EXPLORATION OF PARAMETER SPACE OF TWO ACTIVE CONTOUR ALGORITHMS IN THE SEGMENTATION OF MINI-PIG BURN WOUNDS

Traditional methods of burn wound analysis rely on subjective classification of shape and variable estimation of size using the rule-of-nines. For partial thickness burns, where the standard method leads to differing prognoses, it was hypothesized that the use of active contour algorithms could aid in automatic segmentation and quantitative analysis. Previous work was conducted, comparing burn wounds taken with a DSLR to a Microsoft Kinect V2 and quantifying an algorithm’s ability to distinguish the region of a burn wound on a pig. Image analysis utilizing the Edge and Chan-Vese Active Contour algorithms found the Dice-coefficient, a measure of how well a computer-generated trace aligned with a predetermined human trace, to be artificially high. It was suspected that the inflation was due to a favorable initial mask being provided to the analysis software. It was hypothesized that a difference would be noted in segmentation behavior of the two algorithms and that providing a less favorable initial trace would not yield high Dice coefficients. In order to examine the effects of starting masks on segmentation outputs and measure the generalized performance of these algorithms, in this study, we incrementally modified parameters such as the contraction bias, iteration, and starting mask, to find a parameter space that would yield repeatable, high accuracy predictions of burn wound regions. Results showed a significant difference in the performance of the algorithms and choice of starting masks at thresholds for Dice’s coefficients above 0.8 and that choice of starting mask shape and size bias segmentation. By developing an automated system to determine the best parameter set not subject to artificially high Dice’s coefficients, computers can aid physicians in accurate diagnosis and treatment of partial thickness burn wounds.Bachelor of Scienc

Dynamic Control System Based On Context for Mobile Devices

“To render the accurate information, at correct place in real period with custom-made setup and locality sensitiveness” is the inspiration for every location based information scheme. Android applications in mobile devices may often have access to susceptible data and resources on user device. “Location Based Services” can only provide services that give a data and information to person, wherever he might be through various android applications. To avoid the data misuse by malicious applications, an application may get privilege on the specific user location and thus a Context Based Access Control Mechanism (CBACM) is needed so that privileges can be established and revoked vigorously. A very interesting application include shadowing where immediate information is required to choose if the people being monitored are valid intimidation or an flawed object. The execution of CBACM differentiates between the narrowly located sub-areas within the distinct area. Android operating system is modified such that context based access restriction can be precise and imposed. DOI: 10.17762/ijritcc2321-8169.15057

Restoration of Neonatal Retinal Images

Retinopathy of prematurity (ROP) is an eye disorder primarily affecting premature neonates. Specialists use a number of neonatal retinal images acquired by a wide field of view camera for diagnosis and the subsequent follow up. However, the premature infants’ retinal images are generally of lower visibility compared to adult retinal images, affecting the quality of diagnosis. We study some image dehazing methods from general outdoor scenes and propose an image restoration scheme for neonatal retinal images, based on the physical model of light propagation in a medium. The results from our restoration algorithm is useful for analysis by human experts as well as computer aided diagnosis and specifically we show that our method enhances vessel segmentation significantly compared to traditional methods like adaptive histogram equalization

Social distance and face mask detector system exploiting transfer learning

As time advances, the use of deep learning-based object detection algorithms has also evolved leading to developments of new human-computer interactions, facilitating an exploration of various domains. Considering the automated process of detection, systems suitable for detecting violations are developed. One such applications is the social distancing and face mask detectors to control air-borne diseases. The objective of this research is to deploy transfer learning on object detection models for spotting violations in face masks and physical distance rules in real-time. The common drawbacks of existing models are low accuracy and inability to detect in real-time. The MobileNetV2 object detection model and YOLOv3 model with Euclidean distance measure have been used for detection of face mask and physical distancing. A proactive transfer learning approach is used to perform the functionality of face mask classification on the patterns obtained from the social distance detector model. On implementing the application on various surveillance footage, it was observed that the system could classify masked and unmasked faces and if social distancing was maintained or not with accuracies 99% and 94% respectively. The models exhibited high accuracy on testing and the system can be infused with the existing internet protocol (IP) cameras or surveillance systems for real-time surveillance of face masks and physical distancing rules effectively

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.</p> <p>Results</p> <p>The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.</p> <p>Conclusions</p> <p>The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at <url>http://biomine.ece.ualberta.ca/MODAS/</url>.</p

Kartläggning av promotor- och förstärkarinteraktion i trippelnegativa bröstcancercellinjer

Trippelnegativ bröstcancer (TNBC) är den mest maligna formen av bröstcancer utan någon framträdande behandlingsbar biomarkör. Således har den djupa återfallsfrekvensen och bristen på behandlingsalternativ öppnat behovet av att förstå TNBC: s etiopatogenes och molekylära mekanism. Huvudsyftet är att kartlägga det genomomfattande differentiella uttrycket av den förmodade cancergenen (en prenyleringsgen) och dess betydelse vid trippelnegativ bröstcancer. Hi-Cap (Capture Hi-C) är en teknik som genererar högupplösta promotor-förstärkare interak- tioner med nästan en-förstärkare upplösning.Vi arbetade med cancercellinjer, MDA-MB_231, med och utan den förmodade genen. Hi-C-tekniken optimerades i enlighet därmed för cancer- cellinjen för att generera en högupplöst berikad region. Resultaten kan vidare användas för att utföra biblioteksförberedelser och bioinformatikanalys. Dessa fynd kommer att ägnas åt att upptäcka nya vägar involverade i prenylering och TNBC.Triple-negative Breast cancer (TNBC) is the most malignant form of breast cancer with no prominent treatable biomarker. The profound recurrence rate and lack of treatment options have opened the need to understand the etiopathogenesis and molecular mechanism of TNBC. The main objective of this study is to map the genome-wide differential expression of the putative cancer gene (a prenylation gene) and its importance in Triple-Negative Breast cancer. Hi-Cap (Capture Hi-C) is a technique which generates high-resolution promoter-enhancer interactions with almost single-enhancer resolution. We worked with cancer cell lines, MDA-MB_231, with and without the putative gene. The Hi-C technique was optimized accordingly for the cancer cell line to generate a high-resolution enriched region. The results can be further used to perform library prep and bioinformatics analysis. These findings will devote to discovering novel pathways involved with prenylation and TNBC

Kartläggning av promotor- och förstärkarinteraktion i trippelnegativa bröstcancercellinjer

Trippelnegativ bröstcancer (TNBC) är den mest maligna formen av bröstcancer utan någon framträdande behandlingsbar biomarkör. Således har den djupa återfallsfrekvensen och bristen på behandlingsalternativ öppnat behovet av att förstå TNBC: s etiopatogenes och molekylära mekanism. Huvudsyftet är att kartlägga det genomomfattande differentiella uttrycket av den förmodade cancergenen (en prenyleringsgen) och dess betydelse vid trippelnegativ bröstcancer. Hi-Cap (Capture Hi-C) är en teknik som genererar högupplösta promotor-förstärkare interak- tioner med nästan en-förstärkare upplösning.Vi arbetade med cancercellinjer, MDA-MB_231, med och utan den förmodade genen. Hi-C-tekniken optimerades i enlighet därmed för cancer- cellinjen för att generera en högupplöst berikad region. Resultaten kan vidare användas för att utföra biblioteksförberedelser och bioinformatikanalys. Dessa fynd kommer att ägnas åt att upptäcka nya vägar involverade i prenylering och TNBC.Triple-negative Breast cancer (TNBC) is the most malignant form of breast cancer with no prominent treatable biomarker. The profound recurrence rate and lack of treatment options have opened the need to understand the etiopathogenesis and molecular mechanism of TNBC. The main objective of this study is to map the genome-wide differential expression of the putative cancer gene (a prenylation gene) and its importance in Triple-Negative Breast cancer. Hi-Cap (Capture Hi-C) is a technique which generates high-resolution promoter-enhancer interactions with almost single-enhancer resolution. We worked with cancer cell lines, MDA-MB_231, with and without the putative gene. The Hi-C technique was optimized accordingly for the cancer cell line to generate a high-resolution enriched region. The results can be further used to perform library prep and bioinformatics analysis. These findings will devote to discovering novel pathways involved with prenylation and TNBC

Leaping frogs (Anura: Ranixalidae) of the Western Ghats of India: An integrated taxonomic review

Leaping frogs of the family Ranixalidae are endemic to the Western Ghats of India and are currently placed in a single genus, Indirana.  Based on specimens collected from their entire range and a comprehensive study of type material defining all known species, we propose a revised taxonomy for the leaping frogs using an integrative approach including an analysis of the mitochondrial 16S rRNA and nuclear rhodopsin genes, as well as multivariate morphometrics. Both genetic and morphological analyses suggest that the genus Indirana is paraphyletic and a distinct monophyletic group, Walkerana gen. nov is described herein.  The new genus is separated from Indirana sensu stricto by an apomorphic character state of reduced webbing, with one phalange free on the first and second toe (vs. no free phalanges), two phalanges free on the third and fifth toe (vs. one free phalange), and three phalanges free on the fourth toe (vs. 2–2½ phalanges free).  This review includes (i) identification of lectotypes and redescription of three species of the genus Walkerana; (ii) identification of lectotypes for Indirana beddomii and I. semipalmata and their redescription; (iii) redescription of I. brachytarsus and I. gundia; and (iv) descriptions of four new species, namely, I. duboisi and I. tysoni from north of the Palghat gap, and I. yadera and I. sarojamma from south of the Palghat gap; and (iv) a key to the genera and species in the family Ranixalidae. </div