernel methods. Accuracy, F Score, Kappa and Area under ROC curve are used as model evaluation metrics in order to evaluate the models and rank them according to their performances. Based on the results, we derived the conclusion that SMO classifier when used with Polynomial kernel is the overall best performing classifier applicable for auto evaluation of descriptive answers. [ABSTRACT FROM AUTHOR]

In this paper, we study the performance of various models for automated evaluation of descriptive answers by using rank based feature selection filters for dimensionality reduction. We quantitatively analyze the best from amongst five rank based feature selection techniques, namely Chi Squared filter, Information Gain filter, Gain Ratio filter, Relief filter and Symmetrical Uncertainty filter. We use Sequential Minimal Optimization with Polynomial kernel to build models and we evaluate these models in terms of various parameters such as Accuracy, Time to build the models, Kappa, Mean Absolute Error and Root Mean Squared Error. For all except the Relief filter, the accuracies obtained are at least 4% better than the accuracies obtained with the same models without any filters applied. We found that the accuracies recorded are same for Chi Squared filter, Information Gain filter, Gain Ratio filter and Symmetrical Uncertainty filter. Therefore accuracy alone is not the sole determinant in selecting the best filter. The time taken to build the models, Kappa, Mean Absolute Error and Root Mean Squared Error play a major role in determining the effectiveness of these filters. The overall rank aggregation metric of Symmetrical Uncertainty filter is 45 and this is better by 1 rank than the rank aggregation metric of Information gain filter. Symmetric Uncertainty filter's rank aggregation metric is better by 3, 6, 112 ranks respectively when compared to the rank aggregation metrics of Chi Squared filter, Gain Ratio filter and Relief filters. Through these quantitative measurements, we conclude that Symmetrical Uncertainty attribute evaluation is the overall best performing rank based feature selection algorithm applicable for auto evaluation of descriptive answers. [ABSTRACT FROM AUTHOR]

Automation of descriptive answers evaluation is the need of the hour because of the huge increase in the number of students enrolling each year in educational institutions and the limited staff available to spare their time for evaluations. In this paper, we use a machine learning workbench called LightSIDE to accomplish auto evaluation and scoring of descriptive answers. We attempted to identify the best supervised machine learning algorithm given a limited training set sample size scenario. We evaluated performances of Bayes, SVM, Logistic Regression, Random forests, Decision stump and Decision trees algorithms. We confirmed SVM as best performing algorithm based on quantitative measurements across accuracy, kappa, training speed and prediction accuracy with supplied test set. [ABSTRACT FROM AUTHOR]

The E-Health is a business scenario in which the integration problem is greater than before by the intensive use of knowledge, by the need of accurately handling citizens' privacy and by live or death inferences. E-Health has been seeking for semantic interoperability for more then a decade, but securely sharing health care data among healthcare associations remains an open challenge. Numerous standardization activities (For e.g., openEHR, HL7 CDA, CEN ENV 13606, DICOM, EHRcom, IHE) are addressing current problem but none of them has achieve the attractive level of flexibility. The paper is being addressed about security policies of sharing of health care data respecting vendors' autonomy and citizens' privacy. It is aiming at developing a highly scalable, semantically improved communication infrastructure. Such infrastructure is the result of the integration of web services technologies. Semantic web provide machine procedural semantics in order to allow mechanized integration of services. [ABSTRACT FROM AUTHOR]

Prokaryotic protein–protein interactions are underrepresented in currently available databases. Here, we describe a ‘gold standard’ dataset (MPI-LIT) focusing on microbial binary protein–protein interactions and associated experimental evidence that we have manually curated from 813 abstracts and full texts that were selected from an initial set of 36 852 abstracts. The MPI-LIT dataset comprises 1237 experimental descriptions that describe a non-redundant set of 746 interactions of which 659 (88%) are not reported in public databases. To estimate the curation quality, we compared our dataset with a union of microbial interaction data from IntAct, DIP, BIND and MINT. Among common abstracts, we achieve a sensitivity of up to 66% for interactions and 75% for experimental methods. Compared with these other datasets, MPI-LIT has the lowest fraction of interaction experiments per abstract (0.9) and the highest coverage of strains (92) and scientific articles (813). We compared methods that evaluate functional interactions among proteins (such as genomic context or co-expression) which are implemented in the STRING database. Most of these methods discriminate well between functionally relevant protein interactions (MPI-LIT) and high-throughput data. Availability: http://www.jcvi.org/mpidb/interaction.php?dbsource=MPI-LIT. Contact: raja@jcvi.org Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Introduction: The success or failure of the root canal treatment depends on proper bio-mechanical preparation and obturation. However, a missed canal might be the foremost reason for failure of a tooth that seems to be properly treated. The canal that is often missed without much attention is second mesio-buccal canal of maxillary molars. Purpose: To investigate the incidence of MB2 canals in Maxillary first molars among Tirupathi population. Material and Methods: A survey was carried out in Tirupathi and data was obtained from the dental practitioners and from our dental college for the past one year. Results: Out of 6945 maxillary first molars a total of 2534 MB2 canals were located and filled. A high statistically significant difference (p<0.0001) in the occurrence of MB2 canals in males compared to females and left maxillary first molars comparative to the right molars was observed. Conclusion: The percentage of MB2 canals in maxillary first molar derived in the present study was 36.49%. [ABSTRACT FROM AUTHOR]