Search results for: minimal residual disease

Authors: W. J. Kim

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The behaviors of the recovery stress and strain of an ultrafine-grained Ni-50.2 at.% Ti alloy prepared by high-ratio differential speed rolling (HRDSR) were examined by a specially designed tensile-testing set up, and the factors that influence the recovery stress and strain were studied. After HRDSR, both the recovery stress and strain were enhanced compared to the initial condition. The constitutive equation showing that the maximum recovery stress is a sole function of the recovery strain was developed based on the experimental data. The recovery strain increased as the yield stress increased. The maximum recovery stress increased with an increase in yield stress. The residual recovery stress was affected by the yield stress as well as the austenite-to-martensite transformation temperature. As the yield stress increased and as the martensitic transformation temperature decreased, the residual recovery stress increased.

Keywords: high-ratio differential speed rolling, tensile testing, severe plastic deformation, shape memory alloys

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Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: convolutional neural network, lithology, prediction of reservoir, seismic attributes

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Manish Subedi, Bijay Bartaula, Ashok R. Pant, Purbesh Adhikari, Sanjib K. Sharma

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Background: The pattern of glomerular disease varies worldwide. In absence of kidney disease/Kidney biopsy registry in Nepal, the exact etiology of different forms of glomerular disease is primarily unknown in our country. Method: We retrospectively analyzed 175 cases of renal biopsies performed from dated September 2014 to August 2016 at B. P. Koirala Institute of Health Sciences, Dharan, Nepal. Results: The commonest indication for renal biopsy was nephrotic syndrome (34.9%), followed by Systemic lupus erythematosus with suspected renal involvement (22.3%). Majority of patients were in the 30-60 year bracket (57.2%), with the mean age of the patients being 35.37 years. The average number of glomeruli per core was 13, with inadequate sampling in 5.1%. IgA nephropathy (17%) was found to be the most common primary glomerular disease, followed by membranous nephropathy (14.6%) and FSGS (14.6%). The commonest secondary glomerular disease was lupus nephritis. Complications associated with renal biopsy were pain at biopsy site in 18% of cases, hematuria in 6% and perinephric hematoma in 4% cases. Conclusion: The commonest primary and secondary glomerular disease was IgA nephropathy and lupus nephritis respectively. The high prevalence of Systemic lupus erythematosus with lupus nephritis among Nepalese in comparison with other developing countries warrants further evaluation. As an initial attempt towards documentation of glomerular diseases in the national context, this study should serve as a stepping stone towards the eventual establishment of a full-fledged national registry of glomerular diseases in Nepal.

Keywords: glomerular, Nepal, renal biopsy, systemic lupus erythematoses

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Authors: Anjela Koblischka-Veneva, Michael R. Koblischka

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To increase the flux pinning performance of bulk YBa2Cu3O7-δ (YBCO or Y-123) superconductors, it is common to employ secondary phase particles, either Y2BaCuO5 (Y-211) particles created during the growth of the samples or additionally added (nano)particles of various types, embedded in the superconducting Y-123 matrix. As the crystallographic parameters of all the particles indicate a misfit to Y-123, there will be residual strain within the Y-123 matrix around such particles. With a dedicated analysis of electron backscatter diffraction (EBSD) data obtained on various bulk, Y-123 superconductor samples, the strain distribution around such embedded secondary phase particles can be revealed. The results obtained are presented in form of Kernel Average Misorientation (KAM) mappings. Around large Y-211 particles, the strain can be so large that YBCO subgrains are formed. Therefore, it is essential to properly control the particle size as well as their distribution within the bulk sample to obtain the best performance. The impact of the strain distribution on the flux pinning properties is discussed.

Keywords: Bulk superconductors, EBSD, Strain, YBa2Cu3Oy

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Authors: R. Mickiene, A. Friese, U. Rosler, A. Maruska, O. Ragazinskiene

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This research focuses on phytochemistry and antimicrobial activities of compounds isolated and identified from three species of Eryngium. The antimicrobial activity of extracts from Eryngiumplanum L., Eryngium maritimum L., Eryngium campestre L. grown in Lithuania, were tested by the method of series dilutions, against different bacteria species: Escherichia coli, Proteus vulgaris and Staphylococcus aureus with and without antibiotic resistances, originating from livestock. The antimicrobial activity of extracts was described by determination of the minimal inhibitory concentration. Preliminary results show that the minimal inhibitory concentration range between 8.0 % and 17.0 % for the different Eryngium extracts and bacterial species.The total amounts ofphenolic compounds and total amounts of flavonoids were tested in the methanolic extracts of the plants. Identification and evaluation of the phenolic compounds were performed by liquid chromatography. The essential oils were analyzed by gas chromatography mass spectrometry.

Keywords: antimicrobial activities, Eryngium L. species, essential oils, gas chromatography mass spectrometry

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: R. Harihara Prakash, Shweta R. Parikh, Sangna S. Sheth

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The purpose of this study was to explore the usefulness of the Mini-BESTest compared to the Berg Balance Scale in evaluating balance in people with Parkinson's Disease (PD) of varying severity. Evaluation were done to obtain (1) the distribution of patients scores to look for ceiling effects, (2) concurrent validity with severity of disease, and (3) the sensitivity & specificity of separating people with or without postural response deficits. Methods and Material: Seventy-seven(77) people with Parkinson's Disease were tested for balance deficits using the Berg Balance Scale, Mini-BESTest. Unified Parkinson’s Disease Rating Scale (UPDRS) III and the Hoehn & Yahr (H&Y) disease severity scales were used for classification. Materials used in this study were case record sheet, chair without arm rests or wheels, Incline ramp, stopwatch, a box, 3 meter distance measured out and marked on the floor with tape [from chair]. Statistical analysis used: Multiple Linear regression was carried out of UPDRS jointly on the two scores for the Berg and Mini-BESTest. Receiver operating characteristic curves for classifying people into two groups based on a threshold for the H&Y score, to discriminate between mild PD versus more severe PD.Correlation co-efficient to find relativeness between the two variables. Results: The Mini-BESTest is highly correlated with the Berg (r = 0.732,P < 0.001), but avoids the ceiling compression effect of the Berg for mild PD (skewness −0.714 Berg, −0.512 Mini-BESTest). Consequently, the Mini-BESTest is more effective than the Berg for predicting UPDRS Motor score (P < 0.001 Mini-BESTest versus P = 0.72 Berg), and for discriminating between those with and without postural response deficits as measured by the H&Y (ROC).

Keywords: balance, berg balance scale, MINI BESTest, parkinson's disease

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Authors: Riad Benelmir, Muhammad Shoaib Ahmed Khan

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The socio-economic importance of the olive oil production is significant in the Mediterranean region, both in terms of wealth and tradition. However, the extraction of olive oil generates huge quantities of wastes that may have a great impact on land and water environment because of their high phytotoxicity. Especially olive mill wastewater (OMWW) is one of the major environmental pollutants in olive oil industry. This work projects to design a smart and sustainable integrated thermochemical catalytic processes of residues from olive mills by hydrothermal carbonization (HTC) of olive mill wastewater (OMWW) and fast pyrolysis of olive mill wastewater sludge (OMWS). The byproducts resulting from OMWW-HTC treatment are a solid phase enriched in carbon, called biochar and a liquid phase (residual water with less dissolved organic and phenolic compounds). HTC biochar can be tested as a fuel in combustion systems and will also be utilized in high-value applications, such as soil bio-fertilizer and as catalyst or/and catalyst support. The HTC residual water is characterized, treated and used in soil irrigation since the organic and the toxic compounds will be reduced under the permitted limits. This project’s concept includes also the conversion of OMWS to a green diesel through a catalytic pyrolysis process. The green diesel is then used as biofuel in an internal combustion engine (IC-Engine) for automotive application to be used for clean transportation. In this work, a theoretical study is considered for the use of heat from the pyrolysis non-condensable gases in a sorption-refrigeration machine for pyrolysis gases cooling and condensation of bio-oil vapors.

Keywords: biomass, olive oil extraction, adsorption cooling, pyrolisis

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Authors: Wen-Jay Lee, Kuo-Ning Chiang

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The graphene binding with silicon substrate has apparently Schottky barriers property, which can be used in the application of solar cell and light source. Because graphene has only one atom layer, the atomistic structure of graphene binding with the silicon surface plays an important role to affect the properties of graphene. In this work, temperature effect on the morphology of graphene sheet attached on different crystal planes of silicon substrates are investigated by Molecular dynamics (MD) (LAMMPS, developed by Sandia National Laboratories). The results show that the covered graphene sheet would cause the structural deformation of the surface Si atoms of stubtrate. To achieve a stable state in the binding process, the surface Si atoms would adjust their position and fit the honeycomb structure of graphene after the graphene attaches to the Si surface. The height contour of graphene on different plane of silicon surfaces presents different pattern, leading the local residual stress at the interface. Due to the high density of dangling bond on the Si (111)7x7 surface, the surface of Si(111)7x7 is not matching with the graphene so well in contrast with Si(100)2x1and Si(111)2x1. Si(111)7x7 is found that only partial silicon adatoms are rearranged on surface after the attachment when the temperature is lower than 200K, As the temperature gradually increases, the deformation of surface structure becomes significant, as well as the residue stress. With increasing temperature till the 815K, the graphene sheet begins to destroy and mixes with the silicon atoms. For the Si(100)2x1 and Si(111)2x1, the silicon surface structure keep its structural arrangement with a higher temperature. With increasing temperature, the residual stress gradually decrease till a critical temperatures. When the temperature is higher than the critical temperature, the residual stress gradually increases and the structural deformation is found on the surface of the Si substrates.

Keywords: molecular dynamics, graphene, silicon, Schottky barriers, interface

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Authors: Nirupama Dixit, Anyaa Mittal, Neeru Sood

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Alzheimer’s disease (AD) is a progressive form of dementia, contributing to up to 70% of cases, mostly observed in elderly but is not restricted to old age. The pathophysiology of the disease is characterized by specific pathological changes in brain. The changes (i.e. accumulation of metal ions in brain, formation of extracellular β-amyloid (Aβ) peptide aggregates and tangle of hyper phosphorylated Tau protein inside neurons) damage the neuronal connections irreversibly. The current issues in improvement of life quality of Alzheimer's patient lies in the fact that the diagnosis is made at a late stage of the disease and the medications do not treat the basic causes of Alzheimer's. The targeted delivery of drug through the blood brain barrier (BBB) poses several limitations via traditional approaches for treatment. To overcome these drug delivery limitation, nanoparticles provide a promising solution. This review focuses on current strategies for efficient targeted drug delivery using nanoparticles and improving the quality of therapy provided to the patient. Nanoparticles can be used to encapsulate drug (which is generally hydrophobic) to ensure its passage to brain; they can be conjugated to metal ion chelators to reduce the metal load in neural tissue thus lowering the harmful effects of oxidative damage; can be conjugated with drug and monoclonal antibodies against BBB endogenous receptors. Finally this review covers how the nanoparticles can play a role in diagnosing the disease.

Keywords: Alzheimer's disease, β-amyloid plaques, blood brain barrier, metal chelators, nanoparticles

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Authors: Somorjeet Sharma Shamurailatpam, Rabindra Das, A. Suchitra Devi

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Computer and video display users might complain about Asthenopia, burning, dry eyes etc. The management of dry eyes is often not in the lines of severity. Following systematic evaluation and grading, dry eye disease is one condition that can be practiced at all levels of ophthalmic care. In the present study, different spectrum causing dry eye and prevalence of dry eye disease in computer users of Manipur, India are determined with 600 individuals (300 cases and 300 control). Individuals between 15 and 50 years who used computers for more than 3 hrs a day for 1 year or more were included. Tear break up time (TBUT) and Schirmer’s test were conducted. It shows that 33 (20.4%) out of 164 males and 47 (30.3%) out of 136 females have dry eye. Possible explanation for the observed result is discussed.

Keywords: asthenopia, computer vision syndrome, dry eyes, Schirmer's test, TBUT

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Authors: Chaoyi Sun, Congxin Chen, Yun Zheng, Kaizong Xia, Wei Zhang

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Crest loads are often encountered in hydropower, highway, open-pit and other engineering rock slopes. Toppling failure is one of the most common deformation failure types of anti-dip bedding rock slopes. Analysis on such failure of anti-dip bedding rock slopes subjected to crest loads has an important influence on engineering practice. Based on the step-by-step analysis approach proposed by Goodman and Bray, a geo-mechanical model was developed, and the related analysis approach was proposed for the toppling failure of anti-dip bedding rock slopes subjected to crest loads. Using the transfer coefficient method, a formulation was derived for calculating the residual thrust of slope toe and the support force required to meet the requirements of the slope stability under crest loads, which provided a scientific reference to design and support for such slopes. Through slope examples, the influence of crest loads on the residual thrust and sliding ratio coefficient was investigated for cases of different block widths and slope cut angles. The results show that there exists a critical block width for such slope. The influence of crest loads on the residual thrust is non-negligible when the block thickness is smaller than the critical value. Moreover, the influence of crest loads on the slope stability increases with the slope cut angle and the sliding ratio coefficient of anti-dip bedding rock slopes increases with the crest loads. Finally, the theoretical solutions and numerical simulations using Universal Distinct Element Code (UDEC) were compared, in which the consistent results show the applicability of both approaches.

Keywords: anti-dip bedding rock slope, crest loads, stability analysis, toppling failure

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Fatemeh Keshavarz

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Background: Rheumatoid arthritis (RA) is one of the most common autoimmune diseases, often leading to joint damage and physical disability. This study aimed to investigate the relationship of serum levels of interleukin 17 and anti-CCP factor with disease severity in RA patients. Materials and Methods: Fifty-four patients with RA confirmed by clinical and laboratory criteria were recruited. A 5 ml venous blood sample was taken from every patient, its serum was separated. Based on clinical data and severity of symptoms, patients were classified into three groups of those with mild, moderate, and severe symptoms. Serum levels of IL-17 and anti-CCP in all samples were measured using ELISA. Results: Analysis of IL-17 serum levels in different groups showed that its amount was higher in the group with mild clinical symptoms than in other groups. Comparison of IL-17 serum levels between mild and moderate disease severity groups showed a statistically significant relationship. There was also a positive linear relationship between anti-CCP and serum IL-17 levels in different groups of the disease, and serum IL-17 levels were inversely related to the duration of exposure to the disease. Conclusion: Higher IL-17 serum levels in patients with mild symptom severity confirm that this highly specific marker is involved in the pathogenesis of RA and may be effective in initiating patients’ clinical symptoms.

Keywords: IL-17, anti-CCP, rheumatoid arthritis, autoimmune

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Authors: Teja Koduru

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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

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Authors: Zahra Rakhshan Masoudi, Sona Rostampour Yasouri

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Considering that the only way to save the lives of patients and healthy people who have suffered sudden accidents is blood transfusion, what is important is the presence of the known HCV virus as the most important cause of the disease after blood transfusion. HCV is one of the major global problems, and its transmission through blood causes life-threatening complications and extensive legal, social and economic consequences. On the one hand, unfortunately, there is still no effective vaccine available to prevent HCV. In Iran, the exact statistics of the prevalence of this disease have not yet been fully announced. The main purpose of this study is to investigate the prevalence rate and rapid diagnosis of HCV among those who refer to laboratory centers in Tehran. From spring to winter of 1401 (2022-2023), 2166 blood samples were collected from laboratory centers in Tehran. Blood samples were evaluated for the presence of HCV by Electrochemiluminescence (ECL) and PCR techniques along with specific HCV primers. In general, 36 samples (1.6%) were tested positive by the mentioned techniques. The results indicated that the ECL technique is a sensitive and specific diagnostic method for detecting HCV in the early stages of the disease and can be very helpful and provide the possibility of starting the treatment steps to prevent the exacerbation of the disease earlier. Also, the results of PCR technique showed that PCR is an accurate, sensitive and fast method for definitive diagnosis of HCV. It seems that the incidence rate of this disease is increasing in Iran, and investigating the spread of the disease throughout Iran for a longer period of time in the continuation of our research can be helpful in the future to take the necessary measures to prevent the transmission of the disease to people and the rapid onset Treatment steps for patients with HCV should be carried out.

Keywords: electrochemiluminescence, HCV, PCR, prevalence

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Authors: Ritu Nambiar, Shazia Tariq, Sumaira Jamil, Farida Munawar, Imelda Israell

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GBS has emerged as a leading cause of neonatal infections worldwide and clinical trials have demonstrated that giving IAP was effective in reducing early onset GBS (EOGBS) disease of the newborn. There is no available data on the prevalence of GBS in the UAE, therefore, a retrospective chart analysis of our parturients were done to look at our prevalence. The aim of this study is: 1. To study the prevalence of GBS colonization of parturients at al Rahba Hospital following universal screening between 35-37 week. 2. To look at efficacy of GBS intrapartum antibiotic prophylaxis by NICU admission for EO GBS disease of the newborn. 1) The prevalence of GBS in our patient population is 24.15%. 2) Incidence of EO GBS disease of the newborn was 0.6%.

Keywords: GBS Screening, universal intrapartum antibiotic prophylaxis, parturients, newborn

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Authors: Jade Edey, Andrew Bennett, Gareth Hathway

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Nuclear receptor-related 1 protein (also known as Nurr1 or NR4A2) is an orphan nuclear receptor which plays a vital role in the development, survival and maintenance of dopaminergic (DA) neurons particularly in the substantia nigra (SN). Increasing research has investigated Nurr1’s additional role within microglia and astrocytes where it has been suggested to act as a negative regulator of inflammation; potentially offering neuroprotection. Considering both DA neurodegeneration and neuroinflammation are commonly accepted constituents of Parkinson’s Disease (PD), understanding the mechanisms by which Nurr1 regulates inflammatory processes could provide an attractive therapeutic target. Nurr1 regulates inflammation via a transrepressive mechanism possibly dependent upon SUMOylation. In addition, Nurr1 can transactivate numerous genes involved in DA synthesis, such as Tyrosine Hydroxylase (TH). A C-terminal splice variant of Nurr1, Nurr-1a, has been reported in both neuronal and glial cells. However, research into its transcriptional activity is minimal. We employed in vitro methods such as SUMO-Pulldown experiments alongside Luciferase reporter assays to investigate the SUMOylation status and transactivation capabilities of Nurr1 and Nurr-1a respectively. The SUMO-Pulldown assay demonstrated Nurr-1a undergoes significantly more SUMO modification than its full-length variant. Consequently, despite having less transcriptional activation than Nurr1, Nurr1a may play a more prominent role in repression of microglial inflammation. Contrary to published literature we also identified that SUMOylation enhances transcriptional activation by Nurr1 and Nurr1a. SUMOylation-dependent increases in Nurr1 and Nurr1a transcriptional activation were only evident in neuronal SHSY5Y cells but not in HEK293 cells. This research provides novel insight into the regulation of Nurr-1a and indicates differential effects of SUMOylation dependent regulation in neuronal and inflammatory cells.

Keywords: nuclear receptors, Parkinson’s disease, inflammation, transcriptional regulation

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Authors: Heiko Scholz

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When feeding high qualitysilagetoheifersfromthe age of two, there is a riskofenergyoversupply. Depending on the feeding valueorscarceavailability ofsilageorcorn silage diets withhighproportionsof straw is often incorporated. Foran energetically standardized young cattle supply of strawproportion can be more than 20% of dry matter. It was investigated whether the grinding of straw with the strawmillselective feeding significantly limits. The investigation has been carried out with young cattle in the second year. 78 animals were kept and fed under similar conditions in two groups. The experimental group (EG) consisted of cattle 12 to 15 months, and in the control group (CG), the cattle were 15 to 20 months old. The experimental feeding took place in five days of feed distribution, and residual feed were weighed. The ration of EG contained ground with the straw mill straw, and CG was further fed rotor-cut pressed straw. To determine the selective seizure samples of feed distributionandtheremainingfood with the particle separator boxandthecrude protein-and energy-content have been determined. The grinding of the straw increased the daily feed intake.IntheEGan increase infeed intakewas observedby grinding of the straw. Feed intakedirectlyon the day for changing the dietoflongonground straw increased by more than 2.0 kgofDMper animal. In the following days, the feed intakewasincreasedby 0.9kg DMper animal and day on average (7.4 vs. 8.3 kg DM per day). The results of the screen distribution of residual feed point to a differentiated feeding behavior between the groups. In the EG, the particle length of the residual feed to a large extent with the template matches. The acid-base-balance (NSBA)valuesofEGarewithin normal limits. Ifstrawsharesof25% and more are federations to young cattle (heifers), the theparticlelengthof straw has significant impact ontheselectivefeeding behavior. Aparticlelength of 1.5cmcompared to7.5 cmlongpreventedstrawcertainly discarding of the straw on the feeding barn. The feed intake increases whenshortstrawis mixed into theTMR.

Keywords: straw mill, heifer, feed selection, dry matter intake

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Authors: Rezvan Hosseinian

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Aim: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. The correlation of musculoskeletal features with other parameters should be considered in SSc patients. Methods: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. Results: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. The median age (IQR) was 47.0 years (16), and 52% had a diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) were associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of the distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed an association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low hematocrit (P<0.01). Conclusion: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Keywords: disease subsets, hand radiography, joint erosion, sclerosis

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Authors: Nasrin Azarbani

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Aim: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. Correlation of musculoskeletal features with other parameters should be considered in SSc patients. Methods: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. Results: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. Median age (IQR) was 47.0 years (16), and 52% had diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) was associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed an association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low haematocrit (P<0.01). Conclusion: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Keywords: sclerosis, disease subsets, joint erosion, musculoskeletal

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Authors: Eugueni Romantchik, Gilbero Lopez, Diego Terrazas

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The residual wind energy recovery from axial air extractors in greenhouses represents a constant source of clean energy production, which reduces production costs by reducing energy consumption costs. The objective of this work is to design, build and evaluate a residual wind energy recovery system. This system consists of a wind turbine placed at an optimal distance, a cone in the air discharge and a mechanism to vary the blades angle of the wind turbine. The system energy balance was analyzed, measuring the main energy parameters such as voltage, amperage, air velocities and angular speeds of the rotors. Tests were carried in a greenhouse with extractor Multifan 130 (1.2 kW, 550 rpm and 1.3 m of diameter) without cone and with cone, with the wind turbine (3 blades with 1.2 m in diameter). The implementation of the system allowed recovering up to 55% of the motor's energy. With the cone installed, the electric energy recovered was increased by 10%. Experimentally, it was shown that changing in 3 degrees the original angle of the wind turbine blades, the angular velocity increases 17.7%.

Keywords: air energy, exhaust fan, greenhouse, wind turbine

Procedia PDF Downloads 134

Authors: Jasvinder Singh, Manjinder Singh

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Welding of dissimilar materials is a complicated process due to the difference in melting point of two materials. Thermal conductivity and coefficient of thermal expansion of dissimilar materials also different; therefore, residual stresses produced in the weldment and base metal are the most critical problem associated with the joining of dissimilar materials. Tensile strength and impact toughness also reduced due to the residual stresses. In the present research work, an attempt has been made to weld SS304 and MS1018 dissimilar materials by submerged arc welding (SAW). By conducting trail, runs most effective parameters welding current, Arc voltage, welding speed and nozzle to plate distance were selected to weld these materials. The fractional factorial technique was used to optimize the welding parameters. Effect on tensile strength (TS), fracture toughness (FT) and microhardness of weldment were studied. It was concluded that by optimizing welding current, voltage and welding speed the properties of weldment can be enhanced.

Keywords: SAW, Tensile Strength (TS), fracture toughness, micro hardness

Procedia PDF Downloads 509

Authors: Sina Moradi, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Patrick Hayde, Rose Amal

Abstract:

Chloramine is commonly used as a disinfectant in drinking water distribution systems (DWDSs), particularly in Australia and the USA. Maintaining a chloramine residual throughout the DWDS is important in ensuring microbiologically safe water is supplied at the customer’s tap. In order to simulate how chloramine behaves when it moves through the distribution system, a water quality network model (WQNM) can be applied. In this work, the WQNM was based on mono-chloramine decomposition reactions, which enabled prediction of mono-chloramine residual at different locations through a DWDS in Australia, using the Bentley commercial hydraulic package (Water GEMS). The accuracy of WQNM predictions is influenced by a number of water quality parameters. Optimization of these parameters in order to obtain the closest results in comparison with actual measured data in a real DWDS would result in both cost reduction as well as reduction in consumption of valuable resources such as energy and materials. In this work, the optimum operating conditions of water quality parameters (i.e. temperature, pH, and initial mono-chloramine concentration) to maximize the accuracy of mono-chloramine residual predictions for two water supply scenarios in an entire network were determined using response surface methodology (RSM). To obtain feasible and economical water quality parameters for highest model predictability, Design Expert 8.0 software (Stat-Ease, Inc.) was applied to conduct the optimization of three independent water quality parameters. High and low levels of the water quality parameters were considered, inevitably, as explicit constraints, in order to avoid extrapolation. The independent variables were pH, temperature and initial mono-chloramine concentration. The lower and upper limits of each variable for two water supply scenarios were defined and the experimental levels for each variable were selected based on the actual conditions in studied DWDS. It was found that at pH of 7.75, temperature of 34.16 ºC, and initial mono-chloramine concentration of 3.89 (mg/L) during peak water supply patterns, root mean square error (RMSE) of WQNM for the whole network would be minimized to 0.189, and the optimum conditions for averaged water supply occurred at pH of 7.71, temperature of 18.12 ºC, and initial mono-chloramine concentration of 4.60 (mg/L). The proposed methodology to predict mono-chloramine residual can have a great potential for water treatment plant operators in accurately estimating the mono-chloramine residual through a water distribution network. Additional studies from other water distribution systems are warranted to confirm the applicability of the proposed methodology for other water samples.

Keywords: chloramine decay, modelling, response surface methodology, water quality parameters

Procedia PDF Downloads 189

Authors: Kingston Rajiah, Mari Kannan Maharajan, Si Jen Yeen, Sara Lew

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Parkinson’s disease (PD) is a progressive neurodegenerative disorder with evolving layers of complexity. Both motor and non-motor symptoms of PD may affect patients’ quality of life (QoL). Life expectancy for an individual with Parkinson’s disease depends on the level of care the individual has access to, can have a direct impact on length of life. Therefore, improvement of the QoL is a significant part of therapeutic plans. Patients with PD, especially those who are in advanced stages, are in great need of assistance, mostly from their family members or caregivers in terms of medical, emotional, and social support. The role of a caregiver becomes increasingly important with the progression of PD, the severity of motor impairment and increasing age of the patient. The nature and symptoms associated with PD can place significant stresses on the caregivers’ burden. As the prevalence of PD is estimated to more than double by 2030, it is important to recognize and alleviate the burden experienced by caregivers. This study focused on the impact of the clinical features on the QoL of PD patients, and of their caregivers. This study included PD patients along with their caregivers and was undertaken at the Malaysian Parkinson's Disease Association from June 2016 to November 2016. Clinical features of PD patients were assessed using the Movement Disorder Society revised Unified Parkinson Disease Rating Scale (MDS-UPDRS); the Hoehn and Yahr Staging of Parkinson's Disease were used to assess the severity and Parkinson's disease activities of daily living scale were used to assess the disability of Parkinson’s disease patients. QoL of PD patients was measured using the Parkinson's Disease Questionnaire-39 (PDQ-39). The revised version of the Zarit Burden Interview assessed caregiver burden. At least one of the clinical features affected PD patients’ QoL, and at least one of the QoL domains affected the caregivers’ burden. Clinical features ‘Saliva and Drooling’, and ‘Dyskinesia’ explained 29% of variance in QoL of PD patients. The QoL domains ‘stigma’, along with ‘emotional wellbeing’ explained 48.6% of variance in caregivers’ burden. Clinical features such as saliva, drooling and dyskinesia affected the QoL of PD patients. The PD patients’ QoL domains such as ‘stigma’ and ‘emotional well-being’ influenced their caregivers’ burden.

Keywords: carers, quality of life, clinical features, Malaysia

Procedia PDF Downloads 221

Authors: H. N. Agiza, M. A. Sohaly, M. A. Elfouly

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Parkinson's disease (PD) is a heterogeneous disorder with common age of onset, symptoms, and progression levels. In this paper we will solve analytically the PD model as a non-linear delay differential equation using the steps method. The step method transforms a system of delay differential equations (DDEs) into systems of ordinary differential equations (ODEs). On some numerical examples, the analytical solution will be difficult. So we will approximate the analytical solution using Picard method and Taylor method to ODEs.

Keywords: Parkinson's disease, step method, delay differential equation, two delays

Procedia PDF Downloads 175

Authors: Ecem Deniz Kırkpantur, Ozge Ecmel Onur, Tuba Cimilli Ozturk, Ebru Unal Akoglu

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Moyamoya disease is a unique chronic progressive cerebrovascular disease characterized by bilateral stenosis or occlusion of the arteries around the circle of Willis with prominent arterial collateral circulation. The occurrence of Moyamoya disease is related to immune, genetic and other factors. There is no curative treatment for Moyamoya disease. Secondary prevention for patients with symptomatic Moyamoya disease is largely centered on surgical revascularization techniques. We present here a 62-year old male presented with headache and vision loss for 2 days. He was previously diagnosed with hypertension and glaucoma. On physical examination, left eye movements were restricted medially, both eyes were hyperemic and their movements were painful. Other neurological and physical examination were normal. His vital signs and laboratory results were within normal limits. Computed tomography (CT) showed dilated vascular structures around both lateral ventricles and atherosclerotic changes inside the walls of internal carotid artery (ICA). Magnetic resonance imaging (MRI) and angiography (MRA) revealed dilated venous vascular structures around lateral ventricles and hyper-intense gliosis in periventricular white matter. Ischemic gliosis around the lateral ventricles were present in the Digital Subtracted Angiography (DSA). After the neurology, ophthalmology and neurosurgery consultation, the patient was diagnosed with Moyamoya disease, pulse steroid therapy was started for vision loss, and super-selective DSA was planned for further investigation. Moyamoya disease is a rare condition, but it can be an important cause of stroke in both children and adults. It generally affects anterior circulation, but posterior cerebral circulation may also be affected, as well. In the differential diagnosis of acute vision loss, occipital stroke related to Moyamoya disease should be considered. Direct and indirect surgical revascularization surgeries may be used to effectively revascularize affected brain areas, and have been shown to reduce risk of stroke.

Keywords: headache, Moyamoya disease, stroke, visual loss

Procedia PDF Downloads 241

Authors: Shripad Kulkarni

Abstract:

Plant disease is an impairment of the normal state of a plant that interrupts or modifies its vital functions. Disease occurs on different parts of plants and cause heavy losses. Diagnosis of Problem is very important before planning any management practice and this can be done based on appearance of the crop, examination of the root and examination of the soil. There are various types of diseases such as biotic (transmissible) which accounts for ~30% whereas , abiotic (not transmissible) diseases are the major one with ~70% incidence. Plant diseases caused by different groups of organism’s belonging fungi, bacteria, viruses, nematodes and few others have remained important in causing significant losses in different crops indicating the urgent need of their integrated management. Various factors favor disease development and different steps and methods are involved in management of diseases under protected condition. Management of diseases through botanicals and bioagents by modifying root and aerial environment, vector management along with care to be taken while managing the disease are analysed.

Keywords: organic production system, diseases, bioagents and polyhouse, agriculture

Procedia PDF Downloads 378