Master of Science

thesisRecent developments have shown that restricted Boltzmann machines (RBMs) are useful in learning the features of a given dataset in an unsupervised manner. In the case of digital images, RBMs consider the image pixels as a set of real-valued random variables, disregarding their spatial layout. However, as we know, each image pixel is correlated with its neighboring pixels, and direct modeling of this correlation might help in learning. Therefore, this thesis proposes using a Markov random field prior on the weights of the RBM model, which is designed to model these correlations between neighboring pixels. We compared the test classification error of our model with that of a traditional RBM with no prior on the weights and with RBMs with L1 and L2 regularization prior on the weights. We used the NIST dataset, which consists of images of handwritten digits for our experiments

Dissesiminated cysticercosis with neurocysticercosis – A rare presentation diagnosed by fine needle aspiration cytology

Disseminated cysticercosis is a rare presentation of human T. Solium infection in which the parasite disseminates via the blood stream throughout the human body. The various clinical manifestations depend upon the location of the parasitic cyst inside the body. Neurocysticercosis is the most common parasite disease of the central nervous system. Disseminated cysticercosis with neurocysticercosis is a very rare presentation of human cysticercus infection. Here we present such a rare case in which a young man presented with multiple swellings all over the body and a history of seizures. Fine needle aspiration cytology was done and the diagnosis was established

Deubiquitylating enzymes and disease

Deubiquitylating enzymes (DUBs) can hydrolyze a peptide, amide, ester or thiolester bond at the C-terminus of UBIQ (ubiquitin), including the post-translationally formed branched peptide bonds in mono- or multi-ubiquitylated conjugates. DUBs thus have the potential to regulate any UBIQ-mediated cellular process, the two best characterized being proteolysis and protein trafficking. Mammals contain some 80–90 DUBs in five different subfamilies, only a handful of which have been characterized with respect to the proteins that they interact with and deubiquitylate. Several other DUBs have been implicated in various disease processes in which they are changed by mutation, have altered expression levels, and/or form part of regulatory complexes. Specific examples of DUB involvement in various diseases are presented. While no specific drugs targeting DUBs have yet been described, sufficient functional and structural information has accumulated in some cases to allow their rapid development

Review Paper On: Accident Detection Using VANET

Vehicular ad hoc networks (VANETs) are collecting enlarge recognition from techno based and deployment based sections of industry, due to the various applications and probable immense welfare they offer for future VANET users. Safety information exchange enables life-critical application, such as the alerting functionality during crossing traversing and lane merging, and thus, plays a key role in VANET application. In a VANET, vehicles will depend on the integrity of received data for deciding when to send lerting triggers to drivers. The interaction between car to car, car to roadside unit is done using wireless communication interfaces. That is why sefty is an salient concern area for vehicular grid application. For authentication purpose so many bandwidths is consumed and the production becomes low. In VANET some serious networks attacks such as man in middle attack, impersonation is possible. In this paper we are going to through some light on the previous researches done in this area and will compare the various drawbacks of these researches. After that we are giving different issues on VANET and finally conclude with proposed algorithms. DOI: 10.17762/ijritcc2321-8169.15031

Impact of an International Nosocomial Infection Control Consortium multidimensional approach on central line-associated bloodstream infection rates in adult intensive care units in eight cities in India

SummaryObjectiveTo evaluate the impact of the International Nosocomial Infection Control Consortium (INICC) multidimensional infection control approach on central line-associated bloodstream infection (CLABSI) rates in eight cities of India.MethodsThis was a prospective, before-and-after cohort study of 35650 patients hospitalized in 16 adult intensive care units of 11 hospitals. During the baseline period, outcome surveillance of CLABSI was performed, applying the definitions of the CDC/NHSN (US Centers for Disease Control and Prevention/National Healthcare Safety Network). During the intervention, the INICC approach was implemented, which included a bundle of interventions, education, outcome surveillance, process surveillance, feedback on CLABSI rates and consequences, and performance feedback. Random effects Poisson regression was used for clustering of CLABSI rates across time periods.ResultsDuring the baseline period, 9472 central line (CL)-days and 61 CLABSIs were recorded; during the intervention period, 80898 CL-days and 404 CLABSIs were recorded. The baseline rate was 6.4 CLABSIs per 1000 CL-days, which was reduced to 3.9 CLABSIs per 1000 CL-days in the second year and maintained for 36 months of follow-up, accounting for a 53% CLABSI rate reduction (incidence rate ratio 0.47, 95% confidence interval 0.31–0.70; p=0.0001).ConclusionsImplementing the six components of the INICC approach simultaneously was associated with a significant reduction in the CLABSI rate in India, which remained stable during 36 months of follow-up

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.)

Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study

Software testing is undertaken to ensure that the software meets the expected requirements. The intention is to find bugs, errors, or defects in the developed software so that they can be fixed before deployment. Testing of the software is needed even after it is deployed. Regression testing is an inevitable part of software development, and must be accomplished in the maintenance phase of software development to ensure software reliability. The existing literature presents a large amount of relevant knowledge about the types of techniques and approaches used in regression test case selection and prioritization (TCS&P), comparisons of techniques used in TCS&P, and the data used. Numerous secondary studies (surveys or reviews) have been conducted in the area of TCS&P. This study aimed to provide a comprehensive examination of the analysis of the enhancements in TCS&P using a thorough systematic literature review (SLR) of the existing secondary studies. This SLR provides: (1) a collection of all the valuable secondary studies (and their qualitative analysis); (2) a thorough analysis of the publications and the trends of the secondary studies; (3) a classification of the various approaches used in the secondary studies; (4) insight into the specializations and range of years covered in the secondary texts; (5) a comprehensive list of statistical tests and tools used in the area; (6) insight into the quality of the secondary studies based on the seven selected Research Paper Quality parameters; (7) the common problems and challenges encountered by researchers; (8) common gaps and limitations of the studies; and (9) the probable prospects for research in the field of TCS&P