Search results for: segmentation genes

Authors: C. Miranda, R. Soares, S. Cunha, L. Ferreira, G. Igrejas, P. Poeta

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Different animal species, including ornamental animals, are reported as potential reservoirs of antibiotic resistance genes. Consequently, these resistances can be disseminated in the environment and transferred to humans. Moreover, multidrug-resistant bacteria reduce the efficacy of antibiotics, as the case of vancomycin-resistant enterococci. Enterococcus faecalis and E. faecium are described as the main nosocomial pathogens. In this line, the aim of this study was to characterize resistance and virulence genes of enterococci species isolated from samples of food supplied to ornamental animals during 2020. The 29 enterococci isolates (10 E. faecalis and 19 E. faecium) were tested for the presence of the resistance genes for the following antibiotics: erythromicyn (ermA, ermB and ermC), tetracycline (tetL, tetM, tetK and tetO), quinupristin/dalfopristin (vatD and vatE), gentamicin (aac(6’)-aph(2’’)-Ia), chloramphenicol (catA), streptomycin (ant(6)-Ia) and vancomycin (vanA and vanB). The same isolates were also tested for 10 virulence factors genes (esp, ace, gelE, agg, fsr, cpd, cylA, cylB, cylM and cylLL). The resistance and virulence genes were performed by PCR, using specific primers and conditions. Negative and positive controls were used in all PCR assays. The most prevalent resistance genes detected in both enterococci species were ermB (n=15, 52%), ermC (n=7, 24%), tetK (n=8, 28%) and vatE (n=4, 14%). Resistance genes for vancomycin were found in ten (34%) E. faecalis and ten (34%) E. faecium isolates. Only E. faecium isolates showed the presence of ermA (n=2, 7%), tetL (n=13, 45%) and ant(6)-Ia gene (n=4, 14%). A total of nine (31%) enterococci isolates were classified as multidrug-resistant bacteria (3 E. faecalis and 6 E. faecium). In three E. faecalis and one E. faecium were not detected resistance genes. The virulence genes detected in both species were agg (n=6, 21%) and cylLL (n=11, 38%). In general, each isolate showed only one of these virulence genes. Five E. faecalis and eleven E. faecium isolates were negative for all analyzed virulence genes. These preliminary results showed the presence of multidrug-resistant enterococci in food supplied to ornamental animals, in particular vancomycin-resistant enterococci. This genotypic characterization reinforces the relevance to public health in the control of antibiotic-resistant bacteria.

Keywords: antibiotic resistance, enterococci, feed, ornamental animals

Procedia PDF Downloads 167

Authors: Vincent Murray

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The chromatin structure at the transcription start sites (TSSs) of genes is very important in the control of gene expression. In order for gene expression to occur, the chromatin structure at the TSS has to be altered so that the transcriptional machinery can be assembled and RNA transcripts can be produced. In particular, the nucleosome structure and positioning around the TSS has to be changed. Bleomycin is utilized as an anti-tumor agent to treat Hodgkin's lymphoma, squamous cell carcinoma, and testicular cancer. Bleomycin produces DNA damage in human cells and DNA strand breaks, especially double-strand breaks, are thought to be responsible for the cancer chemotherapeutic activity of bleomycin. Bleomycin is a large glycopeptide with molecular weight of approximately 1500 Daltons and hence its DNA strand cleavage activity can be utilized as a probe of chromatin structure. In this project, Illumina next-generation DNA sequencing technology was used to determine the position of DNA double-strand breaks at the TSSs of genes in intact cells. In this genome-wide study, it was found that bleomycin cleavage preferentially occurred at the TSSs of actively transcribed human genes in comparison with non-transcribed genes. There was a correlation between the level of enhanced bleomycin cleavage at TSSs and the degree of transcriptional activity. In addition, bleomycin was able to determine the position of nucleosomes at the TSSs of human genes. Bleomycin analogues were also utilized as probes of chromatin structure at the TSSs of human genes. In a similar manner to bleomycin, the bleomycin analogues 6′-deoxy-BLM Z and zorbamycin preferentially cleaved at the TSSs of human genes. Interestingly this degree of enhanced TSS cleavage inversely correlated with the cytotoxicity (IC50 values) of BLM analogues. This indicated that the degree of cleavage by bleomycin analogues at the TSSs of human genes was very important in the cytotoxicity of bleomycin and analogues. It also provided a deeper insight into the mechanism of action of this cancer chemotherapeutic agent since actively transcribed genes were preferentially targeted.

Keywords: anti-cancer activity, chromatin structure, cytotoxicity, gene expression, next-generation DNA sequencing

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Authors: Samina Jam Nazeer Ahmad, Samia Yasin, Ijaz Ahmad, Muhammad Tahir, Jam Nazeer Ahmad

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Phytoplasmas are phloem-inhibited plant pathogenic bacteria that are transferred by insect vectors. Among biotic factors, Phytoplasma infection induces abnormality influencing the physiology as well as morphology of plants. In 16Sr-IX group phytoplasma-infected brassica compestris, flower abnormalities have been associated with changes in the expression of floral development genes. To determine whether methylation was involved in down-regulation of flower development, the process of DNA methylation and Demethylation was investigated as a possible mechanism for regulation of floral gene expression in phytoplasma infected Brassica transmitted by Orosious orientalis vector by using RT-PCR, MSRE-PCR, Southern blotting, Bisulfite Sequencing, etc. Transcriptional expression of methylated genes was found to be globally down-regulated in plants infected with phytoplasma, but not severely in those infested by insect vectors and variation in expression was found in genes involved in methylation. These results also showed that genes particularly orthologous to Arabidopsis APETALA3 involved in petal formation and flower development was down-regulated severely in phytoplasma-infected brassica and with the fact that phytoplasma and insect induce variation in developmental gene expression. The DNA methylation status of flower developmental gene in phytoplasma infected plants with 5-azacytidine restored gene expression strongly suggesting that DNA methylation was involved in down-regulation of floral development genes in phytoplasma infected brassica.

Keywords: genes expression, phytoplasma, DNA methylation, flower development

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Authors: Okafor Joan, Nwodo Uchechukwu

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Klebsiella pneumoniae (K. pneumoniae) is a significant pathogen responsible for opportunistic and nosocomial infection. One of the most significant antibiotic resistance mechanisms in K. pneumoniae isolates is efflux pumps. Our current study identified efflux genes (AcrAB, OqxAB, MacAB, and TolC) and regulatory genes (RamR and RarA) in multidrug-resistant (MDR) K. pneumoniae isolated from hospital effluents and investigated their relationship with antibiotic resistance. The sum of 145 K. pneumoniae isolates was established by PCR and screened for antibiotic susceptibility. PCR detected efflux pump genes, and their link with antibiotic resistance was statistically examined. However, 120 (83%) of the confirmed isolated were multidrug-resistant, with the largest percentage of resistance to ampicillin (88.3%) and the weakest rate of resistance to imipenem (5.5%). Resistance to the other antibiotics ranged from 11% to 76.6%. Molecular distribution tests show that AcrA, AcrB, MacA, oqxB oqxA, TolC, MacB were detected in 96.7%, 85%, 76.7%, 70.8%, 55.8%, 39.1%, and 29.1% respectively. However, 14.3% of the isolates harboured all seven genes screened. Efflux pump system AcrAB (83.2%) was the most commonly detected in K. pneumonia isolated across all the antibiotics class-tested. In addition, the frequencies of RamR and RarA were 46.2% and 31.4%, respectively. AcrAB and OqxAB efflux pump genes were significantly associated with fluoroquinolone, beta-lactam, carbapenem, and tetracycline resistance (p<0.05). The high rate of efflux genes in this study demonstrated that this resistance mechanism is the dominant way in K. pneumoniae isolates. Appropriate treatment must be used to reduce and tackle the burden of resistant Klebsiella pneumonia in hospital wastewater effluents.

Keywords: Klebsiella pneumonia, efflux pumps, regulatory genes, multidrug-resistant, hospital, PCR

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Authors: Aicha Majda, Abdelhamid El Hassani

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Lung CT image segmentation is a prerequisite in lung CT image analysis. Most of the conventional methods need a post-processing to deal with the abnormal lung CT scans such as lung nodules or other lesions. The simplest similarity measure in the standard Graph Cuts Algorithm consists of directly comparing the pixel values of the two neighboring regions, which is not accurate because this kind of metrics is extremely sensitive to minor transformations such as noise or other artifacts problems. In this work, we propose an improved version of the standard graph cuts algorithm based on the Patch-Based similarity metric. The boundary penalty term in the graph cut algorithm is defined Based on Patch-Based similarity measurement instead of the simple intensity measurement in the standard method. The weights between each pixel and its neighboring pixels are Based on the obtained new term. The graph is then created using theses weights between its nodes. Finally, the segmentation is completed with the minimum cut/Max-Flow algorithm. Experimental results show that the proposed method is very accurate and efficient, and can directly provide explicit lung regions without any post-processing operations compared to the standard method.

Keywords: graph cuts, lung CT scan, lung parenchyma segmentation, patch-based similarity metric

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Authors: Nuseiba M. Altarawneh, Suhuai Luo, Brian Regan, Guijin Tang

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Liver segmentation from medical images poses more challenges than analogous segmentations of other organs. This contribution introduces a liver segmentation method from a series of computer tomography images. Overall, we present a novel method for segmenting liver by coupling density matching with shape priors. Density matching signifies a tracking method which operates via maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Density matching controls the direction of the evolution process and slows down the evolving contour in regions with weak edges. The shape prior improves the robustness of density matching and discourages the evolving contour from exceeding liver’s boundaries at regions with weak boundaries. The model is implemented using a modified distance regularized level set (DRLS) model. The experimental results show that the method achieves a satisfactory result. By comparing with the original DRLS model, it is evident that the proposed model herein is more effective in addressing the over segmentation problem. Finally, we gauge our performance of our model against matrices comprising of accuracy, sensitivity and specificity.

Keywords: Bhattacharyya distance, distance regularized level set (DRLS) model, liver segmentation, level set method

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Authors: Fathi Kallel, Saida Khachira, Mohamed Ben Slima, Ahmed Ben Hamida

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Speckle noise is a main characteristic of cardiac ultrasound images, it corresponding to grainy appearance that degrades the image quality. For this reason, the ultrasound images are difficult to use automatically in clinical use, then treatments are required for this type of images. Then a filtering procedure of these images is necessary to eliminate the speckle noise and to improve the quality of ultrasound images which will be then segmented to extract the necessary forms that exist. In this paper, we present the importance of the pre-treatment step for segmentation. This work is applied to cardiac ultrasound images. In a first step, a comparative study of speckle filtering method will be presented and then we use a segmentation algorithm to locate and extract cardiac structures.

Keywords: medical image processing, ultrasound images, Speckle noise, image enhancement, speckle filtering, segmentation, snakes

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: Dalia G. Aseel

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The potato (Solanum tubersum, L.) has become one of the major vegetable crops in Egypt and all over the world. Potato leafroll virus(PLRV) was observed on potato plants collected from different governorates in Egypt. Three cultivars, Spunta, Diamont, and Cara, infected with PLRV were collected; RNA was extracted and subjected to Real-Time PCR using the coat protein gene primers. The results showed that the expression of the coat protein was 39.6-fold, 12.45-fold, and 47.43-fold, respectively, for Spunta, Diamont, and Cara cultivars. Differential Display Polymerase Chain Reaction (DD-PCR) using pathogenesis-related protein 1 (PR-1), β-1,3-glucanases (PR-2), chitinase (PR-3), peroxidase (POD), and polyphenol oxidase (PPO) forward primers for pathogenesis-related proteins (PR). The obtained data revealed different banding patterns depending on the viral type and the region of infection. Regarding PLRV, 58 up-regulated and 19 down-regulated genes were detected. Sequence analysis of the up-and down-regulated genes revealed that infected plants were observed in comparison with the healthy control. Sequence analysis of the up-regulated gene was performed, and the encoding sequence analysis showed that the obtained genes include: induced stolen tip protein. On the other hand, two down-regulated genes were identified: disease resistance RPP-like protein and non-specific lipid-transfer protein. In this study, the expressions of PR-1, PR-2, PR-3, POD, and PPO genes in the infected leaves of three potato cultivars were estimated by quantitative real-time PCR. We can conclude that the PLRV-infection of potato plants inhibited the expression of the five PR genes. On the contrary, infected leaves by PLRV elevated the expression of some defense genes. This interaction may also induce and/or suppress the expression of some genes responsible for the plant's defense mechanisms.

Keywords: PLRV, pathogenesis-related proteins (PRs), DD-PCR, sequence, real-time PCR

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Authors: Kyi Pyar Zaw, Zin Mar Kyu

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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.

Keywords: chain code frequency, character recognition, feature extraction, features matching, segmentation

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: Mawloud Mosbah, Bachir Boucheham

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Color Histogram is considered as the oldest method used by CBIR systems for indexing images. In turn, the global histograms do not include the spatial information; this is why the other techniques coming later have attempted to encounter this limitation by involving the segmentation task as a preprocessing step. The weak segmentation is employed by the local histograms while other methods as CCV (Color Coherent Vector) are based on strong segmentation. The indexation based on local histograms consists of splitting the image into N overlapping blocks or sub-regions, and then the histogram of each block is computed. The dissimilarity between two images is reduced, as consequence, to compute the distance between the N local histograms of the both images resulting then in N*N values; generally, the lowest value is taken into account to rank images, that means that the lowest value is that which helps to designate which sub-region utilized to index images of the collection being asked. In this paper, we make under light the local histogram indexation method in the hope to compare the results obtained against those given by the global histogram. We address also another noteworthy issue when Relying on local histograms namely which value, among N*N values, to trust on when comparing images, in other words, which sub-region among the N*N sub-regions on which we base to index images. Based on the results achieved here, it seems that relying on the local histograms, which needs to pose an extra overhead on the system by involving another preprocessing step naming segmentation, does not necessary mean that it produces better results. In addition to that, we have proposed here some ideas to select the local histogram on which we rely on to encode the image rather than relying on the local histogram having lowest distance with the query histograms.

Keywords: CBIR, color global histogram, color local histogram, weak segmentation, Euclidean distance

Procedia PDF Downloads 335

Authors: Siddharth Soni, Gourav Kumar Pandey, Sneha Kumari, Dev Mani Pandey, Koel Mukherjee

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The silkworm Bombyx mori is a commercially important insect, but a major roadblock in silk production are silkworm diseases. Flacherie is one of the diseases of the silkworm, that affects the midgut of the 4th and 5th instar larvae and eventually makes them lethargic, stop feeding and finally result in their death. The concerned disease is a result of bacterial and viral infection and in some instances a combination of both. The present study aims to identify and study the expression level of genes in the flacherie condition. For the said work, total RNA was isolated from the infected larvae at their most probable infectious instar and cDNA was synthesized using Reverse Transcriptase PCR (RT-PCR). This cDNA was then used to amplify disease relalted genes whose expression levels were checked using quantitaive PCR (qPCR) using the double delta Ct method. Cry toxin receptors like APN and BtR-175, ROS mediator Dual Oxidase are few proteins whose genes were overexpressed. Interestingly, pattern recognition receptors (PRRs) C-type lectins' genes were found to be downregulated. The results explain about the strong expression of genes that can distinguish the concerned protein in the midgut of diseased silkworm and thereby aiding knowledge in the field of inhibitor designing research.

Keywords: Bombyx mori, flacherie disease, inhibitor designing, up and down regulation

Procedia PDF Downloads 253

Authors: Somyung Oh, Jeonghyeon Ha, Kyungwon Lee, Sejong Oh

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Microarray is a general scheme to find differentially expressed genes for target concept. The output is expressed by heat map, and biologists analyze related terms of gene ontology to find some characteristics of differentially expressed genes. In this paper, we propose integrated visualization tool for heat map and gene ontology graph. Previous two methods are used by static manner and separated way. Proposed visualization tool integrates them and users can interactively manage it. Users may easily find and confirm related terms of gene ontology for given differentially expressed genes. Proposed tool also visualize connections between genes on heat map and gene ontology graph. We expect biologists to find new meaningful topics by proposed tool.

Keywords: heat map, gene ontology, microarray, differentially expressed gene

Procedia PDF Downloads 280

Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Rik van Leeuwen, Ger Koole

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Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Abbas Al-Refaie, Nour Bata

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A distributor of Apple products' experiences numerous difficulties in developing marketing strategies for new and existing mobile product entries that maximize customer satisfaction and the firm's profitability. This research, therefore, integrates market segmentation in platform-based product family design and conjoint analysis to identify iSystem combinations that increase customer satisfaction and business profits. First, the enhanced market segmentation grid is created. Then, the estimated demand model is formulated. Finally, the profit models are constructed then used to determine the ideal product family design that maximizes profit. Conjoint analysis is used to explore customer preferences with their satisfaction levels. A total of 200 surveys are collected about customer preferences. Then, simulation is used to determine the importance values for each attribute. Finally, sensitivity analysis is conducted to determine the product family design that maximizes both objectives. In conclusion, the results of this research shall provide great support to Apple distributors in determining the best marketing strategies that enhance their market share.

Keywords: market segmentation, conjoint analysis, market strategies, optimization

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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The most aggressive form of brain tumor is called glioma. Glioma is kind of tumor that arises from glial tissue of the brain and occurs quite often. A fully automatic 2D-CNN model for brain tumor segmentation is presented in this paper. We performed pre-processing steps to remove noise and intensity variances using N4ITK and standard intensity correction, respectively. We used Keras open-source library with Theano as backend for fast implementation of CNN model. In addition, we used BRATS 2015 MRI dataset to evaluate our proposed model. Furthermore, we have used SimpleITK open-source library in our proposed model to analyze images. Moreover, we have extracted random 2D patches for proposed 2D-CNN model for efficient brain segmentation. Extracting 2D patched instead of 3D due to less dimensional information present in 2D which helps us in reducing computational time. Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.77 for complete, 0.76 for core, 0.77 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, LGG

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Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: Hediyeh Talebi, Shokoofeh Ghiam, Changiz Eslahchi

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Traditionally, Alzheimer’s disease research has focused on genes with significant fold changes, potentially neglecting subtle but biologically important alterations. Our study introduces an integrative approach that highlights genes crucial to underlying biological processes, regardless of their fold change magnitude. Alzheimer's Single-cell RNA-seq data related to the peripheral blood mononuclear cells (PBMC) was extracted from the Gene Expression Omnibus (GEO). After quality control, normalization, scaling, batch effect correction, and clustering, differentially expressed genes (DEGs) were identified with adjusted p-values less than 0.05. These DEGs were categorized based on cell-type, resulting in four datasets, each corresponding to a distinct cell type. To distinguish between cells from healthy individuals and those with Alzheimer's, an adversarial autoencoder with a classifier was employed. This allowed for the separation of healthy and diseased samples. To identify the most influential genes in this classification, the weight matrices in the network, which includes the encoder and classifier components, were multiplied, and focused on the top 20 genes. The analysis revealed that while some of these genes exhibit a high fold change, others do not. These genes, which may be overlooked by previous methods due to their low fold change, were shown to be significant in our study. The findings highlight the critical role of genes with subtle alterations in diagnosing Alzheimer's disease, a facet frequently overlooked by conventional methods. These genes demonstrate remarkable discriminatory power, underscoring the need to integrate biological relevance with statistical measures in gene prioritization. This integrative approach enhances our understanding of the molecular mechanisms in Alzheimer’s disease and provides a promising direction for identifying potential therapeutic targets.

Keywords: alzheimer's disease, single-cell RNA-seq, neural networks, blood biomarkers

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Authors: Ankita Shukla, Tiratha Raj Singh

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Colorectal cancer (CRC) imposes serious mortality burden worldwide and it has been increasing for past consecutive years. Continuous efforts have been made so far to diagnose the disease condition and to identify the root cause for it. In this study, we performed the pathway level as well as the differential gene expression studies for CRC. We analyzed the gene expression profile GSE24514 from Gene Expression Omnibus (GEO) along with the gene pathways involved in the CRC. This analysis helps us to understand the behavior of the genes that have shown differential expression through their targeted pathways. Pathway analysis for the targeted genes covers the wider area which therefore decreases the possibility to miss the significant ones. This will prove to be beneficial to expose the ones that have not been given attention so far. Through this analysis, we attempt to understand the various neighboring genes that have close relationship to the targeted one and thus proved to be significantly controlling the CRC. It is anticipated that the identified hub and neighboring genes will provide new directions to look at the pathway level differently and will be crucial for the regulatory processes of the disease.

Keywords: mismatch repair, microsatellite instability, carcinogenesis, morbidity

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Authors: Srijoni Banerjee

Abstract:

Sequences of some of the candidate genes (e.g., CPE, CDKAL1, GCKR, HSD11B1, IGF2BP2, IRS1, LPIN1, PKLR, TNF, PPARG) implicated in some of the complex disease, e.g. Type II diabetes in man has been compared with other species to investigate phylogenetic affinity. Based on mRNA sequence of these genes of 7 to 8 species, using bioinformatics tools Mega 5, Bioedit, Clustal W, distance matrix was obtained. Phylogenetic trees were obtained by NJ and UPGMA clustering methods. The results of the phylogenetic analyses show that of the species compared: Xenopus l., Danio r., Macaca m., Homo sapiens s., Rattus n., Mus m. and Gallus g., Bos taurus, both NJ and UPGMA clustering show close affinity between clustering of Homo sapiens s. (Man) with Rattus n. (Rat), Mus m. species for the candidate genes, except in case of Lipin1 gene. The results support the functional similarity of these genes in physiological and biochemical process involving man and mouse/rat. Therefore, in understanding the complex etiology and treatment of the complex disease mouse/rate model is the best laboratory choice for experimentation.

Keywords: phylogeny, candidate gene of type-2 diabetes, CPE, CDKAL1, GCKR, HSD11B1, IGF2BP2, IRS1, LPIN1, PKLR, TNF, PPARG

Procedia PDF Downloads 287

Authors: Ahad Salimi, Hassan Masoumi

Abstract:

Prostate cancer is one of the most common recognized cancers in men, and, is one of the most important mortality factors of cancer in this group. Determining of prostate’s boundary in TRUS (Transrectal Ultra Sound) images is very necessary for prostate cancer treatments. The weakness edges and speckle noise make the ultrasound images inherently to segment. In this paper a new automatic algorithm for prostate segmentation in TRUS images proposed that include three main stages. At first morphological smoothing and sticks filtering are used for noise removing. In second step, for finding a point in prostate region, SOFM algorithm is enlisted and in the last step, the boundary of prostate extracting accompanying active contour is employed. For validation of proposed method, a number of experiments are conducted. The results obtained by our algorithm show the promise of the proposed algorithm.

Keywords: SOFM, preprocessing, GVF contour, segmentation

Procedia PDF Downloads 300

Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

Abstract:

Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

Procedia PDF Downloads 99

Authors: Mohammed Abdelsabour-Khalaf, F. Yusuf , B Brand-Saberi

Abstract:

Purpose: Applications of nanotechnology nowadays extended to include a wide range of scientific areas such electron micrscopy and gene expression. The aim of the current study was to investigate the developmental expression pattern of genes involved in human glomerulo-nephropathies associated with massive proteinuria and podocyte differentiation using the chicken mesonephros as a model system. Method: We performed in situ hybridization using chicken specific mRNA probes for genes expressed in the early nephron and slit diaphragm genes. The probes used were cNeph1, cNeph2, cSim1, cLmx1b, and cAtoh8. Chicken embryos from Hamburger Hamilton developmental stage HH19 (E3) to HH 34 (E9) were used for the in situ hybridization (ISH). ISH was performed on whole mount embryos which were sectioned by vibratome. Results: Our result show that Neph1, Neph2, Sim1. Lmx1b and Atoh8 genes are dynamically expressed during nephron morphogenesis and Neph1 and Atoh8 are also specifically expressed in the podocytes during late stages of differentiation. Conclusion: We conclude from our results that the genes implicated in congenital and acquired glomerulo-nephropathies like Neph1 and Neph2 are dynamically expressed during mesonephros development pointing towards a role in the formation of the filtration barrier and the differentiation of the mesonephric podocytes. Thus the avian mesonephros could serve as a model to study human kidney diseases.

Keywords: mesonephros, chicken embryo, gene expression, immunohistochemistry

Procedia PDF Downloads 583

Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

Abstract:

Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

Procedia PDF Downloads 85

Authors: Khin Thanda Win, Chunying Zhang, Sanghyeob Lee

Abstract:

Mildew resistance locus o (Mlo), a plant-specific gene family with seven-transmembrane (TM), plays an important role in plant resistance to powdery mildew (PM). PM caused by Podosphaera xanthii is a widespread plant disease and probably represents the major fungal threat for many Cucurbits. The recent Cucurbita maxima genome sequence data provides an opportunity to identify and characterize the MLO gene family in this species. Total twenty genes (designated CmaMLO1 through CmaMLO20) have been identified by using an in silico cloning method with the MLO gene sequences of Cucumis sativus, Cucumis melo, Citrullus lanatus and Cucurbita pepo as probes. These CmaMLOs were evenly distributed on 15 chromosomes of 20 C. maxima chromosomes without any obvious clustering. Multiple sequence alignment showed that the common structural features of MLO gene family, such as TM domains, a calmodulin-binding domain and 30 important amino acid residues for MLO function, were well conserved. Phylogenetic analysis of the CmaMLO genes and other plant species reveals seven different clades (I through VII) and only clade IV is specific to monocots (rice, barley, and wheat). Phylogenetic and structural analyses provided preliminary evidence that five genes belonged to clade V could be the susceptibility genes which may play the importance role in PM resistance. This study is the first comprehensive report on MLO genes in C. maxima to our knowledge. These findings will facilitate the functional analysis of the MLOs related to PM susceptibility and are valuable resources for the development of disease resistance in pumpkin.

Keywords: Mildew resistance locus o (Mlo), powdery mildew, phylogenetic relationship, susceptibility genes

Procedia PDF Downloads 157

Authors: Lae-Jeong Park

Abstract:

The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: pedestrian detection, color segmentation, false positive, feature extraction

Procedia PDF Downloads 255