Search results for: object detection with multimodal inputs

Authors: Christian Fercher, Jiaul Islam, Simon R. Corrie

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Fluorescence-based immunodiagnostics are an emerging field in biosensor development and exhibit several advantages over traditional detection methods. While various affinity biosensors have been developed to generate a fluorescence signal upon sensing varying concentrations of analytes, reagentless, reversible, and continuous monitoring of complex biological samples remains challenging. Here, we aimed to genetically engineer biosensors based on single-chain antibody fragments (scFv) that are site-specifically labeled with environmentally sensitive fluorescent unnatural amino acids (UAA). A rational design approach resulted in quantifiable analyte-dependent changes in peak fluorescence emission wavelength and enabled antigen detection in vitro. Incorporation of a polarity indicator within the topological neighborhood of the antigen-binding interface generated a titratable wavelength blueshift with nanomolar detection limits. In order to ensure continuous analyte monitoring, scFv candidates with fast binding and dissociation kinetics were selected from a genetic library employing a high-throughput phage display and affinity screening approach. Initial rankings were further refined towards rapid dissociation kinetics using bio-layer interferometry (BLI) and surface plasmon resonance (SPR). The most promising candidates were expressed, purified to homogeneity, and tested for their potential to detect biomarkers in a continuous microfluidic-based assay. Variations of dissociation kinetics within an order of magnitude were achieved without compromising the specificity of the antibody fragments. This approach is generally applicable to numerous antibody/antigen combinations and currently awaits integration in a wide range of assay platforms for one-step protein quantification.

Keywords: antibody engineering, biosensor, phage display, unnatural amino acids

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Authors: D. P. Houhoula, J. Papaparaskevas, S. Konteles, A. Dargenta, A. Farka, C. Spyrou, M. Ziaka, S. Koussisis, E. Charvalos

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Objectives: Nanotechnology is providing revolutionary opportunities for the rapid and simple diagnosis of many infectious diseases. Staphylococcus aureus, Listeria monocytogenes and Salmonella enteritis are important human pathogens. Diagnostic assays for bacterial culture and identification are time consuming and laborious. There is an urgent need to develop rapid, sensitive, and inexpensive diagnostic tests. In this study, a gold nanoprobe strategy developed and relies on the colorimetric differentiation of specific DNA sequences based approach on differential aggregation profiles in the presence or absence of specific target hybridization. Method: Gold nanoparticles (AuNPs) were purchased from Nanopartz. They were conjugated with thiolated oligonucleotides specific for the femA gene for the identification of members of Staphylococcus aureus, the mecA gene for the differentiation of Staphylococcus aureus and MRSA Staphylococcus aureus, hly gene encoding the pore-forming cytolysin listeriolysin for the identification of Listeria monocytogenes and the invA sequence for the identification of Salmonella enteritis. DNA isolation from Staphylococcus aureus Listeria monocytogenes and Salmonella enteritis cultures was performed using the commercial kit Nucleospin Tissue (Macherey Nagel). Specifically 20μl of DNA was diluted in 10mMPBS (pH5). After the denaturation of 10min, 20μl of AuNPs was added followed by the annealing step at 58oC. The presence of a complementary target prevents aggregation with the addition of acid and the solution remains pink, whereas in the opposite event it turns to purple. The color could be detected visually and it was confirmed with an absorption spectrum. Results: Specifically, 0.123 μg/μl DNA of St. aureus, L.monocytogenes and Salmonella enteritis was serially diluted from 1:10 to 1:100. Blanks containing PBS buffer instead of DNA were used. The application of the proposed method on isolated bacteria produced positive results with all the species of St. aureus and L. monocytogenes and Salmonella enteritis using the femA, mecA, hly and invA genes respectively. The minimum detection limit of the assay was defined at 0.2 ng/μL of DNA. Below of 0.2 ng/μL of bacterial DNA the solution turned purple after addition of HCl, defining the minimum detection limit of the assay. None of the blank samples was positive. The specificity was 100%. The application of the proposed method produced exactly the same results every time (n = 4) the evaluation was repeated (100% repeatability) using the femA, hly and invA genes. Using the gene mecA for the differentiation of Staphylococcus aureus and MRSA Staphylococcus aureus the method had a repeatability 50%. Conclusion: The proposed method could be used as a highly specific and sensitive screening tool for the detection and differentiation of Staphylococcus aureus Listeria monocytogenes and Salmonella enteritis. The use AuNPs for the colorimetric detection of DNA targets represents an inexpensive and easy-to-perform alternative to common molecular assays. The technology described here, may develop into a platform that could accommodate detection of many bacterial species.

Keywords: gold nanoparticles, pathogens, nanotechnology, bacteria

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Authors: Miho Yamashita

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In recent years, there have been attempts to introduce Machine Translation (MT) into foreign language teaching, especially in writing instructions. This is because the performance of neural machine translation has improved dramatically since 2016, and some university instructors started to introduce MT translations to their students as a "good model" to learn from. However, MT is still not perfect, and there are many incorrect translations. In order to translate the intended text into a foreign language, it is necessary to edit the original manuscript written in the native language (pre-edit) and revise the translated foreign language text (post-edit). The latter is considered especially difficult for users without a high proficiency level of foreign language. Therefore, the author allowed her students to use MT in her writing class in one of the private universities in Japan and investigated 1) how groups of students with different English proficiency levels revised MT translations when translating Japanese manuscripts into English and 2) whether the post-edit process differed when the students revised alone or in pairs. The results showed that in 1), certain non-post-edited grammatical errors were found regardless of their proficiency levels, indicating the need for teacher intervention, and in 2), more appropriate corrections were found in pairs, and their frequent use of a dictionary was also observed. In this presentation, the author will discuss how MT writing instruction can be integrated effectively in an aim to achieve multimodal foreign language education.

Keywords: machine translation, writing instruction, pre-edit, post-edit

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Authors: Ihn Sook Jeong, Eun Joo Lee, Jae Hyung Kim, Gun Ho Kim, Young Jun Hwang

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This study investigated intravenous (IV) infiltration using bioelectrical impedance for 27 hospitalized patients in a long-term care hospital. Impedance parameters showed significant differences before and after infiltration as follows. First, the resistance (R) after infiltration significantly decreased compared to the initial resistance. This indicates that the IV solution flowing from the vein due to infiltration accumulates in the extracellular fluid (ECF). Second, the relative resistance at 50 kHz was 0.94 ± 0.07 in 9 subjects without infiltration and was 0.75 ± 0.12 in 18 subjects with infiltration. Third, the magnitude of the reactance (Xc) decreased after infiltration. This is because IV solution and blood components released from the vein tend to aggregate in the cell membrane (and acts analogously to the linear/parallel circuit), thereby increasing the capacitance (Cm) of the cell membrane and reducing the magnitude of reactance. Finally, the data points plotted in the R-Xc graph were distributed on the upper right before infiltration but on the lower left after infiltration. This indicates that the infiltration caused accumulation of fluid or blood components in the epidermal and subcutaneous tissues, resulting in reduced resistance and reactance, thereby lowering integrity of the cell membrane. Our findings suggest that bioelectrical impedance is an effective method for detection of infiltration in a noninvasive and quantitative manner.

Keywords: intravenous infiltration, impedance, parameters, resistance, reactance

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Authors: Mona Mihailescu, Rebeca Tudor, Irina A. Paun, Cristian Kusko, Eugen I. Scarlat, Mihai Kusko

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Specific intensity distributions in the laser beams are required in many fields: optical communications, material processing, microscopy, optical tweezers. In optical communications, the information embedded in specific beams and the superposition of multiple beams can be used to increase the capacity of the communication channels, employing spatial modulation as an additional degree of freedom, besides already available polarization and wavelength multiplexing. In this regard, optical vortices present interest due to their potential to carry independent data which can be multiplexed at the transmitter and demultiplexed at the receiver. Also, in the literature were studied their combinations: 1) axial or perpendicular superposition of multiple optical vortices or 2) with other laser beam types: Bessel, Airy. Optical vortices, characterized by stationary ring-shape intensity and rotating phase, are achieved using computer generated holograms (CGH) obtained by simulating the interference between a tilted plane wave and a wave passing through a helical phase object. Here, we propose a method to combine information through the reunion of two CGHs. One is obtained using the helical phase distribution, characterized by its topological charge, m. The other is obtained using conical phase distribution, characterized by its radial factor, r0. Each CGH is obtained using plane wave with different tilts: km and kr for CGH generated from helical phase object and from conical phase object, respectively. These reunions of two CGHs are calculated to be phase optical elements, addressed on the liquid crystal display of a spatial light modulator, to optically process the incident beam for investigations of the diffracted intensity pattern in far field. For parallel reunion of two CGHs and high values of the ratio between km and kr, the bright ring from the first diffraction order, specific for optical vortices, is changed in an asymmetric intensity pattern: a number of circle arcs. Both diffraction orders (+1 and -1) are asymmetrical relative to each other. In different planes along the optical axis, it is observed that this asymmetric intensity pattern rotates around its centre: in the +1 diffraction order the rotation is anticlockwise and in the -1 diffraction order, the rotation is clockwise. The relation between m and r0 controls the diameter of the circle arcs and the ratio between km and kr controls the number of arcs. For perpendicular reunion of the two CGHs and low values of the ratio between km and kr, the optical vortices are multiplied and focalized in different planes, depending on the radial parameter. The first diffraction order contains information about both phase objects. It is incident on the phase masks placed at the receiver, computed using the opposite values for topological charge or for the radial parameter and displayed successively. In all, the proposed method is exploited in terms of constructive parameters, for the possibility offered by the combination of different types of beams which can be used in robust optical communications.

Keywords: asymmetrical diffraction orders, computer generated holograms, conical phase distribution, optical vortices, spatial light modulator

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Authors: Mohamed Gazzeh, Bradley Null, Fethi Tlili, Hichem Besbes

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The burgeoning global ownership of personal vehicles has posed a significant strain on urban infrastructure, notably parking facilities, leading to traffic congestion and environmental concerns. Effective parking management systems (PMS) are indispensable for optimizing urban traffic flow and reducing emissions. The most commonly deployed systems nowadays rely on computer vision technology. This paper explores the integration of radar sensors and simulation in the context of smart parking management. We concentrate on radar sensors due to their versatility and utility in automotive applications, which extends to PMS. Additionally, radar sensors play a crucial role in driver assistance systems and autonomous vehicle development. However, the resource-intensive nature of radar data collection for algorithm development and testing necessitates innovative solutions. Simulation, particularly the monoDrive simulator, an internal development tool used by NI the Test and Measurement division of Emerson, offers a practical means to overcome this challenge. The primary objectives of this study encompass simulating radar sensors to generate a substantial dataset for algorithm development, testing, and, critically, assessing the transferability of models between simulated and real radar data. We focus on occupancy detection in parking as a practical use case, categorizing each parking space as vacant or occupied. The simulation approach using monoDrive enables algorithm validation and reliability assessment for virtual radar sensors. It meticulously designed various parking scenarios, involving manual measurements of parking spot coordinates, orientations, and the utilization of TI AWR1843 radar. To create a diverse dataset, we generated 4950 scenarios, comprising a total of 455,400 parking spots. This extensive dataset encompasses radar configuration details, ground truth occupancy information, radar detections, and associated object attributes such as range, azimuth, elevation, radar cross-section, and velocity data. The paper also addresses the intricacies and challenges of real-world radar data collection, highlighting the advantages of simulation in producing radar data for parking lot applications. We developed classification models based on Support Vector Machines (SVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), exclusively trained and evaluated on simulated data. Subsequently, we applied these models to real-world data, comparing their performance against the monoDrive dataset. The study demonstrates the feasibility of transferring models from a simulated environment to real-world applications, achieving an impressive accuracy score of 92% using only one radar sensor. This finding underscores the potential of radar sensors and simulation in the development of smart parking management systems, offering significant benefits for improving urban mobility and reducing environmental impact. The integration of radar sensors and simulation represents a promising avenue for enhancing smart parking management systems, addressing the challenges posed by the exponential growth in personal vehicle ownership. This research contributes valuable insights into the practicality of using simulated radar data in real-world applications and underscores the role of radar technology in advancing urban sustainability.

Keywords: autonomous vehicle simulator, FMCW radar sensors, occupancy detection, smart parking management, transferability of models

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Authors: Kanchan J. Kakade, S. A. Annadate

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This paper presents design and development of underground coal mine monitoring using mbed arm cortex controller and ZigBee communication. Coal mine is a special type of mine which is dangerous in nature. Safety is the most important feature of a coal industry for proper functioning. It’s not only for employees and workers but also for environment and nation. Many coal producing countries in the world face phenomenal frequently occurred accidents in coal mines viz, gas explosion, flood, and fire breaking out during coal mines exploitation. Thus, such emissions of various gases from coal mines are necessary to detect with the help of robot. Coal is a combustible, sedimentary, organic rock, which is made up of mainly carbon, hydrogen and oxygen. Coal Mine Detection Robot mainly detects mash gas and carbon monoxide. The mash gas is the kind of the mixed gas which mainly make up of methane in the underground of the coal mine shaft, and sometimes it abbreviate to methane. It is formed from vegetation, which has been fused between other rock layers and altered by the combined effects of heat and pressure over millions of years to form coal beds. Coal has many important uses worldwide. The most significant uses of coal are in electricity generation, steel production, cement manufacturing and as a liquid fuel.

Keywords: Zigbee communication, various sensors, hazardous gases, mbed arm cortex M3 core controller

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Authors: I. Parkova, A. Vališevskis, A. Viļumsone

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Nocturnal enuresis or bed-wetting is intermittent incontinence during sleep of children after age 5 that may precipitate wide range of behavioural and developmental problems. One of the non-pharmacological treatment methods is the use of a bed-wetting alarm system. In order to improve comfort conditions of nocturnal enuresis alarm system, modular moisture sensor should be replaced by a textile sensor. In this study behaviour and moisture detection speed of woven and sewn sensors were compared by analysing change in electrical resistance after solution (salt water) was dripped on sensor samples. Material of samples has different structure and yarn location, which affects solution detection rate. Sensor system circuit was designed and two sensor tests were performed: system activation test and false alarm test to determine the sensitivity of the system and activation threshold. Sewn sensor had better result in system’s activation test – faster reaction, but woven sensor had better result in system’s false alarm test – it was less sensitive to perspiration simulation. After experiments it was found that the optimum switching threshold is 3V in case of 5V input voltage, which provides protection against false alarms, for example – during intensive sweating.

Keywords: conductive yarns, moisture textile sensor, industry, material

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Authors: Abdelkhalek Bouchikhi, Elyes Benmokhtar, Sebastien Saletzki

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The aeronautical field is experiencing issues with RF spectrum congestion due to the constant increase in the number of flights, aircrafts and telecom systems on board. In addition, these systems are bulky in size, weight and energy consumption. The cognitive radio helps particularly solving the spectrum congestion issue by its capacity to detect idle frequency channels then, allowing an opportunistic exploitation of the RF spectrum. The present work aims to propose a new use case for aeronautical spectrum sharing and to study the performances of three different detection techniques: energy detector, matched filter and cyclostationary detector within the aeronautical use case. The spectrum in the proposed cognitive radio is allocated dynamically where each cognitive radio follows a cognitive cycle. The spectrum sensing is a crucial step. The goal of the sensing is gathering data about the surrounding environment. Cognitive radio can use different sensors: antennas, cameras, accelerometer, thermometer, etc. In IEEE 802.22 standard, for example, a primary user (PU) has always the priority to communicate. When a frequency channel witch used by the primary user is idle, the secondary user (SU) is allowed to transmit in this channel. The Distance Measuring Equipment (DME) is composed of a UHF transmitter/receiver (interrogator) in the aircraft and a UHF receiver/transmitter on the ground. While the future cognitive radio will be used jointly to alleviate the spectrum congestion issue in the aeronautical field. LDACS, for example, is a good candidate; it provides two isolated data-links: ground-to-air and air-to-ground data-links. The first contribution of the present work is a strategy allowing sharing the L-band. The adopted spectrum sharing strategy is as follow: the DME will play the role of PU which is the licensed user and the LDACS1 systems will be the SUs. The SUs could use the L-band channels opportunely as long as they do not causing harmful interference signals which affect the QoS of the DME system. Although the spectrum sensing is a key step, it helps detecting holes by determining whether the primary signal is present or not in a given frequency channel. A missing detection on primary user presence creates interference between PU and SU and will affect seriously the QoS of the legacy radio. In this study, first brief definitions, concepts and the state of the art of cognitive radio will be presented. Then, a study of three communication channel detection algorithms in a cognitive radio context is carried out. The study is made from the point of view of functions, material requirements and signal detection capability in the aeronautical field. Then, we presented a modeling of the detection problem by three different methods (energy, adapted filter, and cyclostationary) as well as an algorithmic description of these detectors is done. Then, we study and compare the performance of the algorithms. Simulations were carried out using MATLAB software. We analyzed the results based on ROCs curves for SNR between -10dB and 20dB. The three detectors have been tested with a synthetics and real world signals.

Keywords: aeronautic, communication, navigation, surveillance systems, cognitive radio, spectrum sensing, software defined radio

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Authors: Aleksandra Bylinska, Samantha Slight-Webb, Kevin Thomas, Miles Smith, Susan Macwana, Nicolas Dominguez, Eliza Chakravarty, Joan T. Merrill, Judith A. James, Joel M. Guthridge

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Background: Systemic Lupus Erythematosus (SLE) is an interferon-related autoimmune disease characterized by B cell dysfunction. One of the main hallmarks is a loss of tolerance to self-antigens leading to increased levels of autoantibodies against nuclear components (ANAs). However, up to 20% of healthy ANA+ individuals will not develop clinical illness. SLE is more prevalent among women and minority populations (African, Asian American and Hispanics). Moreover, African Americans have a stronger interferon (IFN) signature and develop more severe symptoms. The exact mechanisms involved in ethnicity-dependent B cell dysregulation and the progression of autoimmune disease from ANA+ healthy individuals to clinical disease remains unclear. Methods: Peripheral blood mononuclear cells (PBMCs) from African (AA) and European American (EA) ANA- (n=12), ANA+ (n=12) and SLE (n=12) individuals were assessed by multimodal scRNA-Seq/CITE-Seq methods to examine differential gene signatures in specific B cell subsets. Library preparation was done with a 10X Genomics Chromium according to established protocols and sequenced on Illumina NextSeq. The data were further analyzed for distinct cluster identification and differential gene signatures in the Seurat package in R and pathways analysis was performed using Ingenuity Pathways Analysis (IPA). Results: Comparing all subjects, 14 distinct B cell clusters were identified using a community detection algorithm and visualized with Uniform Manifold Approximation Projection (UMAP). The proportion of each of those clusters varied by disease status and ethnicity. Transitional B cells trended higher in ANA+ healthy individuals, especially in AA. Ribonucleoprotein high population (HNRNPH1 elevated, heterogeneous nuclear ribonucleoprotein, RNP-Hi) of proliferating Naïve B cells were more prevalent in SLE patients, specifically in EA. Interferon-induced protein high population (IFIT-Hi) of Naive B cells are increased in EA ANA- individuals. The proportion of memory B cells and plasma cells clusters tend to be expanded in SLE patients. As anticipated, we observed a higher signature of cytokine-related pathways, especially interferon, in SLE individuals. Pathway analysis among AA individuals revealed an NRF2-mediated Oxidative Stress response signature in the transitional B cell cluster, not seen in EA individuals. TNFR1/2 and Sirtuin Signaling pathway genes were higher in AA IFIT-Hi Naive B cells, whereas they were not detected in EA individuals. Interferon signaling was observed in B cells in both ethnicities. Oxidative phosphorylation was found in age-related B cells (ABCs) for both ethnicities, whereas Death Receptor Signaling was found only in EA patients in these cells. Interferon-related transcription factors were elevated in ABCs and IFIT-Hi Naive B cells in SLE subjects of both ethnicities. Conclusions: ANA+ healthy individuals have altered gene expression pathways in B cells that might drive apoptosis and subsequent clinical autoimmune pathogenesis. Increases in certain regulatory pathways may delay progression to SLE. Further, AA individuals have more elevated activation pathways that may make them more susceptible to SLE.

Keywords:

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Anupoju Rajeev, Joanne Mathew, Amit Shelke

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In the current study, a new type of Composite Structural Insulated Panels (CSIPs) is developed and investigated its performance against shock loading which can replace the conventional wooden structural materials. The CSIPs is made of Fibre Cement Board (FCB)/aluminum as the facesheet and the expanded polystyrene foam as the core material. As tornadoes are very often in the western countries, it is suggestable to monitor the health of the CSIPs during its lifetime. So, the composite structure is installed with three smart sensors located randomly at definite locations. Each smart sensor is fabricated with an embedded half stainless phononic crystal sensor attached to both ends of the nylon shaft that can resist the shock and impact on facesheet as well as polystyrene foam core and safeguards the system. In addition to the granular crystal sensors, the accelerometers are used in the horizontal spanning and vertical spanning with a definite offset distance. To estimate the health and damage of the CSIP panel using granular crystal sensor, shock wave loading experiments are conducted. During the experiments, the time of flight response from the granular sensors is measured. The main objective of conducting shock wave loading experiments on the CSIP panels is to study the effect and the sustaining capacity of the CSIP panels in the extreme hazardous situations like tornados and hurricanes which are very common in western countries. The effects have been replicated using a shock tube, an instrument that can be used to create the same wind and pressure intensity of tornado for the experimental study. Numerous experiments have been conducted to investigate the flexural strength of the CSIP. Furthermore, the study includes the damage detection using three smart sensors embedded in the CSIPs during the shock wave loading.

Keywords: composite structural insulated panels, damage detection, flexural strength, sandwich structures, shock wave loading

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Authors: Jae Won Lee, Kyung Hyun Min, Hong Jae Lee, Sang Cheon Lee

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To date, imaging techniques have attracted much attention in medicine because the detection of diseases at an early stage provides greater opportunities for successful treatment. Consequently, over the past few decades, diverse imaging modalities including magnetic resonance (MR), positron emission tomography, computed tomography, and ultrasound (US) have been developed and applied widely in the field of clinical diagnosis. However, each of the above-mentioned imaging modalities possesses unique strengths and intrinsic weaknesses, which limit their abilities to provide accurate information. Therefore, multimodal imaging systems may be a solution that can provide improved diagnostic performance. Among the current medical imaging modalities, US is a widely available real-time imaging modality. It has many advantages including safety, low cost and easy access for patients. However, its low spatial resolution precludes accurate discrimination of diseased region such as cancer sites. In contrast, MR has no tissue-penetrating limit and can provide images possessing exquisite soft tissue contrast and high spatial resolution. However, it cannot offer real-time images and needs a comparatively long imaging time. The characteristics of these imaging modalities may be considered complementary, and the modalities have been frequently combined for the clinical diagnostic process. Biominerals such as calcium carbonate (CaCO3) and calcium phosphate (CaP) exhibit pH-dependent dissolution behavior. They demonstrate pH-controlled drug release due to the dissolution of minerals in acidic pH conditions. In particular, the application of this mineralization technique to a US contrast agent has been reported recently. The CaCO3 mineral reacts with acids and decomposes to generate calcium dioxide (CO2) gas in an acidic environment. These gas-generating mineralized nanoparticles generated CO2 bubbles in the acidic environment of the tumor, thereby allowing for strong echogenic US imaging of tumor tissues. On the basis of this previous work, it was hypothesized that the loading of MR contrast agents into the CaCO3 mineralized nanoparticles may be a novel strategy in designing a contrast agent for dual imaging. Herein, CaCO3 mineralized nanoparticles that were capable of generating CO2 bubbles to trigger the release of entrapped MR contrast agents in response to tumoral acidic pH were developed for the purposes of US and MR dual-modality imaging of tumors. Gd2O3 nanoparticles were selected as an MR contrast agent. A key strategy employed in this study was to prepare Gd2O3 nanoparticle-loaded mineralized nanoparticles (Gd2O3-MNPs) using block copolymer-templated CaCO3 mineralization in the presence of calcium cations (Ca2+), carbonate anions (CO32-) and positively charged Gd2O3 nanoparticles. The CaCO3 core was considered suitable because it may effectively shield Gd2O3 nanoparticles from water molecules in the blood (pH 7.4) before decomposing to generate CO2 gas, triggering the release of Gd2O3 nanoparticles in tumor tissues (pH 6.4~7.4). The kinetics of CaCO3 dissolution and CO2 generation from the Gd2O3-MNPs were examined as a function of pH and pH-dependent in vitro magnetic relaxation; additionally, the echogenic properties were estimated to demonstrate the potential of the particles for the tumor-specific US and MR imaging.

Keywords: calcium carbonate, mineralization, ultrasound imaging, magnetic resonance imaging

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Nagendra P. Luitel, Mark J. D. Jordans

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Globally, there is a significant gap between the number of individuals in need of mental health care and those who actually receive treatment. The evidence is accumulating that mental health services can be delivered effectively by primary health care workers through community-based programs and task-sharing approaches. Changing the role of specialist mental health workers from service delivery to building clinical capacity of the primary health care (PHC) workers could help in reducing treatment gap in low and middle-income countries (LMICs). We developed a comprehensive mental health care plan in 2012 and evaluated its feasibility and effectiveness over the past three years. Initially, a mixed method formative study was conducted for the development of mental health care plan (MHCP). Routine monitoring and evaluation data, including client flow and reports of satisfaction, were obtained from beneficiaries (n=135) during the pilot-testing phase. Repeated community survey (N=2040); facility detection survey (N=4704) and the cohort study (N=576) were conducted for evaluation of the MHCP. The resulting MHCP consists of twelve packages divided over the community, health facility, and healthcare organization platforms. Detection of mental health problems increased significantly after introducing MHCP. Service implementation data support the real-life applicability of the MHCP, with reasonable treatment uptake. Currently, MHCP has been implemented in the entire Chitwan district where over 1400 people (438 people with depression, 406 people with psychosis, 181 people with epilepsy, 360 people with alcohol use disorder and 51 others) have received mental health services from trained health workers. Key barriers were identified and addressed, namely dissatisfaction with privacy, perceived burden among health workers, high drop-out rates and continue the supply of medicines. The results indicated that involvement of PHC workers in detection and management of mental health problems is an effective strategy to minimize treatment gap on mental health care in Nepal.

Keywords: mental health, Nepal, primary care, treatment gap

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Authors: Tuba Zahra, Aakash Parekh

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Hyperspectral remote sensing refers to imaging of objects or materials in narrow conspicuous spectral bands. Hyperspectral images (HSI) enable the extraction of spectral signatures for objects or materials observed. These images contain information about the reflectance of each pixel across the electromagnetic spectrum. It enables the acquisition of data simultaneously in hundreds of spectral bands with narrow bandwidths and can provide detailed contiguous spectral curves that traditional multispectral sensors cannot offer. The contiguous, narrow bandwidth of hyperspectral data facilitates the detailed surveying of Earth's surface features. This would otherwise not be possible with the relatively coarse bandwidths acquired by other types of imaging sensors. Hyperspectral imaging provides significantly higher spectral and spatial resolution. There are several use cases that represent the commercial applications of hyperspectral remote sensing. Each use case represents just one of the ways that hyperspectral satellite imagery can support operational efficiency in the respective vertical. There are some use cases that are specific to VNIR bands, while others are specific to SWIR bands. This paper discusses the different commercially viable use cases that are significant for HSI application areas, such as agriculture, mining, oil and gas, defense, environment, and climate, to name a few. Theoretically, there is n number of use cases for each of the application areas, but an attempt has been made to streamline the use cases depending upon economic feasibility and commercial viability and present a review of literature from this perspective. Some of the specific use cases with respect to agriculture are crop species (sub variety) detection, soil health mapping, pre-symptomatic crop disease detection, invasive species detection, crop condition optimization, yield estimation, and supply chain monitoring at scale. Similarly, each of the industry verticals has a specific commercially viable use case that is discussed in the paper in detail.

Keywords: agriculture, mining, oil and gas, defense, environment and climate, hyperspectral, VNIR, SWIR

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

Procedia PDF Downloads 157

Authors: Mamyrbek A. Beisenbi, Nurgul M. Kissikova, Saltanat E. Beisembina, Salamat T. Suleimenova, Samal A. Kaliyeva

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The paper proposes a method for constructing a self-organizing control system for unstable and deterministic chaotic processes in the class of catastrophe “hyperbolic umbilic” for objects with m-inputs and n-outputs. The self-organizing control system is investigated by the universal gradient-velocity method of Lyapunov vector functions. The conditions for self-organization of the control system in the class of catastrophes “hyperbolic umbilic” are shown in the form of a system of algebraic inequalities that characterize the aperiodic robust stability in the stationary states of the system.

Keywords: gradient-velocity method of Lyapunov vector-functions, hyperbolic umbilic, self-organizing control system, stability

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Authors: Shuaili Ji

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The correct use of verbs is an important element of high-quality research articles, and thus for Chinese EFL learners, it is significant to master characteristics of verbs and to precisely use verbs. However, some researches have shown that there are differences in using verbs between learners and native speakers and learners have difficulty in using English verbs. This corpus-based quantitative research can enhance learners’ knowledge of English verbs and promote the quality of research article abstracts even of the whole academic writing. The aim of this study is to find the differences between learners’ and native speakers’ use of verbs and to study the factors that contribute to those differences. To this end, the research question is as follows: What are the differences between most frequently used verbs by learners and those by native speakers? The research question is answered through a study that uses corpus-based data-driven approach to analyze the verbs used by learners in their abstract writings in terms of collocation, colligation and semantic prosody. The results show that: (1) EFL learners obviously overused ‘be, can, find, make’ and underused ‘investigate, examine, may’. As to modal verbs, learners obviously overused ‘can’ while underused ‘may’. (2) Learners obviously overused ‘we find + object clauses’ while underused ‘nouns (results, findings, data) + suggest/indicate/reveal + object clauses’ when expressing research results. (3) Learners tended to transfer the collocation, colligation and semantic prosody of shǐ and zuò to make. (4) Learners obviously overused ‘BE+V-ed’ and used BE as the main verb. They also obviously overused the basic forms of BE such as be, is, are, while obviously underused its inflections (was, were). These results manifested learners’ lack of accuracy and idiomatic property in verb usage. Due to the influence of the concept transfer of Chinese, the verbs in learners’ abstracts showed obvious transfer of mother language. In addition, learners have not fully mastered the use of verbs, avoiding using complex colligations to prevent errors. Based on these findings, the present study has implications for English teaching, seeking to have implications for English academic abstract writing in China. Further research could be undertaken to study the use of verbs in the whole dissertation to find out whether the characteristic of the verbs in abstracts can apply in the whole dissertation or not.

Keywords: academic writing abstracts, Chinese EFL learners, corpus-based, data-driven, verbs

Procedia PDF Downloads 323

Authors: A. Bahieldin, A. Atef, S. Edris, N. O. Gadalla, S. M. Hassan, M. A. Al-Kordy, A. M. Ramadan, A. S. M. Al- Hajar, F. M. El-Domyati

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The idea of this work is based on a natural exciting phenomenon suggesting that suppression of genes related to the program cell death (or PCD) mechanism might help the plant cells to efficiently tolerate abiotic stresses. The scope of this work was the detection of PCD-related transcription factors (TFs) that might also be related to salt stress tolerance in plant. Two model plants, e.g., tobacco and Arabidopsis, were utilized in order to investigate this phenomenon. Occurrence of PCD was first proven by Evans blue staining and DNA laddering after tobacco leaf discs were treated with oxalic acid (OA) treatment (20 mM) for 24 h. A number of 31 TFs up regulated after 2 h and co-expressed with genes harboring PCD-related domains were detected via RNA-Seq analysis and annotation. These TFs were knocked down via virus induced gene silencing (VIGS), an RNA interference (RNAi) approach, and tested for their influence on triggering PCD machinery. Then, Arabidopsis SALK knocked out T-DNA insertion mutants in selected TFs analogs to those in tobacco were tested under salt stress (up to 250 mM NaCl) in order to detect the influence of different TFs on conferring salt tolerance in Arabidopsis. Involvement of a number of candidate abiotic-stress related TFs was investigated.

Keywords: VIGS, PCD, RNA-Seq, transcription factors

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Authors: A. I. Alajtal, H. G. M. Edwards, M. A. Elbagermi

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Raman spectroscopy is currently a part of the instrumentation suite of the ESA ExoMars mission for the remote detection of life signatures in the Martian surface and subsurface. Terrestrial analogues of Martian sites have been identified and the biogeological modifications incurred as a result of extremophilic activity have been studied. Analytical instrumentation protocols for the unequivocal detection of biomarkers in suitable geological matrices are critical for future unmanned explorations, including the forthcoming ESA ExoMars mission to search for life on Mars scheduled for 2018 and Raman spectroscopy is currently a part of the Pasteur instrumentation suite of this mission. Here, Raman spectroscopy using 785nm excitation was evaluated for determining various concentrations of beta-carotene in admixture with polyaromatic hydrocarbons and usnic acid have been investigated by Raman microspectrometry to determine the lowest levels detectable in simulation of their potential identification remotely in geobiological conditions in Martian scenarios. Information from this study will be important for the development of a miniaturized Raman instrument for targetting Martian sites where the biosignatures of relict or extant life could remain in the geological record.

Keywords: raman spectroscopy, mars-analog, beta-carotene, PAHs

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Authors: Indu Verma, Santanu Kumar Pal

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Interactions between DNA and adsorbed Poly (L-lysine) (PLL) on liquid crystal (LC) droplets were investigated using polarizing optical microcopy (POM) and epi-fluorescence microscopy. Earlier, we demonstrated that adsorption of PLL to the LC/aqueous interface resulted in homeotropic orientation of the LC and thus exhibited a radial configuration of the LC confined within the droplets. Subsequent adsorption of DNA (single stranded DNA/double stranded DNA) at PLL coated LC droplets was found to trigger a LC reorientation within the droplets leading to pre-radial/bipolar configuration of those droplets. To our surprise, subsequent exposure of complementary ssDNA (c-ssDNA) to ssDNA/ adsorbed PLL modified LC droplets did not cause the LC reorientation. This is likely due to the formation of polyplexes (DNA-PLL complex) as confirmed by fluorescence microscopy and atomic force microscopy. In addition, dsDNA adsorbed PLL droplets have been found to be effectively used to displace (controlled release) propidium iodide (a model drug) encapsulated within dsDNA over time. These observations suggest the potential for a label free droplet based LC detection system that can respond to DNA and may provide a simple method to develop DNA-based drug nano-carriers.

Keywords: DNA biosensor, drug delivery, interfaces, liquid crystal droplets

Procedia PDF Downloads 292

Authors: Mohd Sukri Hassan, Ahlam Inayatullah Badrul Munir, M. Husaini A. Rahman

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Halal authentication and verification in supplement capsules are highly required as the gelatine available in the market can be from halal or non-halal sources. It is an obligation for Muslim to consume and use the halal consumer goods. At present, real-time polymerase chain reaction (RT-PCR) is the most common technique being used for the detection of porcine and bovine DNA in gelatine due to high sensitivity of the technique and higher stability of DNA compared to protein. In this study, twenty samples of supplements capsules from different products with different Halal logos were analyzed for porcine and bovine DNA using RT-PCR. Standard bovine and porcine gelatine from eurofins at a range of concentration from 10^-1 to 10^-5 ng/µl were used to determine the linearity range, limit of detection and specificity on RT-PCR (SYBR Green method). RT-PCR detected porcine (two samples), bovine (four samples) and mixture of porcine and bovine (six samples). The samples were also tested using FT-IR technique where normalized peak of IR spectra were pre-processed using Savitsky Golay method before Principal Components Analysis (PCA) was performed on the database. Scores plot of PCA shows three clusters of samples; bovine, porcine and mixture (bovine and porcine). The RT-PCR and FT-IR with chemometrics technique were found to give same results for porcine gelatine samples which can be used for Halal authentication.

Keywords: halal, real-time PCR, gelatine, chemometrics

Procedia PDF Downloads 228

Authors: F. J. Puertas, M. Castro, A. Tebar, P. J. Fito, R. Gadea, J. M. Monzó, R. J. Colom

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There is a great diversity of diseases that affect the integrity of the walls of the esophagus, generally of a digestive nature. Among them, gastroesophageal reflux is a common disease in the general population, affecting the patient's quality of life; however, there are still unmet diagnostic and therapeutic issues. The consequences of untreated or asymptomatic acid reflux on the esophageal mucosa are not only pain, heartburn, and acid regurgitation but also an increased risk of esophageal cancer. Currently, the diagnostic methods to detect problems in the esophageal tract are invasive and annoying, as 24-hour impedance-pH monitoring forces the patient to be uncomfortable for hours to be able to make a correct diagnosis. In this work, the development of a sensor able to measure in depth is proposed, allowing the detection of liquids circulating in the esophageal tract. The multisensor detection system is based on radiofrequency photospectrometry. At an experimental level, consumers representative of the population in terms of sex and age have been used, placing the sensors between the trachea and the diaphragm analyzing the measurements in vacuum, water, orange juice and saline medium. The results obtained have allowed us to detect the appearance of different liquid media in the esophagus, segregating them based on their ionic content.

Keywords: bioimpedance, dielectric spectroscopy, gastroesophageal reflux, GERD

Procedia PDF Downloads 91

Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

Procedia PDF Downloads 47

Authors: René Vieroth, Christian Dils, M. V. Krshiwoblozki, Christine Kallmayer, Martin Schneider-Ramelow, Klaus-Dieter Lang

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Green urban mobility in commercial and private contexts is one of the great challenges for the continuously growing cities all over the world. Bicycle based solutions are already and since a long time the key to success. Modern developments like e-bikes and high-end cargo-bikes complement the portfolio. Weight, aerodynamic drag, and security for the transported goods are the key factors for working solutions. Recent achievements in the field of smart-textiles allowed the creation of a totally new generation of intelligent textile cargo containers, which fulfill those demands. The fusion of technical textiles, design and electrical engineering made it possible to create an ecological solution which is very near to become a product. This paper shows all the details of this solution that includes an especially developed sensor textile for cut detection, a protective textile layer for intrusion prevention, an universal-charging-unit for energy harvesting from diverse sources and a low-energy alarm system with GSM/GPRS connection, GPS location and RFID interface.

Keywords: cargo-bike, cut-detection, e-bike, energy-harvesting, green urban mobility, logistics, smart-textiles, textile-integrity sensor

Procedia PDF Downloads 307

Authors: Yassine Sadji

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Some architectural conditions required some shapes often lead to an irregular distribution of masses, rigidities, and resistances. The main object of the present study consists in estimating the influence of the irregularity both in plan and in elevation which presenting some structures on the dynamic characteristics and his influence on the behavior of this structures. To do this, it is necessary to make apply both dynamic methods proposed by the RPA99 (spectral modal method and method of analysis by accélérogramme) on certain similar prototypes and to analyze the parameters measuring the answer of these structures and to proceed to a comparison of the results.

Keywords: irregularity, seismic, response, structure, ductility

Procedia PDF Downloads 271

Authors: Mohkammad Nur Cahyadi, Imam Wahyu Farid, Ronny Mardianto, Agung Budi Cahyono, Eko Yuli Handoko, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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Unmanned aerial vehicle (UAV) technology has cost efficiency and data retrieval time advantages. Using technologies such as UAV, GNSS, and LiDAR will later be combined into one of the newest technologies to cover each other's deficiencies. This integration system aims to increase the accuracy of calculating the volume of the land stockpile of PT. Garam (Salt Company). The use of UAV applications to obtain geometric data and capture textures that characterize the structure of objects. This study uses the Taror 650 Iron Man drone with four propellers, which can fly for 15 minutes. LiDAR can classify based on the number of image acquisitions processed in the software, utilizing photogrammetry and structural science principles from Motion point cloud technology. LiDAR can perform data acquisition that enables the creation of point clouds, three-dimensional models, Digital Surface Models, Contours, and orthomosaics with high accuracy. LiDAR has a drawback in the form of coordinate data positions that have local references. Therefore, researchers use GNSS, LiDAR, and drone multi-sensor technology to map the stockpile of salt on open land and warehouses every year, carried out by PT. Garam twice, where the previous process used terrestrial methods and manual calculations with sacks. Research with LiDAR needs to be combined with UAV to overcome data acquisition limitations because it only passes through the right and left sides of the object, mainly when applied to a salt stockpile. The UAV is flown to assist data acquisition with a wide coverage with the help of integration of the 200-gram LiDAR system so that the flying angle taken can be optimal during the flight process. Using LiDAR for low-cost mapping surveys will make it easier for surveyors and academics to obtain pretty accurate data at a more economical price. As a survey tool, LiDAR is included in a tool with a low price, around 999 USD; this device can produce detailed data. Therefore, to minimize the operational costs of using LiDAR, surveyors can use Low-Cost LiDAR, GNSS, and UAV at a price of around 638 USD. The data generated by this sensor is in the form of a visualization of an object shape made in three dimensions. This study aims to combine Low-Cost GPS measurements with Low-Cost LiDAR, which are processed using free user software. GPS Low Cost generates data in the form of position-determining latitude and longitude coordinates. The data generates X, Y, and Z values to help georeferencing process the detected object. This research will also produce LiDAR, which can detect objects, including the height of the entire environment in that location. The results of the data obtained are calibrated with pitch, roll, and yaw to get the vertical height of the existing contours. This study conducted an experimental process on the roof of a building with a radius of approximately 30 meters.

Keywords: LiDAR, unmanned aerial vehicle, low-cost GNSS, contour

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

Procedia PDF Downloads 84

Authors: Mohamed Al-Hazmi, Abdullah Al-Arfaj, Moussa Ihab

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Raw, unpasteurized milk can carry dangerous bacteria such as Salmonella, E. coli, and Listeria, which are responsible for causing numerous foodborne illnesses. The objective of this study was molecular characterization of shiga toxogenic E. coli in raw milk collected from different Egyptian governorates by multiplex PCR. During the period of 25th May to 25th October 2012, a total of 320 bulk-tank milk samples were collected from 10 cow farms located in different Egyptian governorates. Bacteriological examination of milk samples revealed the presence of E. coli organisms in 65 samples (20.3%), serotyping of the E. coli isolates revealed, 35 strains (10.94%) O111, 15 strains (4.69%) O157: H7, 10 strains (3.13%) O128 and 5 strains (1.56%) O119. Multiplex PCR for detection of shiga toxin type 2 and intimin genes revealed positive amplification of 255 bp fragment of shiga toxin type 2 gene and 384 bp fragment of intimin gene from all E. coli serovar O157: H7, while from serovar O111 were 25 (71.43%), 20 (57.14%) and from serovar O128 were 6 (60%), 8 (80%), respectively. The results of multiplex PCR assay are useful for identification of STEC possessing the eaeA and stx2 genes.

Keywords: raw milk, E. coli, multiplex PCR, Shiga toxin type 2, intimin gene

Procedia PDF Downloads 296