Search results for: objects detection

Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

Procedia PDF Downloads 280

Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary

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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.

Keywords: k-nearest neighbor (knn), face detection, vitiligo, bone deformity

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Authors: Bal Chandra Yadav, Rakesh K. Sonker, Anjali Sharma, Punit Tyagi, Vinay Gupta, Monika Tomar

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In the present study, ZnO doped SnO2 thin films of various compositions were deposited on the surface of a corning substrate by dropping the two sols containing the precursors for composite (ZSO) with subsequent heat treatment. The sensor materials used for selective detection of nitrogen dioxide (NO2) were designed from the correlation between the sensor composition and gas response. The available NO2 sensors are operative at very high temperature (150-800 °C) with low sensing response (2-100) even in higher concentrations. Efforts are continuing towards the development of NO2 gas sensor aiming with an enhanced response along with a reduction in operating temperature by incorporating some catalysts or dopants. Thus in this work, a novel sensor structure based on ZSO nanocomposite has been fabricated using chemical route for the detection of NO2 gas. The structural, surface morphological and optical properties of prepared films have been studied by using X-ray diffraction (XRD), Atomic force microscopy (AFM), Transmission electron microscope (TEM) and UV-visible spectroscopy respectively. The effect of thickness variation from 230 nm to 644 nm of ZSO composite thin film has been studied and the ZSO thin film of thickness ~ 460 nm was found to exhibit the maximum gas sensing response ~ 2.1×103 towards 20 ppm NO2 gas at an operating temperature of 90 °C. The average response and recovery times of the sensor were observed to be 3.51 and 6.91 min respectively. Selectivity of the sensor was checked with the cross-exposure of vapour CO, acetone, IPA, CH4, NH3 and CO2 gases. It was found that besides the higher sensing response towards NO2 gas, the prepared ZSO thin film was also highly selective towards NO2 gas.

Keywords: ZSO nanocomposite thin film, ZnO tetrapod structure, NO2 gas sensor, sol-gel method

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Authors: Shuo Li, Yannick Coffinier, Chann Lagadec, Fabrizio Cleri, Katsuhiko Nishiguchi, Akira Fujiwara, Soo Hyeon Kim, Nicolas Clément

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The need for single cell detection and analysis techniques has increased in the past decades because of the heterogeneity of individual living cells, which increases the complexity of the pathogenesis of malignant tumors. In the search for early cancer detection, high-precision medicine and therapy, the technologies most used today for sensitive detection of target analytes and monitoring the variation of these species are mainly including two types. One is based on the identification of molecular differences at the single-cell level, such as flow cytometry, fluorescence-activated cell sorting, next generation proteomics, lipidomic studies, another is based on capturing or detecting single tumor cells from fresh or fixed primary tumors and metastatic tissues, and rare circulating tumors cells (CTCs) from blood or bone marrow, for example, dielectrophoresis technique, microfluidic based microposts chip, electrochemical (EC) approach. Compared to other methods, EC sensors have the merits of easy operation, high sensitivity, and portability. However, despite various demonstrations of low limits of detection (LOD), including aptamer sensors, arrayed EC sensors for detecting single-cell have not been demonstrated. In this work, a new technique based on 20-nm-thick nanopillars array to support cells and keep them at ideal recognition distance for redox-labeled aptamers grafted on the surface. The key advantages of this technology are not only to suppress the false positive signal arising from the pressure exerted by all (including non-target) cells pushing on the aptamers by downward force but also to stabilize the aptamer at the ideal hairpin configuration thanks to a confinement effect. With the first implementation of this technique, a LOD of 13 cells (with5.4 μL of cell suspension) was estimated. In further, the nanosupported cell technology using redox-labeled aptasensors has been pushed forward and fully integrated into a single-cell electrochemical aptasensor array. To reach this goal, the LOD has been reduced by more than one order of magnitude by suppressing parasitic capacitive electrochemical signals by minimizing the sensor area and localizing the cells. Statistical analysis at the single-cell level is demonstrated for the recognition of cancer cells. The future of this technology is discussed, and the potential for scaling over millions of electrodes, thus pushing further integration at sub-cellular level, is highlighted. Despite several demonstrations of electrochemical devices with LOD of 1 cell/mL, the implementation of single-cell bioelectrochemical sensor arrays has remained elusive due to their challenging implementation at a large scale. Here, the introduced nanopillar array technology combined with redox-labeled aptamers targeting epithelial cell adhesion molecule (EpCAM) is perfectly suited for such implementation. Combining nanopillar arrays with microwells determined for single cell trapping directly on the sensor surface, single target cells are successfully detected and analyzed. This first implementation of a single-cell electrochemical aptasensor array based on Brownian-fluctuating redox species opens new opportunities for large-scale implementation and statistical analysis of early cancer diagnosis and cancer therapy in clinical settings.

Keywords: bioelectrochemistry, aptasensors, single-cell, nanopillars

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: S. Jain, R. Savalia, V. Saini

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A simple, accurate, precise, sensitive and specific RP-HPLC method was developed and validated for simultaneous estimation of Metoprolol Succinate and Olmesartan Medoxomil in bulk and tablet dosage form. The RP-HPLC method has shown adequate separation for Metoprolol Succinate and Olmesartan Medoxomil from its degradation products. The separation was achieved on a Phenomenex luna ODS C18 (250mm X 4.6mm i.d., 5μm particle size) with an isocratic mixture of acetonitrile: 50mM phosphate buffer pH 4.0 adjusted with glacial acetic acid in the ratio of 55:45 v/v. The mobile phase at a flow rate of 1.0ml/min, Injection volume 20μl and wavelength of detection was kept at 225nm. The retention time for Metoprolol Succinate and Olmesartan Medoxomil was 2.451±0.1min and 6.167±0.1min, respectively. The linearity of the proposed method was investigated in the range of 5-50μg/ml and 2-20μg/ml for Metoprolol Succinate and Olmesartan Medoxomil, respectively. Correlation coefficient was 0.999 and 0.9996 for Metoprolol Succinate and Olmesartan Medoxomil, respectively. The limit of detection was 0.2847μg/ml and 0.1251μg/ml for Metoprolol Succinate and Olmesartan Medoxomil, respectively and the limit of quantification was 0.8630μg/ml and 0.3793μg/ml for Metoprolol and Olmesartan, respectively. Proposed methods were validated as per ICH guidelines for linearity, accuracy, precision, specificity and robustness for estimation of Metoprolol Succinate and Olmesartan Medoxomil in commercially available tablet dosage form and results were found to be satisfactory. Thus the developed and validated stability indicating method can be used successfully for marketed formulations.

Keywords: metoprolol succinate, olmesartan medoxomil, RP-HPLC method, validation, ICH

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Authors: Mohammed Al-Shalabi

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In recent years, the Internet of Things (IoT) has become an important technology for connecting various smart objects in many applications, especially in health monitoring applications. The IoT technology develops healthcare applications from traditional to personalized ones by making the treatment and monitoring of patients easier. Nowadays, especially during the COVID-19 pandemic, the necessity of enhancing healthcare monitoring applications to avoid the spreading of this pandemic is increased. In this paper, a mechanism is proposed to minimize direct contact with COVID-19 patients who need ventilators by using IoT technology. The proposed mechanism saves medical providers from COVID-19 infection and then minimizes the spreading of this disease.

Keywords: internet of things, healthcare monitoring, COVID-19, ventilators, thingspeak, clouds

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Authors: K. Doi, T. Nishimura, M. Umeda

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There are very few sound interfaces that both healthy people and hearing handicapped people can use to play together. In this study, we developed a sound tactile interface that makes use of the human sensation of stiffness. The interface comprises eight elastic objects having varying degrees of stiffness. Each elastic object is shaped like a column. When people with and without hearing disabilities press each elastic object, different sounds are produced depending on the stiffness of the elastic object. The types of sounds used were “Do Re Mi sounds.” The interface has a major advantage in that people with or without hearing disabilities can play with it. We found that users were able to recognize the hardness sensation and relate it to the corresponding Do Re Mi sounds.

Keywords: tactile sense, sound interface, stiffness perception, elastic object

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Authors: Kaida Kobylka

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Museum education policy can be developed through the lens of radical democracy and radically democratic relational aesthetics to provoke a more wholistic, agonistic, and utopian educational experiences that expand a viewer’s experiences and knowledge of artwork in a museum’s permanent collection to encourage a deeper understanding of art and the community of a museum’s connections to equity, diversity, inclusion, and decolonization. Practices used by the museum will create cohesive and engaging informal education that utilizes community-based, alternative knowledge and create dignity-safe spaces for viewers to engage critically with the visual objects.

Keywords: museum education, radical democracy, Canadian policy, community-based knowledge

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Authors: Alka Jakhar, Harmanpreet Kaur Sandhu, Samaresh Das

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The Terahertz (THz) technology-based devices are heightening at an alarming rate on account of the wide range of applications in imaging, security, communication, and spectroscopic field. The various available room operational THz detectors, including Golay cell, pyroelectric detector, field-effect transistors, and photoconductive antennas, have some limitations such as narrow-band response, slow response speed, transit time limits, and complex fabrication process. There is an urgent demand to explore new materials and device structures to accomplish efficient THz detection systems. Recently, TMDs including topological semimetals and topological insulators such as PtSe₂, MoTe₂, WSe₂, and PtTe₂ provide novel feasibility for photonic and optical devices. The peculiar properties of these materials, such as Dirac cone, fermions presence, nonlinear optical response, high conductivity, and ambient stability, make them worthy for the development of the THz devices. Here, the platinum telluride (PtTe₂) based devices have been demonstrated for THz detection in the frequency range of 0.1-1 THz. The PtTe₂ is synthesized by direct selenization of the sputtered platinum film on the high-resistivity silicon substrate by using the chemical vapor deposition (CVD) method. The Raman spectra, XRD, and XPS spectra confirm the formation of the thin PtTe₂ film. The PtTe₂ channel length is 5µm and it is connected with a bow-tie antenna for strong THz electric field confinement in the channel. The characterization of the devices has been carried out in a wide frequency range from 0.1-1 THz. The induced THz photocurrent is measured by using lock-in-amplifier after preamplifier. The maximum responsivity is achieved up to 1 A/W under self-biased mode. Further, this responsivity has been increased by applying biasing voltage. This photo response corresponds to low energy THz photons is mainly due to the photo galvanic effect in PtTe₂. The DC current is induced along the PtTe₂ channel, which is directly proportional to the amplitude of the incident THz electric field. Thus, these new topological semimetal materials provide new pathways for sensitive detection and sensing applications in the THz domain.

Keywords: terahertz, detector, responsivity, topological-semimetals

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Authors: Hyeon-seoung Kim, Sang-mi Park, Sun-ju Han, Leen-seok Kang

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The visualization technology of construction information using 3D and nD modeling can satisfy the visualization needs of each construction project participant. The nD CAD system is a tool that the construction information, such as construction schedule, cost and resource utilization, are simulated by 4D, 5D and 6D object formats based on 3D object. This study developed a methodology and simulation engine for nD CAD system for construction project management. It has improved functions such as built-in schedule generation, cost simulation of changed budget and built-in resource allocation comparing with the current systems. To develop an integrated nD CAD system, this study attempts an integrated method to link 5D and 6D objects based on 4D object.

Keywords: building information modeling, visual simulation, 3D object, nD CAD augmented reality

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Nastaran Hemmati, Farhad Nikkhahi, Amir Javadi, Sahar Eskandarion, Seyed Mahmuod Amin Marashi

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Neisseria meningitidis, Escherichia coli K, Streptococcus agalactiae, and Streptococcus pneumoniae cause 90% of bacterial meningitis. Almost all infected people die or have irreversible neurological complications. Therefore, it is essential to have a diagnostic kit with the ability to quickly detect these fatal infections. The project involved 212 patients from whom cerebrospinal fluid samples were obtained. After total genome extraction and performing multiplex quantitative polymerase chain reaction (qPCR), the presence or absence of each infectious factor was determined by comparing with standard strains. The specificity, sensitivity, positive predictive value, and negative predictive value calculated were 100%, 92.9%, 50%, and 100%, respectively. So, due to the high specificity and sensitivity of the designed primers, they can be used instead of bacterial culture that takes at least 24 to 48 hours. The remarkable benefit of this method is associated with the speed (up to 3 hours) at which the procedure could be completed. It is also worth noting that this method can reduce the personnel unintentional errors which may occur in the laboratory. On the other hand, as this method simultaneously identifies four common factors that cause bacterial meningitis, it could be used as an auxiliary method diagnostic technique in laboratories particularly in cases of emergency medicine.

Keywords: cerebrospinal fluid, meningitis, quantitative polymerase chain reaction, simultaneous detection, diagnosis testing

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Authors: Nguyen Chan Hung, Mai Ngoc Anh, Nguyen Xuan Ha, Tran Xuan Duc, Dang Bao Lam, Nguyen Hoang Viet

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This paper presents a design and implementation of a navigation controller for a humanoid mobile robot platform to operate in indoor office environments. In order to fulfil the requirement of recognizing and approaching human to provide service while avoiding random obstacles, a behavior-based fuzzy logic controller was designed to simultaneously coordinate multiple behaviors. Experiments in real office environment showed that the fuzzy controller deals well with complex scenarios without colliding with random objects and human.

Keywords: behavior control, fuzzy logic, humanoid robot, mobile robot

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Authors: Philip Edinger, Christian Ludwig

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The formation and control of tars remain one of the major challenges in the implementation of biomass gasification technologies. Robust, on-line analytical methods are needed to investigate the fate of tar compounds when different measures for their reduction are applied. This work establishes an on-line UV-Vis method, based on a liquid quench sampling system, to monitor tar compounds in biomass gasification processes. Recorded spectra from the liquid phase were analyzed for their tar composition by means of a classical least squares (CLS) and partial least squares (PLS) approach. This allowed for the detection of UV-Vis active tar compounds with detection limits in the low part per million by volume (ppmV) region. The developed method was then applied to two case studies. The first involved a lab-scale reactor, intended to investigate the decomposition of a limited number of tar compounds across a catalyst. The second study involved a gas scrubber as part of a pilot scale wood gasification plant. Tar compound quantification results showed good agreement with off-line based reference methods (GC-FID) when the complexity of tar composition was limited. The two case studies show that the developed method can provide rapid, qualitative information on the tar composition for the purpose of process monitoring. In cases with a limited number of tar species, quantitative information about the individual tar compound concentrations provides an additional benefit of the analytical method.

Keywords: biomass gasification, on-line, tar, UV-Vis

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Authors: Ergun Sakalar, Kubra Bilgic

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In the last decades, real-time PCR has become a reliable tool preferred to use in many laboratories for pathogen detection. This technique allows for monitoring target amplification via fluorescent molecules besides admit of quantitative analysis by enabling of convert outcomes of thermal cycling to digital data. Sensitivity and traceability of real-time PCR are based on measuring of fluorescence that appears only when fluorescent reporter dye bound to specific target DNA.The fluorescent reporter systems developed for this purpose are divided into two groups. The first group consists of intercalator fluorescence dyes such as SYBR Green, EvaGreen which binds to double-stranded DNA. On the other hand, the second group includes fluorophore-labeled oligonucleotide probes that are separated into three subgroups due to differences in mechanism of action; initial primer-probes such as Cyclicons, Angler®, Amplifluor®, LUX™, Scorpions, and the second one hydrolysis probes like TaqMan, Snake assay, finally hybridization probes, for instance, Molecular Beacons, Hybprobe/FRET, HyBeacon™, MGB-Eclipse, ResonSense®, Yin-Yang, MGB-Pleiades. In addition nucleic acid analogues, an increase of probe affinity to target site is also employed with fluorescence-labeled probes. Consequently, abundant real-time PCR detection chemistries are chosen by researcher according to the field of application, mechanism of action, advantages, and proper structures of primer/probes.

Keywords: fluorescent dye, food safety, molecular probes, nucleic acid analogues

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Authors: Wu Liching

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Aim This case aims to discuss the pathogenesis, clinical manifestations and medical treatment of strokes caused by mitochondrial gene mutations. Methods Diagnosis of ischemic stroke caused by mitochondrial gene defect by means of "next-generation sequencing mitochondrial DNA gene variation detection", imaging examination, neurological examination, and medical history; this study took samples from the neurology ward of a medical center in northern Taiwan cases diagnosed with acute cerebral infarction as the research objects. Result This case is a 49-year-old married woman with a rare disease, mitochondrial gene mutation inducing ischemic stroke. She has severe hearing impairment and needs to use hearing aids, and has a history of diabetes. During the patient’s hospitalization, the blood test showed that serum Lactate: 7.72 mmol/L, Lactate (CSF) 5.9 mmol/L. Through the collection of relevant medical history, neurological evaluation showed changes in consciousness and cognition, slow response in language expression, and brain magnetic resonance imaging examination showed subacute bilateral temporal lobe infarction, which was an atypical type of stroke. The lineage DNA gene has m.3243A>G known pathogenic mutation point, and its heteroplasmic level is 24.6%. This pathogenic point is located in MITOMAP and recorded as Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes (MELAS) , Leigh Syndrome and other disease-related pathogenic loci, this mutation is located in ClinVar and recorded as Pathogenic (dbSNP: rs199474657), so it is diagnosed as a case of stroke caused by a rare disease mitochondrial gene mutation. After medical treatment, there was no more seizure during hospitalization. After interventional rehabilitation, the patient's limb weakness, poor language function, and cognitive impairment have all improved significantly. Conclusion Mitochondrial disorders can also be associated with abnormalities in psychological, neurological, cerebral cortical function, and autonomic functions, as well as problems with internal medical diseases. Therefore, the differential diagnoses cover a wide range and are not easy to be diagnosed. After neurological evaluation, medical history collection, imaging and rare disease serological examination, atypical ischemic stroke caused by rare mitochondrial gene mutation was diagnosed. We hope that through this case, the diagnosis of rare disease mitochondrial gene variation leading to cerebral infarction will be more familiar to clinical medical staff, and this case report may help to improve the clinical diagnosis and treatment for patients with similar clinical symptoms in the future.

Keywords: acute stroke, MELAS, lactic acidosis, mitochondrial disorders

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Omar Youssef, Aija Knuuttila, Paivi Piirilä, Virinder Sarhadi, Sakari Knuutila

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Exhaled breath condensate (EBC) is a unique sample that allows studying different genetic changes in lung carcinoma through a non-invasive way. With the aid of next generation sequencing (NGS) technology, analysis of genetic mutations has been more efficient with increased sensitivity for detection of genetic variants. In order to investigate the possibility of applying this method for cancer diagnostics, mutations in EBC DNA from lung cancer patients and healthy individuals were studied by using NGS. The key aim is to assess the feasibility of using this approach to detect clinically important mutations in EBC. EBC was collected from 20 healthy individuals and 9 lung cancer patients (four lung adenocarcinomas, four 8 squamous cell carcinoma, and one case of mesothelioma). Mutations in hotpot regions of 22 genes were studied by using Ampliseq Colon and Lung cancer panel and sequenced on Ion PGM. Results demonstrated that all nine patients showed a total of 19 cosmic mutations in APC, BRAF, EGFR, ERBB4, FBXW7, FGFR1, KRAS, MAP2K1, NRAS, PIK3CA, PTEN, RET, SMAD4, and TP53. In controls, 15 individuals showed 35 cosmic mutations in BRAF, CTNNB1, DDR2, EGFR, ERBB2, FBXW7, FGFR3, KRAS, MET, NOTCH1, NRAS, PIK3CA, PTEN, SMAD4, and TP53. Additionally, 45 novel mutations not reported previously were also seen in patients’ samples, and 106 novel mutations were seen in controls’ specimens. KRAS exon 2 mutations G12D was identified in one control specimen with mutant allele fraction of 6.8%, while KRAS G13D mutation seen in one patient sample showed mutant allele fraction of 17%. These findings illustrate that hotspot mutations are present in DNA from EBC of both cancer patients and healthy controls. As some of the cosmic mutations were seen in controls too, no firm conclusion can be drawn on the clinical importance of cosmic mutations in patients. Mutations reported in controls could represent early neoplastic changes or normal homeostatic process of apoptosis occurring in lung tissue to get rid of mutant cells. At the same time, mutations detected in patients might represent a non-invasive easily accessible way for early cancer detection. Follow up of individuals with important cancer mutations is necessary to clarify the significance of these mutations in both healthy individuals and cancer patients.

Keywords: exhaled breath condensate, lung cancer, mutations, next generation sequencing

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Authors: Ali Farzan

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Classification of data objects or patterns is a major part in most of Decision making systems. One of the popular and commonly used classification methods is Decision Tree (DT). It is a hierarchical decision making system by which a binary tree is constructed and starting from root, at each node some of the classes is rejected until reaching the leaf nods. Each leaf node is a representative of one specific class. Finding the splitting criteria in each node for constructing or training the tree is a major problem. Particle Swarm Optimization (PSO) has been adopted as a metaheuristic searching method for finding the best splitting criteria. Result of evaluating the proposed method over benchmark datasets indicates the higher accuracy of the new PSO based decision tree.

Keywords: decision tree, particle swarm optimization, splitting criteria, metaheuristic

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Authors: Sana Gul, Sheeba Murad, Aneela Javed

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Introduction: Human Papilloma Virus (HPV) is small DNA virus mostly infecting mucosa and cutaneous keratinocytes. So far, more than 200 Human papillomaviruses are known. HPV have been divided into high- and low-risk on the basis of their oncogenic potential. High risk HPV is considered to be the main etiological cause for cervical cancer. Objective: Current study was designed to screen the local cervical cancer patients from the twin cities of Pakistan for the occurance of high risk HPV. Methodology: A total of 67 formalin fixed paraffin-embedded samples of cervical cancer biopsies were obtained from the government hospitals in Islamabad and Rawalpindi. Cervical cancer biopsies were examined for the presence of HPV DNA. Polymerase chain reaction (PCR) was used for the amplification of a region in the HPV-L1 gene for the general detection of the Papilloma virus and for the genotype specific detection of high risk HPV 16 and 18 using the GP5/GP6 primers and genotype specific primers respectively. Results: HPV DNA was detected in 59 out of 67 samples analyzed. 30 samples showed the presence of HPV16 while 22 samples were positive for HPV 18 . HPV subtype could not be determined in 7 samples. Conclusion: Our results show a strong association between HPV infection and cervical cancer among women in twin cities of Pakistan. One way to minimize the disease burden in relation to HPV infection in Pakistani population is the use of prophylactic vaccines and routine screening. An early diagnosis of HPV infection will allow better health management to reduce the risk of developing cervical cancer.

Keywords: cervical cancer, Pakistan, human papillomavirus, HPV 16

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Authors: Chen Wang, Jared Evans, Yan Asmann

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With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.

Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing

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Authors: Bivek Bhusal, Ana Legrand

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Detection of crop stress is one of the major applications of remote sensing in agriculture. Multiple studies have demonstrated the capability of remote sensing using Unmanned Aerial Vehicle (UAV)-based multispectral imagery for detection of plant stress, but none so far on Nitrogen (N) stress and PLH feeding stress on green beans. In view of its wide host range, geographical distribution, and damage potential, Potato leafhopper- Empoasca fabae (Harris) has been emerging as a key pest in several countries. Monitoring methods for potato leafhopper (PLH) damage, as well as the laboratory techniques for detecting Nitrogen deficiency, are time-consuming and not always easily affordable. A study was initiated to demonstrate if the multispectral sensor attached to a drone can detect PLH stress and N deficiency in beans. Small-plot trials were conducted in the summer of 2023, where cages were used to manipulate PLH infestation in green beans (Provider cultivar) at their first-trifoliate stage. Half of the bean plots were introduced with PLH, and the others were kept insect-free. Half of these plots were grown with the recommended amount of N, and the others were grown without N. Canopy reflectance was captured using a five-band multispectral sensor. Our findings indicate that drone imagery could detect stress due to a lack of N and PLH damage in beans.

Keywords: potato leafhopper, nitrogen, remote sensing, spectral reflectance, beans

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

Procedia PDF Downloads 178

Authors: Pavlina Capkova, Josef Srovnal, Vera Becvarova, Marie Trkova, Zuzana Capkova, Andrea Stefekova, Vaclava Curtisova, Alena Santava, Sarka Vejvalkova, Katerina Adamova, Radek Vodicka

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ASDs are heterogeneous and complex developmental diseases with a significant genetic background. Recurrent CNVs are known to be a frequent cause of ASD. These CNVs can have, however, a variable expressivity which results in a spectrum of phenotypes from asymptomatic to ID/DD/ASD. ASD is associated with ID in ~75% individuals. Various platforms are used to detect pathogenic mutations in the genome of these patients. The performed study is focused on a determination of the frequency of pathogenic mutations in a group of ASD patients and a group of ID/DD patients using various strategies along with a comparison of their detection rate. The possible role of the origin of these mutations in aetiology of ASD was assessed. The study included 35 individuals with ASD and 68 individuals with ID/DD (64 males and 39 females in total), who underwent rigorous genetic, neurological and psychological examinations. Screening for pathogenic mutations involved karyotyping, screening for FMR1 mutations and for metabolic disorders, a targeted MLPA test with probe mixes Telomeres 3 and 5, Microdeletion 1 and 2, Autism 1, MRX and a chromosomal microarray analysis (CMA) (Illumina or Affymetrix). Chromosomal aberrations were revealed in 7 (1 in the ASD group) individuals by karyotyping. FMR1 mutations were discovered in 3 (1 in the ASD group) individuals. The detection rate of pathogenic mutations in ASD patients with a normal karyotype was 15.15% by MLPA and CMA. The frequencies of the pathogenic mutations were 25.0% by MLPA and 35.0% by CMA in ID/DD patients with a normal karyotype. CNVs inherited from asymptomatic parents were more abundant than de novo changes in ASD patients (11.43% vs. 5.71%) in contrast to the ID/DD group where de novo mutations prevailed over inherited ones (26.47% vs. 16.18%). ASD patients shared more frequently their mutations with their fathers than patients from ID/DD group (8.57% vs. 1.47%). Maternally inherited mutations predominated in the ID/DD group in comparison with the ASD group (14.7% vs. 2.86 %). CNVs of an unknown significance were found in 10 patients by CMA and in 3 patients by MLPA. Although the detection rate is the highest when using CMA, recurrent CNVs can be easily detected by MLPA. CMA proved to be more efficient in the ID/DD group where a larger spectrum of rare pathogenic CNVs was revealed. This study determined that maternally inherited highly penetrant mutations and de novo mutations more often resulted in ID/DD without ASD in patients. The paternally inherited mutations could be, however, a source of the greater variability in the genome of the ASD patients and contribute to the polygenic character of the inheritance of ASD. As the number of the subjects in the group is limited, a larger cohort is needed to confirm this conclusion. Inherited CNVs have a role in aetiology of ASD possibly in combination with additional genetic factors - the mutations elsewhere in the genome. The identification of these interactions constitutes a challenge for the future. Supported by MH CZ – DRO (FNOl, 00098892), IGA UP LF_2016_010, TACR TE02000058 and NPU LO1304.

Keywords: autistic spectrum disorders, copy number variant, chromosomal microarray, intellectual disability, karyotyping, MLPA, multiplex ligation-dependent probe amplification

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Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: J. Ierache, N. A. Mangiarua, S. A. Bevacqua, N. N. Verdicchio, M. E. Becerra, D. R. Sanz, M. E. Sena, F. M. Ortiz, N. D. Duarte, S. Igarza

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Augmented Reality is a technology that involves the overlay of virtual content, which is context or environment sensitive, on images of the physical world in real time. This paper presents the development of a catalog system that facilitates and allows the creation, publishing, management and exploitation of augmented multimedia contents and Augmented Reality applications, creating an own space for anyone that wants to provide information to real objects in order to edit and share it then online with others. These spaces would be built for different domains without the initial need of expert users. Its operation focuses on the context of Web 2.0 or Social Web, with its various applications, developing contents to enrich the real context in which human beings act permitting the evolution of catalog’s contents in an emerging way.

Keywords: augmented reality, catalog system, computer graphics, mobile application

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Anita, Sari Septiani Tangke, Rusdina Bte Ladju, Nasrum Massi

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New diagnostic tools are urgently needed to interrupt the transmission of tuberculosis and multidrug-resistant tuberculosis. The microscopic-observation drug-susceptibility (MODS) assay is a rapid, accurate and simple liquid culture method to detect multidrug-resistant tuberculosis (MDR-TB). MODS were evaluated to determine a lower and same concentration of isoniazid and rifampin for detection of MDR-TB. Direct drug-susceptibility testing was performed with the use of the MODS assay. Drug-sensitive control strains were tested daily. The drug concentrations that used for both isoniazid and rifampin were at the same concentration: 0.16, 0.08 and 0.04μg per milliliter. We tested 56 M. tuberculosis clinical isolates and the control strains M. tuberculosis H37RV. All concentration showed same result. Of 53 M. tuberculosis clinical isolates, 14 were MDR-TB, 38 were susceptible with isoniazid and rifampin, 1 was resistant with isoniazid only. Drug-susceptibility testing was performed with the use of the proportion method using Mycobacteria Growth Indicator Tube (MGIT) system as reference. The result of MODS assay using lower concentration was significance (P<0.001) compare with the reference methods. A lower and same concentration of isoniazid and rifampin can be used to detect MDR-TB. Operational cost and application can be more efficient and easier in resource-limited environments. However, additional studies evaluating the MODS using lower and same concentration of isoniazid and rifampin must be conducted with a larger number of clinical isolates.

Keywords: isoniazid, MODS assay, MDR-TB, rifampin

Procedia PDF Downloads 320