Search results for: disease prediction

Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: K. Roushanger, A. Soleymanzadeh

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Weirs are structures often used in irrigation techniques, sewer networks and flood protection. However, the hydraulic behavior of this type of weir is complex and difficult to predict accurately. An accurate flow prediction over a weir mainly depends on the proper estimation of discharge coefficient. In this study, the Genetic Expression Programming (GEP) approach was used for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Three different performance indexes are used as comparing criteria for the evaluation of the model’s performances. The obtained results approved capability of GEP in prediction of trapezoidal and rectangular side weirs discharge coefficient. The results also revealed the influence of downstream Froude number for trapezoidal weir and upstream Froude number for rectangular weir in prediction of the discharge coefficient for both of side weirs.

Keywords: discharge coefficient, genetic expression programming, trapezoidal weir

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Authors: Sadia Munir

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Preeclampsia is the contributor to the worldwide maternal mortality of approximately 100,000 deaths a year. It complicates about 10% of all pregnancies and is the first cause of maternal admission to intensive care units. Predicting preeclampsia is a major challenge in obstetrics. More importantly, no major progress has been achieved in the treatment of preeclampsia. As placenta is the main cause of the disease, the only way to treat the disease is to extract placental and deliver the baby. In developed countries, the cost of an average case of preeclampsia is estimated at £9000. Interestingly, preeclampsia may have an impact on the health of mother or infant, beyond the pregnancy. We performed a systematic search of PubMed including the combination of terms such as preeclampsia, biomarkers, treatment, hypoxia, inflammation, oxidative stress, vascular endothelial growth factor A, activin A, inhibin A, placental growth factor, transforming growth factor β-1, Nodal, placenta, trophoblast cells, microRNAs. In this review, we have summarized current knowledge on the identification of potential biomarkers for the diagnosis of preeclampsia. Although these studies show promising data in early diagnosis of preeclampsia, the current value of these factors as biomarkers, for the precise prediction of preeclampsia, has its limitation. Therefore, future studies need to be done to support some of the very promising and interesting data to develop affordable and widely available tests for early detection and treatment of preeclampsia.

Keywords: activin, biomarkers, growth factors, miroRNA

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Authors: Jiajia Peng, Jianqing Qiu, Jianjun Ren, Yu Zhao

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Background: Insufficient sleep has been focused on as a public health epidemic. However, a comprehensive analysis of disease trajectory associated with unhealthy sleep habits is still unclear currently. Objective: This study sought to comprehensively clarify the disease's trajectory in relation to the overall poor sleep pattern and unhealthy sleep behaviors separately. Methods: 410,682 participants with available information on sleep behaviors were collected from the UK Biobank at the baseline visit (2006-2010). These participants were classified as having high- and low risk of each sleep behavior and were followed from 2006 to 2020 to identify the increased risks of diseases. We used Cox regression to estimate the associations of high-risk sleep behaviors with the elevated risks of diseases, and further established diseases trajectory using significant diseases. The low-risk unhealthy sleep behaviors were defined as the reference. Thereafter, we also examined the trajectory of diseases linked with the overall poor sleep pattern by combining all of these unhealthy sleep behaviors. To visualize the disease's trajectory, network analysis was used for presenting these trajectories. Results: During a median follow-up of 12.2 years, we noted 12 medical conditions in relation to unhealthy sleep behaviors and the overall poor sleep pattern among 410,682 participants with a median age of 58.0 years. The majority of participants had unhealthy sleep behaviors; in particular, 75.62% with frequent sleeplessness, and 72.12% had abnormal sleep durations. Besides, a total of 16,032 individuals with an overall poor sleep pattern were identified. In general, three major disease clusters were associated with overall poor sleep status and unhealthy sleep behaviors according to the disease trajectory and network analysis, mainly in the digestive, musculoskeletal and connective tissue, and cardiometabolic systems. Of note, two circularity disease pairs (I25→I20 and I48→I50) showed the highest risks following these unhealthy sleep habits. Additionally, significant differences in disease trajectories were observed in relation to sex and sleep medication among individuals with poor sleep status. Conclusions: We identified the major disease clusters and high-risk diseases following participants with overall poor sleep health and unhealthy sleep behaviors, respectively. It may suggest the need to investigate the potential interventions targeting these key pathways.

Keywords: sleep, poor sleep, unhealthy sleep behaviors, disease trajectory, UK Biobank

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Authors: Saeed Pourhassan, Nastaran Maghbouli

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Statement of the problem: The relationship between obesity and cardiovascular diseases has been studied widely(1). The distribution of fat tissue gained attention in relation to cardiovascular risk factors during lang-time research (2). American College of Cardiology/American Heart Association (ACC/AHA) is widely and the most reliable tool to be used as a cardiovascular risk (CVR) assessment tool(3). This study aimed to determine which anthropometric index is better in discrimination of high CVR patients from low risks using ACC/AHA score in addition to finding the best index as a CVR predictor among both genders in different races and countries. Methodology & theoretical orientation: The literature in PubMed, Scopus, Embase, Web of Science, and Google Scholar were searched by two independent investigators using the keywords "anthropometric indices," "cardiovascular risk," and "obesity." The search strategy was limited to studies published prior to Jan 2022 as full-texts in the English language. Studies using ACC/AHA risk assessment tool as CVR and those consisted at least 2 anthropometric indices (ancient ones and novel ones) are included. Study characteristics and data were extracted. The relative risks were pooled with the use of the random-effect model. Analysis was repeated in subgroups. Findings: Pooled relative risk for 7 studies with 16,348 participants were 1.56 (1.35-1.72) for BMI, 1.67(1.36-1.83) for WC [waist circumference], 1.72 (1.54-1.89) for WHR [waist-to-hip ratio], 1.60 (1.44-1.78) for WHtR [waist-to-height ratio], 1.61 (1.37-1.82) for ABSI [A body shape index] and 1.63 (1.32-1.89) for CI [Conicity index]. Considering gender, WC among females and WHR among men gained the highest RR. The heterogeneity of studies was moderate (α²: 56%), which was not decreased by subgroup analysis. Some indices such as VAI and LAP were evaluated just in one study. Conclusion & significance: This meta-analysis showed WHR could predict CVR better in comparison to BMI or WHtR. Some new indices like CI and ABSI are less accurate than WHR and WC. Among women, WC seems to be a better choice to predict cardiovascular disease risk.

Keywords: obesity, cardiovascular disease, risk assessment, anthropometric indices

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Authors: Baeza S. Roberto

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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Moriom Akter Mousumi, Spyridon Paparrizos, Fulco Ludwig

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Rice yield reduction due to climate change-induced disease occurrence is becoming a great concern for coastal farmers of Bangladesh. The development of participatory climate information services (CIS) based on farmers’ needs could implicitly facilitate farmers to get disease forecasts and make better decisions to manage diseases. Therefore, this study aimed to investigate how participatory climate information services assist coastal rice farmers to take up rice disease forecasts and better manage rice diseases by improving their informed decision-making. Through participatory approaches, we developed a tailor-made agrometeorological service through the DROP app to forecast rice diseases and manage them in advance. During farmers field schools (FFS) we communicated 7-day disease forecasts during face-to-face weekly meetings using printed paper and, messenger app derived from DROP app. Results show that the majority of the farmers understand disease forecasts through visualization, symbols, and text. The majority of them use disease forecast information directly from the DROP app followed by face-to-face meetings, messenger app, and printed paper. Farmers participation and engagement during capacity building training at FFS also assist them in making more informed decisions and improved management of diseases using both preventive measures and chemical measures throughout the rice cultivation period. We conclude that the development of participatory CIS and the associated capacity-building and training of farmers has increased farmers' understanding and uptake of disease forecasts to better manage of rice diseases. Participatory services such as the DROP app offer great potential as an adaptation option for climate-smart rice production under changing climatic conditions.

Keywords: participatory climate service, disease forecast, disease management, informed decision making, coastal Bangladesg

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Authors: Kennedy Efosa Ehimwenma, Sujatha Krishnamoorthy, Safiya Al‑Sharji

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The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning.

Keywords: complement of probability, Bayes’ rule, prediction, pre-assessments, computational education, information theory

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

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Authors: Marine Segui, Ruxandra Mihaela Botez

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OpenVSP is an aerodynamic solver developed by National Aeronautics and Space Administration (NASA) that allows building a reliable model of an aircraft. This software performs an aerodynamic simulation according to the angle of attack of the aircraft makes between the incoming airstream, and its speed. A reliable aerodynamic model of the Cessna Citation X was designed but it required a lot of computation time. As a consequence, a prediction method was established that allowed predicting lift and drag coefficients for all Mach numbers and for all angles of attack, exclusively for stall conditions, from a computation of three angles of attack and only one Mach number. Aerodynamic coefficients given by the prediction method for a Cessna Citation X model were finally compared with aerodynamics coefficients obtained using a complete OpenVSP study.

Keywords: aerodynamic, coefficient, cruise, improving, longitudinal, openVSP, solver, time

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

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Authors: Welawat Tienpratarn, Chaiyaporn Yuksen, Rungrawin Promkul, Chetsadakon Jenpanitpong, Pajit Bunta, Suthap Jaiboon

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Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times. The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: supraventricular tachycardia, recurrance, emergency department, adenosine

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Lourdes Hanna, Edward Poluyi, Chibuikem Ikwuegbuenyi, Eghosa Morgan, Grace Imaguezegie

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Background: Degeneration of the central nervous system (CNS), also known as neurodegeneration, describes an age-associated progressive loss of the structure and function of neuronal materials, leading to functional and mental impairments. Main body: Neuroinflammation contributes to the continuous worsening of neurodegenerative states which are characterised by functional and mental impairments due to the progressive loss of the structure and function of neu-ronal materials. Some of the most common neurodegenerative diseases include Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Whilst neuroinflammation is a key contributor to the progression of such disease states, it is not the single cause as there are multiple factors which contribute. Theoretically, non-steroidal anti-inflammatory drugs (NSAIDs) have potential to target neuroinflammation to reduce the severity of disease states. Whilst some animal models investigating the effects of NSAIDs on the risk of neurodegenerative diseases have shown a beneficial effect, this is not the same finding. Conclusion: Further investigation using more advanced research methods is required to better understand neuroinflammatory pathways and understand if there is still a potential window for NSAID efficacy.

Keywords: intervention, central nervous system, neurodegeneration, neuroinflammation

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Authors: Shadi Kanan, Ghada Shahrour, Barbara Broome, Donna Bernert, Muntaha Alibrahim, Dana Hansen

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Coronary artery disease is responsible for over 7 million deaths a year worldwide. In developing countries, such as Jordan, the incidence of coronary artery disease exceeds that of developed countries. One contributing factor to this disparity is decreased physical activity among the population, for reasons related to specific cultural and religious values. Using the theory of planned behaviour, the purpose of this study was to investigate the intentions of Jordanian patients with coronary artery disease regarding physical activity. A total of 109 patients with coronary artery disease were recruited for this cross-sectional study from King Abdullah University Hospital in Jordan. A 15-item questionnaire based on the theory of planned behaviour was used to assess participants’ attitudes, subjective norms, perceived behavioural control and intentions towards engagement in physical activity. Perceived behavioural control was found to have the strongest significant relationship with participants’ intentions to engage in physical activity. Barriers to physical activity included lack of time, lack of support from family or friends, and feelings of exhaustion. Lifestyle interventions for patients with coronary artery disease should focus on fostering a sense of control over the environment to encourage patients to engage in physical activity.

Keywords: coronary artery disease, perceived behavioural control, subjective norms, theory of planned behaviour

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: Oya Bulut, Oguzhan Avci, Zafer Bulut, Atilla Simsek

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Livestock breeders have focused on the improvement of production traits with little or no attention for improvement of disease resistance traits. In order to determine the association between the genetic structure of the individual gene loci with possibility of the occurrence and the development of diseases, MHC (major histocompatibility complex) are frequently used. Because of their importance in the immune system, MHC locus is considered as candidate genes for resistance/susceptibility against to different diseases. Major histocompatibility complex (MHC) molecules play a critical role in both innate and adaptive immunity and have been considered candidate molecular markers of an association between polymorphisms and resistance/susceptibility to diseases. The purpose of this study is to give some information about MHC genes become an important area of study in recent years in terms of animal husbandry and determine the relation between MHC genes and resistance/susceptibility to disease.

Keywords: MHC, polymorphism, disease, resistance

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Authors: Intan Maisarah Abd Rahim, Herlina Abdul Rahim, Rashidah Ghazali

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Diabetes is a medical condition that can lead to various diseases such as stroke, heart disease, blindness and obesity. In clinical practice, the concern of the diabetic patients towards the blood glucose examination is rather alarming as some of the individual describing it as something painful with pinprick and pinch. As for some patient with high level of glucose level, pricking the fingers multiple times a day with the conventional glucose meter for close monitoring can be tiresome, time consuming and painful. With these concerns, several non-invasive techniques were used by researchers in measuring the glucose level in blood, including ultrasonic sensor implementation, multisensory systems, absorbance of transmittance, bio-impedance, voltage intensity, and thermography. This paper is discussing the application of the near-infrared (NIR) spectroscopy as a non-invasive method in measuring the glucose level and the implementation of the linear system identification model in predicting the output data for the NIR measurement. In this study, the wavelengths considered are at the 1450 nm and 1950 nm. Both of these wavelengths showed the most reliable information on the glucose presence in blood. Then, the linear Autoregressive Moving Average Exogenous model (ARMAX) model with both un-regularized and regularized methods was implemented in predicting the output result for the NIR measurement in order to investigate the practicality of the linear system in this study. However, the result showed only 50.11% accuracy obtained from the system which is far from the satisfying results that should be obtained.

Keywords: diabetes, glucose level, linear, near-infrared, non-invasive, prediction system

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

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Authors: Khanh To Duc, Monica Chiogna, Gianfranco Adimari

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With three diagnostic categories, assessment of the performance of diagnostic tests is achieved by the analysis of the receiver operating characteristic (ROC) surface, which generalizes the ROC curve for binary diagnostic outcomes. The volume under the ROC surface (VUS) is a summary index usually employed for measuring the overall diagnostic accuracy. When the true disease status can be exactly assessed by means of a gold standard (GS) test, unbiased nonparametric estimators of the ROC surface and VUS are easily obtained. In practice, unfortunately, disease status verification via the GS test could be unavailable for all study subjects, due to the expensiveness or invasiveness of the GS test. Thus, often only a subset of patients undergoes disease verification. Statistical evaluations of diagnostic accuracy based only on data from subjects with verified disease status are typically biased. This bias is known as verification bias. Here, we consider the problem of correcting for verification bias when continuous diagnostic tests for three-class disease status are considered. We assume that selection for disease verification does not depend on disease status, given test results and other observed covariates, i.e., we assume that the true disease status, when missing, is missing at random. Under this assumption, we discuss several solutions for ROC surface analysis based on imputation and re-weighting methods. In particular, verification bias-corrected estimators of the ROC surface and of VUS are proposed, namely, full imputation, mean score imputation, inverse probability weighting and semiparametric efficient estimators. Consistency and asymptotic normality of the proposed estimators are established, and their finite sample behavior is investigated by means of Monte Carlo simulation studies. Two illustrations using real datasets are also given.

Keywords: imputation, missing at random, inverse probability weighting, ROC surface analysis

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Authors: Rujijan Vichivanives, Kittiya Poonsilp, Canasanan Wanavijit

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This research aims to create mobile tools to analyze rice disease quickly and easily. The principle of object-oriented software engineering and objective-C language were used for software development methodology and the principle of decision tree technique was used for analysis method. Application users can select the features of rice disease or the color appears on the rice leaves for recognition analysis results on iOS mobile screen. After completing the software development, unit testing and integrating testing method were used to check for program validity. In addition, three plant experts and forty farmers have been assessed for usability and benefit of this system. The overall of users’ satisfaction was found in a good level, 57%. The plant experts give a comment on the addition of various disease symptoms in the database for more precise results of the analysis. For further research, it is suggested that image processing system should be developed as a tool that allows users search and analyze for rice diseases more convenient with great accuracy.

Keywords: rice disease, data analysis system, mobile application, iOS operating system

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Authors: Jibran Abbasy, Rizwan Sultan, Ammar Humayun, Tabish Chawla

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Background: Hydatid disease caused by Echinococcus Granulosus affects liver in 70-90% of cases. Dogs are the definitive host while humans are the accidental host. Modalities used for its treatment are especially important for our population as the disease is endemic in many Asian countries. The aim of the study was to perform an audit of the various modalities used for treatment of hydatid disease of liver and the response to each modality in tertiary care center of Pakistan. Materials and Methods: Retrospective audit of patients diagnosed and treated for Hydatid disease of the liver at Aga Khan University Hospital from 1st January 2007 to 31st December 2014 was completed. All patients aged 16 and above were included. Patients who had extra hepatic disease and missing records were excluded. Outcome measures were morbidity, mortality and recurrence of the disease. Results: During the study period 56 patients were treated for isolated hepatic hydatid disease and were included. Mean age was 39 years with 48% being females and 52% males. Most common presenting complaint was abdominal pain seen in 53% of patients(n=41). Duration of symptoms was less than 6 months in 74% (n=38). Mostly right lobe was involved in 69% (n=38).Most common treatment modality used was surgery in 34 patients followed by PAIR in 14 patients while 8 patients were treated medically. At a median follow up of 34 months recurrence was seen in 2 patients treated with PAIR while no patient treated with surgery had recurrence with the median follow up of 20 months. While no morbidity and mortality were observed in PAIR, but in surgery 5 patients had morbidity while 1 patient had mortality. Conclusion: Our data is comparative to other studies in terms of morbidity, mortality, and recurrence. We had adequate follow up. In our study PAIR and surgery both are effective and have less complications and recurrence rate. Surgery is still the gold standard in terms of recurrence.

Keywords: echinococcous granulosus, puncture aspiration irrigation reaspiration (PAIR), surgery, hydatid disease

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Authors: Amirreza Razzaghipour Sorkhab

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One of the most common disorders among humans is hearing impairment. This paper provides an evidence base that recovers understanding of Meniere’s disease and highlights the physical and mental health correlates of the disorder. Meniere's disease is more common in the elderly. The term idiopathic endolymphatic hydrops has been attributed to this disease by some in the previous. Meniere’s disease demonstrations a genetic tendency, and a family history is found in 10% of cases, with an autosomal dominant inheritance pattern. The COCH gene may be one of the hereditary factors contributing to Meniere’s disease, and the possibility of a COCH mutation should be considered in patients with Meniere’s disease symptoms. Should be considered Missense mutations in the COCH gene cause the autosomal dominant sensorineural hearing loss and vestibular disorder. Meniere’s disease is a complex, heterogeneous disorder of the inner ear and that is characterized by episodes of vertigo lasting from minutes to hours, fluctuating sensorineural hearing loss, tinnitus, and aural fullness. The existing evidence supports the suggestion that age and sleep disorder are risk factors for Meniere's disease. Many factors have been reported to precipitate the progress of Menier, including endolymphatic hydrops, immunology, viral infection, inheritance, vestibular migraine, and altered intra-labyrinthine fluid dynamics. Although there is currently no treatment that has a proven helpful effect on hearing levels or on the long-term evolution of the disease, however, in the primary stages, the hearing may improve among attacks, but a permanent hearing loss occurs in the majority of cases. Current publications have proposed a role for the intratympanic use of medicine, mostly aminoglycosides, for the control of vertigo. more than 85% of patients with Meniere's disease are helped by either changes in lifestyle and medical treatment or minimally aggressive surgical procedures such as intratympanic steroid therapy, intratympanic gentamicin therapy, and endolymphatic sac surgery. However, unilateral vestibular extirpation methods (intratympanic gentamicin, vestibular nerve section, or labyrinthectomy) are more predictable but invasive approaches to control the vertigo attacks. Medical therapy aimed at reducing endolymph volume, such as low-sodium diet, diuretic use, is the typical initial treatment.

Keywords: meniere's disease, endolymphatic hydrops, hearing loss, vertigo, tinnitus, COCH gene

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju

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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.

Keywords: comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events

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Authors: Nisha Thapa, Ram Prasad Mainali, Prakriti Chand

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Broadleaf mustard is a commercialized leafy vegetable of Nepal. However, its utilization is hindered in terms of production and productivity due to the high intensity of insects, pests, and diseases causing great loss. The plant protection part of the crop’s disease and damage intensity has not been studied much from research perspectives in Nepal. The research aimed to evaluate broadleaf mustard genotypes for resistance against different diseases. A total of 35 native genotypes of broadleaf mustard were screened at weekly intervals by scoring the plants for ten weeks. Five different diseases, such as Rhizoctonia root rot, Alternaria blight, black rot, turnip mosaic virus disease, and white rust, were reported from the broad leaf mustard genotypes. Out of 35 genotypes, 23 genotypes were found with very high Rhizoctonia Root Rot severity, whereas 8 genotypes showed very high Alternaria blight severity. Likewise, 3 genotypes were found with high Black rot severity, and 1 genotype was found with very high Turnip mosaic virus disease incidence. Similarly, 2 genotypes were found to have very high White rust severity. Among the disease of national importance, Rhizoctonia root rot was found to be the most severe disease with the greatest loss. Broadleaf mustard genotypes like Rato Rayo, CO 1002, and CO 11007 showed average to the high level of field resistance; therefore, these genotypes should be used, conserved, and stored in a mustard improvement program as the disease resistance quality or susceptibility of these genotypes can be helpful for seed producing farmers, companies and other stakeholders through varietal improvement and developmental works that further aids in sustainable disease management of the vegetable.

Keywords: genotype, disease resistance, Rhizoctonia root rot severity, varietal improvement

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Authors: Sahar Shams

Abstract:

Coeliac disease (CD) is a widely reported disease particularly in countries with predominant Caucasian populations. It presents with many signs and symptoms including iron deficiency (ID) and iron deficiency anaemia/anaemia (IDA/A). The exact association between ID, IDA/A and CD and how accurate these signs are in diagnosing CD is not fully known. This systematic review was conducted to investigate the accuracy of both ID & IDA/A as a diagnostic indicator for CD and whether it warrants point of care testing. A systematic review was performed looking at studies published in MEDLINE, Embase, Cochrane Library, and Web of Science. QUADAS-2 tool was used to assess risk of bias in each study. ROC curve and forest plots were generated as part of the meta-analysis after data extraction. 16 studies were identified in total, 13 of which were IDA/A studies and 3 ID studies. The prevalence of CD regardless of diagnostic indicator was assumed as 1%. The QUADAS-2 tool indicated most of studies as having high risk of bias. The PPV for CD was higher in those with ID than for those with IDA/A. Meta-analysis showed the overall odds of having CD is 5 times higher in individuals with ID & IDA/A. The ROC curve showed that there is definitely an association between both diagnostic indicators and CD, the association is not a particularly strong one due to great heterogeneity between studies. Whilst an association between IDA/A & ID and coeliac disease was evident, the results were not deemed significant enough to prompt coeliac disease testing in those with IDA/A & ID.

Keywords: anemia, iron deficiency anemia, coeliac disease, point of care testing

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Authors: Abdela Bulbula

Abstract:

Background: Marek’s disease virus is a devastating infection, causing high morbidity and mortality in chickens in Ethiopia. Methods: The current study was conducted from March to November, 2021 with the general objective of performing antemortem and postmortem, isolation, and molecular detection of Marek’s disease virus from outbreak cases in southwestern Ethiopia. Accordingly, based on outbreak information reported from the study sites namely, Bedelle, Yayo, and Bonga towns in southwestern Ethiopia, 50 sick chickens were sampled. The backyard and intensive farming systems of chickens were included in the sampling and priorities were given for chickens that showed clinical signs that are characteristics of Marek’s disease. Results: By clinical examinations, paralysis of legs and wings, gray eye, loss of weight, difficulty in breathing, and depression were recorded on all chickens sampled for this study and death of diseased chickens was observed. In addition, enlargement of the spleen and gross lesions of the liver and heart were recorded during postmortem examination. The death of infected chickens was observed in both vaccinated and non-vaccinated flocks. Out of 50 pooled feather follicle samples, Marek’s disease virus was isolated from 14/50 (28%) by cell culture method and out of six tissue samples, the virus was isolated from 5/6(83.30%). By Real time polymerization chain reaction technique, which was targeted to detect the Meq gene, Marek’s disease virus was detected from 18/50 feather follicles which accounts for 36% of sampled chickens. Conclusion: In general, the current study showed that the circulating Marek’s disease virus in southwestern Ethiopia was caused by the oncogenic Gallid herpesvirus-2 (Serotype-1). Further research on molecular characterization of revolving virus in current and other regions is recommended for effective control of the disease through vaccination.

Keywords: Ethioi, Marek's disease, isolation, molecular

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