Search results for: antibiotics detection

Authors: Kangkang Wu, Kaizhi Wang, Zhijun Yuan

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This paper deals with a mini radar system aimed at detecting small targets at the low latitude. The radar operates at Ku-band in the frequency modulated continuous wave (FMCW) mode with two receiving channels. The radar system has the characteristics of compactness, mobility, and low power consumption. This paper focuses on the implementation of the radar system, and the Block least mean square (Block LMS) algorithm is applied to minimize the fortuitous distortion. It is validated from a series of experiments that the track of the unmanned aerial vehicle (UAV) can be easily distinguished with the radar system.

Keywords: unmanned aerial vehicle (UAV), interference, Block Least Mean Square (Block LMS) Algorithm, Frequency Modulated Continuous Wave (FMCW)

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Authors: Natesan Balasubramanian, Palpandi Pounpandi, Venkatraman Thamil Priya, Vellasamy Shanmugaiah, Karubbiah Balakrishnan, Mandayam Anandam Thirunarayan

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Streptococcus agalactiae is commonly known as Group B Streptococcus (GBS) and it is the most common cause of life-threatening bacterial infection. GBS first considered as a veterinary pathogen causing mastitis in cattle later becomes a human pathogen for severe neonatal infections. In this present study, a total of 20 new clinical isolates of S. agalactiae were collected from male (6) and female patient (14) with different age group. The isolates were from Urinary tract infection (UTI), blood, pus and eye ulcer. All the 20 S. agalactiae isolates has clear hemolysis properties on blood agar medium and were identified by serogrouping and MALTI-TOF-MS analysis. Antibiotic susceptibility/resistance test was performed for 20 S. agalactiae isolates, further phenotypic resistance pattern was observed for tetracycline, vancomycin, ampicillin and penicillin. Genotypically we found two antibiotic resistance genes such as Betalactem antibiotic resistance gene (Tem) (70%) and tetracycline resistance gene Tet(O) 15% in our isolates. Six virulence factors encoding genes were performed by PCR in twenty GBS isolates, cfb gene (100%), followed by, cylE(90.47%), lmp(85.7%), bca(71.42%), rib (38%) and low frequency in bac gene (4.76%) were determined. Most of the S. agalactiae isolates produced strong biofilm in the polystyrene surface (hydrophobic), and low-level biofilm formation was found in glass tube (hydrophilic) surface. lytR is secreted protein and localized in bacterial cell wall, extra cellular membrane, and cytoplasm. In silico docking studies were performed for lytR protein with four antibiofilm compounds, including a peptide (PR39) with the docking study showed peptide has strong interaction followed by ellagic acid and interaction length is 2.95, 2.97 and 2.95 A°. In ligand EGCGO10 and O11 two atoms intract with lytR (Leu271), with binding bond affinity length is 3.24 and 3.14. The aminoacid Leu 271 is act as an impartant aminoacid, since ellagic acid and EGCG interact with same aminoacid.

Keywords: antibiotics, biofilms, clinical isolates, S. agalactiae, virulence

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Authors: Prabhakar Sathujoda

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Vibration analysis of a misaligned rotor coupling bearing system has been carried out while decelerating through its critical speed. The finite element method (FEM) is used to model the rotor system and simulate flexural vibrations. A flexible coupling with a frictionless joint is considered in the present work. The continuous wavelet transform is used to extract the misalignment features from the simulated time response. Subcritical speeds at one-half, one-third, and one-fourth the critical speed have appeared in the wavelet transformed vibration response of a misaligned rotor coupling bearing system. These features are also verified through a parametric study.

Keywords: Continuous Wavelet Transform, Flexible Coupling, Rotor System, Sub Critical Speed

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Authors: Zhala Ahmad, Zainab Lazim, Haider Hamzah

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Bacterial resistance is a pressing global health issue, with multidrug-resistant (MDR), extensively drug-resistant (XDR), and pandrug-resistant (PDR) strains to pose a serious threat. In this context, researchers are investigating effective, safe, and affordable metabolites to combat these pathogens. This study focuses on gum essential oil (GEO) extracted from Pistacia atlantica and its activity and the mechanism of action against XDR Gram-negative Acinetobacter baumannii. GEO was extracted by hydrodistillation and analyzed using GC-MS. Eleven A. baumannii isolates were collected from the ward environment of Burn and Plastic Surgery Hospital in Al Sulaymaniyah City, Iraq. They were identified using the VITEK 2 system, 16S rRNA gene, and confirmed with the blaₒₓₐ₋₅₁ gene; A. baumannii ATCC 19606 was used as a reference strain. The isolates were identified as resistant to twelve different antibiotics spanning six distinct antibiotic classes while showing susceptibility to tetracycline and trimethoprim. Over 40 chemical constituents were detected in the gum's essential oils, with α-pinene being the most abundant. GEO was found to inhibit the growth of A. baumannii isolates; the minimum inhibitory concentration (MIC) of GEO was 2.5 µl/ml. GEO induced protein leakage, phosphate, and potassium ion efflux, distorted cell morphology, and cell death in the tested bacteria. GEO exhibited bacterial clearance and anti-adhesion activity using Band-Aids. This study's findings suggest that GEO could be used as a potential alternative treatment for infectious diseases caused by XRD pathogens, shedding further light on the importance of GEO in biomedical applications. Future studies must focus on generating clinically feasible sources of GEO for testing in small animal models before proceeding to human trials, ensuring safe and effective translation from the laboratory to the clinic.

Keywords: antibiotic resistance, Acinetobacter baumannii, essential oils, Pistacia atlantica, alpha-pinene

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Authors: Desouky Abd El Haleem, Sahar Zaki

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This work was conducted to develop a one-step multiplex PCR system for rapid, sensitive, and specific detection of three different bacterial pathogens, Escherichia coli, Pseudomonas aeruginosa, and Salmonella spp, directly in wastewater without prior isolation on selective media. As a molecular confirmatory test after isolation of the pathogens by classical microbiological methods, PCR-RFLP of their amplified 16S rDNA genes was performed. It was observed that the developed protocols have significance impact in the ability to detect sensitively, rapidly and specifically the three pathogens directly in water within short-time, represents a considerable advancement over more time-consuming and less-sensitive methods for identification and characterization of these kinds of pathogens.

Keywords: multiplex PCR, bacterial pathogens, Escherichia coli, Pseudomonas aeruginosa, Salmonella spp.

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Authors: Harris Niavis, Dimitra Politaki

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The manufacturing industry was collecting vast amounts of data for monitoring product quality thanks to the advances in the ICT sector and dedicated IoT infrastructure is deployed to track and trace the production line. However, industries have not yet managed to unleash the full potential of these data due to defective data collection methods and untrusted data storage and sharing. Blockchain is gaining increasing ground as a key technology enabler for Industry 4.0 and the smart manufacturing domain, as it enables the secure storage and exchange of data between stakeholders. On the other hand, AI techniques are more and more used to detect anomalies in batch and time-series data that enable the identification of unusual behaviors. The proposed scheme is based on smart contracts to enable automation and transparency in the data exchange, coupled with anomaly detection algorithms to enable reliable data ingestion in the system. Before sensor measurements are fed to the blockchain component and the smart contracts, the anomaly detection mechanism uniquely combines artificial intelligence models to effectively detect unusual values such as outliers and extreme deviations in data coming from them. Specifically, Autoregressive integrated moving average, Long short-term memory (LSTM) and Dense-based autoencoders, as well as Generative adversarial networks (GAN) models, are used to detect both point and collective anomalies. Towards the goal of preserving the privacy of industries' information, the smart contracts employ techniques to ensure that only anonymized pointers to the actual data are stored on the ledger while sensitive information remains off-chain. In the same spirit, blockchain technology guarantees the security of the data storage through strong cryptography as well as the integrity of the data through the decentralization of the network and the execution of the smart contracts by the majority of the blockchain network actors. The blockchain component of the Data Traceability Software is based on the Hyperledger Fabric framework, which lays the ground for the deployment of smart contracts and APIs to expose the functionality to the end-users. The results of this work demonstrate that such a system can increase the quality of the end-products and the trustworthiness of the monitoring process in the smart manufacturing domain. The proposed AI-enabled data traceability software can be employed by industries to accurately trace and verify records about quality through the entire production chain and take advantage of the multitude of monitoring records in their databases.

Keywords: blockchain, data quality, industry4.0, product quality

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Authors: Srinivas Bathini, Duraichelvan Raju, Simona Badilescu, Muthukumaran Packirisamy

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A bio-sensing method, based on the plasmonic property of gold nano-islands, has been developed for detection of exosomes in a clinical setting. The position of the gold plasmon band in the UV-Visible spectrum depends on the size and shape of gold nanoparticles as well as on the surrounding environment. By adsorbing various chemical entities, or binding them, the gold plasmon band will shift toward longer wavelengths and the shift is proportional to the concentration. Exosomes transport cargoes of molecules and genetic materials to proximal and distal cells. Presently, the standard method for their isolation and quantification from body fluids is by ultracentrifugation, not a practical method to be implemented in a clinical setting. Thus, a versatile and cutting-edge platform is required to selectively detect and isolate exosomes for further analysis at clinical level. The new sensing protocol, instead of antibodies, makes use of a specially synthesized polypeptide (Vn96), to capture and quantify the exosomes from different media, by binding the heat shock proteins from exosomes. The protocol has been established and optimized by using a glass substrate, in order to facilitate the next stage, namely the transfer of the protocol to a microfluidic environment. After each step of the protocol, the UV-Vis spectrum was recorded and the position of gold Localized Surface Plasmon Resonance (LSPR) band was measured. The sensing process was modelled, taking into account the characteristics of the nano-island structure, prepared by thermal convection and annealing. The optimal molar ratios of the most important chemical entities, involved in the detection of exosomes were calculated as well. Indeed, it was found that the results of the sensing process depend on the two major steps: the molar ratios of streptavidin to biotin-PEG-Vn96 and, the final step, the capture of exosomes by the biotin-PEG-Vn96 complex. The microfluidic device designed for sensing of exosomes consists of a glass substrate, sealed by a PDMS layer that contains the channel and a collecting chamber. In the device, the solutions of linker, cross-linker, etc., are pumped over the gold nano-islands and an Ocean Optics spectrometer is used to measure the position of the Au plasmon band at each step of the sensing. The experiments have shown that the shift of the Au LSPR band is proportional to the concentration of exosomes and, thereby, exosomes can be accurately quantified. An important advantage of the method is the ability to discriminate between exosomes having different origins.

Keywords: exosomes, gold nano-islands, microfluidics, plasmonic biosensing

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Authors: Sharanya C. H., Pragathi M., Vathsala R. S., Theja K. V., Yashaswini S.

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Identifying counterfeit products have become increasingly important in the product manufacturing industries in recent decades. This current ongoing product issue of counterfeiting has an impact on company sales and profits. To address the aforementioned issue, a functional blockchain technology was implemented, which effectively prevents the product from being counterfeited. By utilizing the blockchain technology, consumers are no longer required to rely on third parties to determine the authenticity of the product being purchased. Blockchain is a distributed database that stores data records known as blocks and several databases known as chains across various networks. Counterfeit products are identified using a QR code reader, and the product's QR code is linked to the blockchain management system. It compares the unique code obtained from the customer to the stored unique code to determine whether or not the product is original.

Keywords: blockchain, ethereum, QR code

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Authors: Muhammad Irfan

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The rise of radicalization within Islam has spawned a new era of global terror. The battlefield Successes of ISIS and the Taliban are fuelled by an ideological war waged, largely and successfully, in the media arena. This research will examine how Islamic extremist groups are using media modalities and populist narratives to influence migrant Muslim populations in Europe towards extremism. In 2014, ISIS shocked the world in exporting horrifically graphic forms of violence on social media. Their Muslim support base was largely disgusted and reviled. In response, they reconfigured their narrative by introducing populist 'hooks', astutely portraying the Muslim populous as oppressed and exploited by unjust, corrupt autocratic regimes and Western power structures. Within this crucible of real and perceived oppression, hundreds of thousands of the most desperate, vulnerable and abused migrants left their homelands, risking their lives in the hope of finding peace, justice, and prosperity in Europe. Instead, many encountered social stigmatization, detention and/or discrimination for being illegal migrants, for lacking resources and for simply being Muslim. This research will examine how Islamic extremist groups are exploiting the disenfranchisement of these migrant populations and using populist messaging on social media to influence them towards violent extremism. ISIS, in particular, formulates specific encoded messages for newly-arriving Muslims in Europe, preying upon their vulnerability. Violence is posited, as a populist response, to the tyranny of European oppression. This research will analyze the factors and indicators which propel Muslim migrants along the spectrum from resilience to violence extremism. Expected outcomes are identification of factors which influence vulnerability towards violent extremism; an early-warning detection framework; predictive analysis models; and de-radicalization frameworks. This research will provide valuable tools (practical and policy level) for European governments, security stakeholders, communities, policy-makers, and educators; it is anticipated to contribute to a de-escalation of Islamic extremism globally.

Keywords: populism, radicalization, de-radicalization, social media, ISIS, Taliban, shariah, jihad, Islam, Europe, political communication, terrorism, migrants, refugees, extremism, global terror, predictive analysis, early warning detection, models, strategic communication, populist narratives, Islamic extremism

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Authors: Cristina Fernandez, Felix Amparano, John Tumberger, Stephani Stancil, Sarah Hampl, Brooke Sweeney, Amy R. Beck, Helena H Laroche, Jared Tucker, Eileen Chaves, Sara Gould, Matthew Lindquist, Lora Edwards, Renee Arensberg, Meredith Dreyer, Jazmine Cedeno, Alleen Cummins, Jennifer Lisondra, Katie Cox, Kelsey Dean, Rachel Perera, Nicholas A. Clark

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Background: Adolescents with obesity are at higher risk of disordered eating than the general population. Detection of eating disorders (ED) is difficult. Screening questionnaires may aid in early detection of ED. Our team’s prior efforts focused on increasing ED screening rates to ≥90% using a validated 10-question adolescent binge eating disorder screening questionnaire (ADO-BED). This aim was achieved. We then aimed to improve treatment plan initiation of patients ≥12 years of age who screen positive for BED within our WMC from 33% to 70% within 12 months. Methods: Our WMC is within a tertiary-care, free-standing children’s hospital. A3, an improvement framework, was used. A multidisciplinary team (physicians, nurses, registered dietitians, psychologists, and exercise physiologists) was created. The outcome measure was documentation of treatment plan initiation of those who screen positive (goal 70%). The process measure was ADO-BED screening rate of WMC patients (goal ≥90%). Plan-Do-Study-Act (PDSA) cycle 1 included provider education on current literature and treatment plan initiation based upon ADO-BED responses. PDSA 2 involved increasing documentation of treatment plan and retrain process to providers. Pre-defined treatment plans were: 1) repeat screen in 3-6 months, 2) resources provided only, or 3) comprehensive multidisciplinary weight management team evaluation. Run charts monitored impact over time. Results: Within 9 months, 166 patients were seen in WMC. Process measure showed sustained performance above goal (mean 98%). Outcome measure showed special cause improvement from mean of 33% to 100% (n=31). Of treatment plans provided, 45% received Plan 1, 4% Plan 2, and 46% Plan 3. Conclusion: Through a multidisciplinary improvement team approach, we maintained sustained ADO-BED screening performance, and, prior to our 12-month timeline, achieved our project aim. Our efforts may serve as a model for other multidisciplinary WMCs. Next steps may include expanding project scope to other WM programs.

Keywords: obesity, pediatrics, clinic, eating disorder

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Authors: Shamatha Shetty, Sakshi Dhabadi, Prerana M., Indushree B.

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About 66% of firms claim that insider attacks are more likely to happen. The frequency of insider incidents has increased by 47% in the last two years. The goal of this work is to prevent dangerous employee behavior by using keyloggers and the Machine Learning (ML) model. Every keystroke that the user enters is recorded by the keylogging program, also known as keystroke logging. Keyloggers are used to stop improper use of the system. This enables us to collect all textual data, save it in a CSV file, and analyze it using an ML algorithm and the VirusTotal API. Many large companies use it to methodically monitor how their employees use computers, the internet, and email. We are utilizing the SVM algorithm and the VirusTotal API to improve overall efficiency and accuracy in identifying specific patterns and words to automate and offer the report for improved monitoring.

Keywords: cyber security, machine learning, cyclic process, email notification

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Authors: C. Slim

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Traditional techniques for analyzing time series are based on the notion of stationarity of phenomena under study, but in reality most economic and financial series do not verify this hypothesis, which implies the implementation of specific tools for the detection of such behavior. In this paper, we study nonstationary non-seasonal time series tests in a non-exhaustive manner. We formalize the problem of nonstationary processes with numerical simulations and take stock of their statistical characteristics. The theoretical aspects of some of the most common unit root tests will be discussed. We detail the specification of the tests, showing the advantages and disadvantages of each. The empirical study focuses on the application of these tests to the exchange rate (USD/TND) and the Consumer Price Index (CPI) in Tunisia, in order to compare the Power of these tests with the characteristics of the series.

Keywords: stationarity, unit root tests, economic time series, ADF tests

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Authors: Rade M. Ciric, Sasa Mandic

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Students of Higher Education Technical School of Professional Studies, in Novi Sad follow the subject Maintenance of electric power equipment at the Electrotechnical Department. This paper presents educational plan and program of the subject Maintenance of electric power equipment. The course deals with the problems of preventive and investing maintenance of transformer stations (TS), performing and maintenance of grounding of TS and pillars, as well as tracing and detection the location of the cables failure. There is a special elaborated subject concerning the safe work conditions for the electrician during network maintenance, as well as the basics of making and keeping technical documentation of the equipment.

Keywords: educational plan and program, electric power equipment, maintenance, technical documentation, safe work

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Authors: Ghazi Bouaziz, Abderrahim Derouiche, Damien Brulin, Hélène Pigot, Eric Campo

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Automatic ADL classification is a crucial part of ambient assisted living technologies. It allows to monitor the daily life of the elderly and to detect any changes in their behavior that could be related to health problem. But detection of ADLs is a challenge, especially because each person has his/her own rhythm for performing them. Therefore, we used a correlation matrix to extract custom rules that enable to detect ADLs, including eating activity. Data collected from 3 different individuals between 35 and 105 days allows the extraction of personalized eating patterns. The comparison of the results of the process of eating activity extracted from the correlation matrices with the declarative data collected during the survey shows an accuracy of 90%.

Keywords: elderly monitoring, ADL identification, matrix correlation, meal-taking activity

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Authors: Vinai K. Singh

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In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

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Authors: Nadhiya N. Subramony, Jenifer Vaughan, Lesley E. Scott

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In South Africa, the World Health Organisation estimated 454000 new cases of Mycobacterium tuberculosis (M.tb) infection (MTB) in 2015. Disseminated tuberculosis arises from the haematogenous spread and seeding of the bacilli in extrapulmonary sites. The gold standard for the detection of MTB in bone marrow is TB culture which has an average turnaround time of 6 weeks. Histological examinations of trephine biopsies to diagnose MTB also have a time delay owing mainly to the 5-7 day processing period prior to microscopic examination. Adding to the diagnostic delay is the non-specific nature of granulomatous inflammation which is the hallmark of MTB involvement of the bone marrow. A Ziehl-Neelson stain (which highlights acid-fast bacilli) is therefore mandatory to confirm the diagnosis but can take up to 3 days for processing and evaluation. Owing to this delay in diagnosis, many patients are lost to follow up or remain untreated whilst results are awaited, thus encouraging the spread of undiagnosed TB. The Xpert® MTB/RIF (Cepheid, Sunnyvale, CA) is the molecular test used in the South African national TB program as the initial diagnostic test for pulmonary TB. This study investigates the optimisation and performance of the Xpert® MTB/RIF on bone marrow aspirate specimens (BMA), a first since the introduction of the assay in the diagnosis of extrapulmonary TB. BMA received for immunophenotypic analysis as part of the investigation into disseminated MTB or in the evaluation of cytopenias in immunocompromised patients were used. Processing BMA on the Xpert® MTB/RIF was optimised to ensure bone marrow in EDTA and heparin did not inhibit the PCR reaction. Inactivated M.tb was spiked into the clinical bone marrow specimen and distilled water (as a control). A volume of 500mcl and an incubation time of 15 minutes with sample reagent were investigated as the processing protocol. A total of 135 BMA specimens had sufficient residual volume for Xpert® MTB/RIF testing however 22 specimens (16.3%) were not included in the final statistical analysis as an adequate trephine biopsy and/or TB culture was not available. Xpert® MTB/RIF testing was not affected by BMA material in the presence of heparin or EDTA, but the overall detection of MTB in BMA was low compared to histology and culture. Sensitivity of the Xpert® MTB/RIF compared to both histology and culture was 8.7% (95% confidence interval (CI): 1.07-28.04%) and sensitivity compared to histology only was 11.1% (95% CI: 1.38-34.7%). Specificity of the Xpert® MTB/RIF was 98.9% (95% CI: 93.9-99.7%). Although the Xpert® MTB/RIF generates a faster result than histology and TB culture and is less expensive than culture and drug susceptibility testing, the low sensitivity of the Xpert® MTB/RIF precludes its use for the diagnosis of MTB in bone marrow aspirate specimens and warrants alternative/additional testing to optimise the assay.

Keywords: bone marrow aspirate , extrapulmonary TB, low sensitivity, Xpert® MTB/RIF

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Authors: Nimisha Jadon, Rajeev Jain, Swati Sharma

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A chemically modified carbon paste sensor has been developed for the simultaneous determination of amlodipine (AML) and nebivolol (NBV). Carbon paste electrode (CPE) was fabricated by the addition of Gr/PANI-CeO2. Gr/PANI-CeO2/CPE has achieved excellent electrocatalytic activity and sensitivity. AML and NBV exhibited oxidation peaks at 0.70 and 0.90 V respectively on Gr/ PANI-CeO2/CPE. The linearity range of AML and NBV was 0.1 to 1.6 μgmL-1 in BR buffer (pH 8.0). The Limit of detection (LOD) was 20.0 ngmL-1 for AML and 30.0 ngmL-1 for NBV and limit of quantification (LOQ) was 80.0 ngmL-1 for AML and 100 ngmL-1 for NBV respectively. These analyses were also determined in pharmaceutical formulation and human serum and good recovery was obtained for the developed method.

Keywords: amlodipine, nebivolol, square wave voltammetry, carbon paste electrode, simultaneous quantification

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Authors: Firas Gerges, Frank Y. Shih

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Malignant melanoma, known simply as melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient's death. When detected early, melanoma is curable. In this paper, we propose a deep learning model (convolutional neural networks) in order to automatically classify skin lesion images as malignant or benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: deep learning, skin cancer, image processing, melanoma

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Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

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Authors: Dêivisson Alves Anjos, Blenda Fonseca Aires Teles, Queitiane Castro Costa

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Fire is one of the biggest dangers for factories, warehouses, mills, among other places, causing unimaginable damage, because besides the material damage also directly affects the lives of workers who are likely to suffer death or very serious consequences. This protection of the lives of these people should be taken seriously, always seeking safety. Thus investment in security and monitoring equipment must be high, so you can prevent or reduce the impacts of a possible fire. Our device, made in PIC micro controller monitors the temperature and the presence of gas in the environment, it sends the data via Bluetooth device to a developed in LabVIEW interface saves these data continuously and alert if the temperature exceeds the allowed or some gas is detected. Currently the device is in operation and can perform several tests, as well as use in different areas for which you need anti-fire protection.

Keywords: pic, bluetooth, fire, temperature, gas, LabVIEW

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Authors: Moh Hanif Mubarok, Muhammad Rivai

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The public's response to quality rice is very high. So it is necessary to set minimum standards in checking the quality of rice. Most rice quality measurements still use manual methods, which are prone to errors due to limited human vision and the subjectivity of testers. So, a gas detection system can be a solution that has high effectiveness and subjectivity for solving current problems. The use of gas sensors in testing rice quality must pay attention to several parameters. The parameters measured in this research are the percentage of rice water content, gas concentration, output voltage, and measurement time. Therefore, this research was carried out to identify carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄) gases in rice quality using a series of gas sensors using the Neural Network method.

Keywords: carbon dioxide, dinitrogen oxide, methane, semiconductor gas sensor, neural network

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Authors: Valeria Velasco, Ana M. Bonilla, José L. Vergara, Alcides Lofa, Jorge Campos, Pedro Rojas-García

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Staphylococcus aureus is both commensal bacterium and opportunistic pathogen that can cause different diseases in humans and can rapidly develop antimicrobial resistance. Since this bacterium has the ability to colonize the nares and skin of humans and animals, there is a risk of contamination of food in different steps of the food chain supply. Emerging strains have been detected in food-producing animals and meat, such as methicillin-resistant S. aureus (MRSA). The aim of this study was to determine the prevalence and oxacillin susceptibility of S. aureus in the pork chain supply in Chile and to suggest some natural antimicrobials for control. A total of 487 samples were collected from pigs (n=332), carcasses (n=85), and retail pork meat (n=70). Presumptive S. aureus colonies were isolated by selective enrichment and culture media. The confirmation was carried out by biochemical testing (Api® Staph) and molecular technique PCR (detection of nuc and mecA genes, associated with S. aureus and methicillin resistance, respectively). The oxacillin (β-lactam antibiotic that replaced methicillin) susceptibility was assessed by minimum inhibitory concentration (MIC) using the Epsilometer test (Etest). A preliminary assay was carried out to test thymol, carvacrol, oregano essential oil (Origanum vulgare L.), Maqui or Chilean wineberry extract (Aristotelia chilensis (Mol.) Stuntz) as anti-staphylococcal agents using the disc diffusion method at different concentrations. The overall prevalence of S. aureus in the pork chain supply reached 33.9%. A higher prevalence of S. aureus was determined in carcasses (56.5%) than in pigs (28.3%) and pork meat (32.9%) (P ≤ 0.05). The prevalence of S. aureus in pigs sampled at farms (40.6%) was higher than in pigs sampled at slaughterhouses (23.3%) (P ≤ 0.05). The contamination of no packaged meat with S. aureus (43.1%) was higher than in packaged meat (5.3%) (P ≤ 0.05). The mecA gene was not detected in S. aureus strains isolated in this study. Two S. aureus strains exhibited oxacillin resistance (MIC ≥ 4µg/mL). Anti-staphylococcal activity was detected in solutions of thymol, carvacrol, and oregano essential oil at all concentrations tested. No anti-staphylococcal activity was detected in Maqui extract. Finally, S. aureus is present in the pork chain supply in Chile. Although the mecA gene was not detected, oxacillin resistance was found in S. aureus and could be attributed to another resistance mechanism. Thymol, carvacrol, and oregano essential oil could be used as anti-staphylococcal agents at low concentrations. Research project Fondecyt No. 11140379.

Keywords: antimicrobials, mecA gen, nuc gen, oxacillin susceptibility, pork meat

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Authors: Li Ziming, He Cunfu, Liu Zenghua

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With aim for delamination type defects detection in manufacturing process of seamless pipe,this paper studies the interaction of the circumferential lamb wave with delamination in aluminum pipe.The delamination is located in the middle of pipe wall.A numerical study is carried out,the circumferential lamb wave used here is CL0 mode,which is generated with a finite element method code.Wave structures from the simulation are compared with theoretical results to verify the model’s accuracy.Delamination along the circumferential direction is established by demerging nodes of the same coordinates.When CL0 mode is incident at the entrance and exit of a delamination,it generates new mode-CL1,undergoes multiple reverberation and mode conversions between the two ends of the delamination. Signals of different receptions are obtained to provide insight in using CL0 mode for locating the delamination.

Keywords: circumferential lamb wave, delamination, FEM, seamless pipe

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Authors: B. Kriheli, E. Levner, T. C. E. Cheng, C. T. Ng

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During the last decades, research interest in planning, scheduling, and control of emergency response operations, especially people rescue and evacuation from the dangerous zone of marine accidents, has increased dramatically. Until the survivors (called ‘targets’) are found and saved, it may cause loss or damage whose extent depends on the location of the targets and the search duration. The problem is to efficiently search for and detect/rescue the targets as soon as possible with the help of intelligent mobile robots so as to maximize the number of saved people and/or minimize the search cost under restrictions on the amount of saved people within the allowable response time. We consider a special situation when the autonomous mobile robots (AMR), e.g., unmanned aerial vehicles and remote-controlled robo-ships have no operator on board as they are guided and completely controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robots during the search-and rescue missions after an accident at sea. We presume that in the unknown environments, the AMR’s search-and-rescue activity is subject to two types of error: (i) a 'false-negative' detection error where a target object is not discovered (‘overlooked') by the AMR’s sensors in spite that the AMR is in a close neighborhood of the latter and (ii) a 'false-positive' detection error, also known as ‘a false alarm’, in which a clean place or area is wrongly classified by the AMR’s sensors as a correct target. As the general resource-constrained discrete search problem is NP-hard, we restrict our study to finding local-optimal strategies. A specificity of the considered operational research problem in comparison with the traditional Kadane-De Groot-Stone search models is that in our model the probability of the successful search outcome depends not only on cost/time/probability parameters assigned to each individual location but, as well, on parameters characterizing the entire history of (unsuccessful) search before selecting any next location. We provide a fast approximation algorithm for finding the AMR route adopting a greedy search strategy in which, in each step, the on-board computer computes a current search effectiveness value for each location in the zone and sequentially searches for a location with the highest search effectiveness value. Extensive experiments with random and real-life data provide strong evidence in favor of the suggested operations research model and corresponding algorithm.

Keywords: disaster management, intelligent robots, scheduling algorithm, search-and-rescue at sea

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Authors: Alaa Al Hawari, Mohammad Khader, Tarek Zayed, Osama Moselhi

Abstract:

In this paper, an effective non-destructive, non-invasive approach for leak detection was proposed. The process relies on analyzing thermal images collected by an IR viewer device that captures thermo-grams. In this study a statistical analysis of the collected thermal images of the ground surface along the expected leak location followed by a visual inspection of the thermo-grams was performed in order to locate the leak. In order to verify the applicability of the proposed approach the predicted leak location from the developed approach was compared with the real leak location. The results showed that the expected leak location was successfully identified with an accuracy of more than 95%.

Keywords: thermography, leakage, water pipelines, thermograms

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Authors: Maha Al-Soudani, Gilles Klysz, Jean-Paul Balayssac

Abstract:

The aim of this paper is to use Ground Penetrating Radar (GPR) as a non-destructive testing (NDT) method to increase its accuracy in recognizing the geometric reinforced concrete structures and in particular, the position of steel bars. This definition will help the managers to assess the state of their structures on the one hand vis-a-vis security constraints and secondly to quantify the need for maintenance and repair. Several configurations of acquisition and processing of the simulated signal were tested to propose and develop an appropriate imaging algorithm in the propagation medium to locate accurately the rebar. A subsequent experimental validation was used by testing the imaging algorithm on real reinforced concrete structures. The results indicate that, this algorithm is capable of estimating the reinforcing steel bar position to within (0-1) mm.

Keywords: GPR, NDT, Reinforced concrete structures, Rebar location.

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Authors: Boukerch Haroun, Luo Qing Sheng, Li Hua Shi, Boukraa Sebti

Abstract:

In this paper we present the design and the implementation of a target tracking system where the target is set to be a moving person in a video sequence. The system can be applied easily as a vision system for mobile robot. The system is composed of two major parts the first is the detection of the person in the video frame using the SVM learning machine based on the “HOG” descriptors. The second part is the tracking of a moving person it’s done by using a combination of the Kalman filter and a modified version of the Camshift tracking algorithm by adding the target motion feature to the color feature, the experimental results had shown that the new algorithm had overcame the traditional Camshift algorithm in robustness and in case of occlusion.

Keywords: camshift algorithm, computer vision, Kalman filter, object tracking

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Authors: B. Grygalewicz, R. Woroniecka, J. Rygier, K. Borkowska, A. Labak, B. Nowakowska, B. Pienkowska-Grela

Abstract:

Introduction: Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world. Hemizygous and or homozygous loss at 13q14 occur in more than half of cases and constitute the most frequent chromosomal abnormality in CLL. It is believed that deletions 13q14 play a role in CLL pathogenesis. Two microRNA genes miR-15a and miR- 16-1 are targets of 13q14 deletions and plays a tumor suppressor role by targeting antiapoptotic BCL2 gene. Deletion size, as a single change detected in FISH analysis, has haprognostic significance. Patients with small deletions, without RB1 gene involvement, have the best prognosis and the longest overall survival time (OS 133 months). In patients with bigger deletion region, containing RB1 gene, prognosis drops to intermediate, like in patients with normal karyotype and without changes in FISH with overall survival 111 months. Aim: Precise delineation of 13q14 deletions regions in two groups of CLL patients, with mono- and biallelic deletions and qualifications of their prognostic significance. Methods: Detection of 13q14 deletions was performed by FISH analysis with CLL probe panel (D13S319, LAMP1, TP53, ATM, CEP-12). Accurate deletion size detection was performed by CGH Haematological Cancer and SNP array (8x60K). Results: Our investigated group of CLL patients with the 13q14 deletion, detected by FISH analysis, comprised two groups: 18 patients with monoallelic deletions and 18 patients with biallelic deletions. In FISH analysis, in the monoallelic group the range of cells with deletion, was 43% to 97%, while in biallelic group deletion was detected in 11% to 94% of cells. Microarray analysis revealed precise deletion regions. In the monoallelic group, the range of size was 348,12 Kb to 34,82 Mb, with median deletion size 7,93 Mb. In biallelic group discrepancy of total deletions, size was 135,27 Kb to 33,33 Mb, with median deletion size 2,52 Mb. The median size of smaller deletion regions on one copy chromosome 13 was 1,08 Mb while the average region of bigger deletion on the second chromosome 13 was 4,04 Mb. In the monoallelic group, in 8/18 deletion region covered RB1 gene. In the biallelic group, in 4/18 cases, revealed deletion on one copy of biallelic deletion and in 2/18 showed deletion of RB1 gene on both deleted 13q14 regions. All minimal deleted regions included miR-15a and miR-16-1 genes. Genetic results will be correlated with clinical data. Conclusions: Application of CGH microarrays technique in CLL allows accurately delineate the size of 13q14 deletion regions, what have a prognostic value. All deleted regions included miR15a and miR-16-1, what confirms the essential role of these genes in CLL pathogenesis. In our investigated groups of CLL patients with mono- and biallelic 13q14 deletions, patients with biallelic deletion presented smaller deletion sizes (2,52 Mb vs 7,93 Mb), what is connected with better prognosis.

Keywords: CLL, deletion 13q14, CGH microarrays, SNP array

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Authors: Caspar S. Carson, Gabriel W. Prather, Nicholas E. Wong, Jeffery R. Anton, William H. McCoy

Abstract:

Cutibacterium acnes is a commensal bacterium found on human skin that has been linked to acne. C. acnes can also be an opportunistic pathogen when it infiltrates the body during surgery. This pathogen can cause dangerous infections of medical implants, such as shoulder replacements, leading to life-threatening blood infections. Compounding this issue, C. acnes resistance to many antibiotics has become an increasing problem worldwide, creating a need for special forms of treatment. C. acnes expresses the protein RoxP, and it requires this protein to colonize human skin. Though this protein is required for C. acnes skin colonization, its function is not yet understood. Inhibition of RoxP function might be an effective treatment for C. acnes infections. To develop such reagents, the McCoy Laboratory generated four unique anti-RoxP antibodies. Preliminary studies in the McCoy Lab have established that each antibody binds a distinct site on RoxP. To assess the potential of these antibodies as therapeutics, it is necessary to specifically characterize these antibody epitopes and evaluate them in assays that assess their ability to inhibit RoxP-dependent C. acnes growth. To provide material for these studies, an antibody expression construct, Fv-clasp(v2), was adapted to encode anti-RoxP antibody sequences. The author hypothesizes that this expression strategy can produce sufficient amounts of >95% pure antibody fragments for further characterization of these antibodies. Four anti-RoxP Fv-clasp(v2) expression constructs (pET vector-based) were transformed into E. coli BL21-Gold(DE3) cells and a small-scale expression and purification trial was performed for each construct to evaluate anti-RoxP Fv-clasp(v2) yield and purity. Successful expression and purification of these antibody constructs will allow for their use in structural studies, such as protein crystallography and cryogenic electron microscopy. Such studies would help to define the antibody binding sites on RoxP, which could then be leveraged in the development of certain methods to treat C. acnes infection through RoxP inhibition.

Keywords: structural biology, protein expression, infectious disease, antibody, therapeutics, E. coli

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Authors: Benjamin Osei-Tutu, Henrietta Awewole Kolson

Abstract:

Background: Acute gastroenteritis is one of the frequently reported Out-Patient Department (OPD) cases. However, the causative pathogens of these cases are rarely identified at the OPD due to delay in laboratory results or failure to obtain specimens before antibiotics is administered. Method: A retrospective review of surveillance data from the Adentan Municipality, Accra, Ghana that were recorded in the National foodborne disease surveillance system of Ghana, was conducted with the main aim of describing the epidemiology and food practice of cases reported from the Adentan Municipality. The study involved a retrospective review of surveillance data kept on patients who visited health facilities that are involved in foodborne disease surveillance in Ghana, from January 2015 to December 2016. Results: A total of 375 cases were reviewed and these were classified as viral hepatitis (hepatitis A and E), cholera (Vibrio cholerae), dysentery (Shigella sp.), typhoid fever (Salmonella sp.) or gastroenteritis. Cases recorded were all suspected case and the average cases recorded per week was 3. Typhoid fever and dysentery were the two main clinically diagnosed foodborne illnesses. The highest number of cases were observed during the late dry season (Feb to April), which marks the end of the dry season and the beginning of the rainy season. Relatively high number of cases was also observed during the late wet seasons (Jul to Oct) when the rainfall is the heaviest. Home-made food and street vended food were the major sources of suspected etiological food, recording 49.01% and 34.87% of the cases respectively. Conclusion: Majority of cases recorded were classified as gastroenteritis due to the absence of laboratory confirmation. Few cases were classified as typhoid fever and dysentery based on clinical symptoms presented. Patients reporting with foodborne diseases were found to consume home meal and street vended foods as their predominant source of food.

Keywords: accra, etiologic food, food poisoning, gastroenteritis, illness, surveillance

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