Search results for: disease cluster

Authors: Selvaprakash Ramalingam, Rabi N. Sahoo, Dharmendra Saraswat, A. Kumar, Rajeev Ranjan, Joydeep Mukerjee, Viswanathan Chinnasamy, K. K. Chaturvedi, Sanjeev Kumar

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Rice is one of the most important staple food crops in the world. Among the various diseases that affect rice crops, rice blast is particularly significant, causing crop yield and economic losses. While the plant has defense mechanisms in place, such as chemical indicators (proteins, salicylic acid, jasmonic acid, ethylene, and azelaic acid) and resistance genes in certain varieties that can protect against diseases, susceptible varieties remain vulnerable to these fungal diseases. Early prediction of rice blast (RB) disease is crucial, but conventional techniques for early prediction are time-consuming and labor-intensive. Hyperspectral remote sensing techniques hold the potential to predict RB disease at its asymptomatic stage. In this study, we aimed to demonstrate the prediction of RB disease at the asymptomatic stage using non-imaging hyperspectral ASD spectroradiometer under controlled laboratory conditions. We applied statistical spectral discrimination theory to identify unknown spectra of M. Oryzae, the fungus responsible for rice blast disease. The infrared (IR) region was found to be significantly affected by RB disease. These changes may result in alterations in the absorption, reflection, or emission of infrared radiation by the affected plant tissues. Our research revealed that the protein spectrum in the IR region is impacted by RB disease. In our study, we identified strong correlations in the region (Amide group - I) around X 1064 nm and Y 1300 nm with the Lambda / Lambda derived spectra methods for protein detection. During the stages when the disease is developing, typically from day 3 to day 5, the plant's defense mechanisms are not as effective. This is especially true for the PB-1 variety of rice, which is highly susceptible to rice blast disease. Consequently, the proteins in the plant are adversely affected during this critical time. The spectral contour plot reveals the highly correlated spectral regions 1064 nm and Y 1300 nm associated with RB disease infection. Based on these spectral sensitivities, we developed new spectral disease indices for predicting different stages of disease emergence. The goal of this research is to lay the foundation for future UAV and satellite-based studies aimed at long-term monitoring of RB disease.

Keywords: rice blast, asymptomatic stage, spectral sensitivity, IR

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Authors: Fangjing Zhou, Wen Chen, Brian J. Hall, Yu Wang, Carl Latkin, Li Ling, Joseph D. Tucker

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Background: Economic and social reforms designed to open China to the world has been successful, but also appear to have rapidly laid the foundation for the reemergence of STIs since 1980s. Changes in sexual behaviors, relationships, and norms among Chinese contributed to the STIs epidemic. As the massive population moved during the last 30 years, early coital debut, multiple sexual partnerships, and unprotected sex have increased within the general population. Our objectives were to assess associations between residences location, sexual risk behaviors and sexually transmitted infections (STIs) among adults living in Guangzhou, China. Methods: Stratified cluster sampling followed a two-step process was used to select populations aged 18-59 years in Guangzhou, China. Spatial methods including Geographic Information Systems (GIS) were utilized to identify 1400 coordinates with latitude and longitude. Face-to-face household interviews were conducted to collect self-report data on sexual risk behaviors and diagnosed STIs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of sexual risk behaviors and STIs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results: In this study, 1215 of 1400 households attempted surveys, with 368 refusals, resulting in a sample of 751 completed surveys. The prevalence of self-reported sexual risk behaviors was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STIs was 7.06%. Anal intercourse clustered in an area located along the border within the rural-urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou less than one year (p=0.007) overlapped this cluster. Excess cases for sex without a condom (p=0.031) overlapped the cluster for college students (p<0.001). Conclusions: Short-term migrants and college students reported greater sexual risk behaviors. Programs to increase safer sex within these communities to reduce the risk of STIs are warranted in Guangzhou. Spatial analysis identified geographical clusters of sexual risk behaviors, which is critical for optimizing surveillance and targeting control measures for these locations in the future.

Keywords: cluster analysis, migrant, sexual risk behaviors, spatial distribution

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Authors: Shuqi Zhang

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Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

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Authors: Anat Achiron

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Background: Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system in young adults. It involves the immune system attacking the protective covering of nerve fibers (myelin), leading to inflammation and damage. MS can result in various neurological symptoms, such as muscle weakness, coordination problems, and sensory disturbances. Seizures are not common in MS, and the frequency is estimated between 0.4 to 6.4% over the disease course. Objective: Investigate the frequency of seizures in individuals with multiple sclerosis and to identify associated risk factors. Methods: We evaluated the frequency of seizures in a large cohort of 5686 MS patients followed at the Sheba Multiple Sclerosis Center and studied associated risk factors and comorbidities. Our research was based on data collection using a cohort study design. We applied logistic regression analysis to assess the strength of associations. Results: We found that younger age at onset, longer disease duration, and prolonged time to immunomodulatory treatment initiation were associated with increased risk for seizures. Conclusions: Our findings suggest that seizures in people with MS are directly related to the demyelination process and not associated with other factors like medication side effects or comorbid conditions. Therefore, initiating immunomodulatory treatment early in the disease course could reduce not only disease activity but also decrease seizure risk.

Keywords: epilepsy, seizures, multiple sclerosis, white matter, age

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Authors: Robin Singh, Mayank Nautiyal, Priyank Jain, Vatasta Koul, Vaibhav Sharma

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The curiosity to investigate the space and its mysteries was dependably the main impetus of human interest, as the particle of livings exists from the "debut de l'Univers" (beginning of the Universe) typified with its few other living things. The sharp drive to uncover the secrets of stars and their unusual deportment was dependably an ignitor of stars investigation. As humankind lives in civilizations and states, stars likewise live in provinces named ‘clusters’. Clusters are separates into 2 composes i.e. open clusters and globular clusters. An open cluster is a gathering of thousand stars that were moulded from a comparable goliath sub-nuclear cloud and for the most part; contain Propulsion I (extremely metal-rich) and Propulsion II (mild metal-rich), where globular clusters are around gathering of more than thirty thousand stars that circles a galactic focus and basically contain Propulsion III (to a great degree metal-poor) stars. Futurology of this paper lies in the spectroscopic investigation of globular clusters like M92 and NGC419 and open clusters like M34 and IC2391 in different color bands by using software like VIREO virtual observatory, Aladin, CMUNIWIN, and MS-Excel. Assessing the outcome Hertzsprung-Russel (HR) diagram with exemplary cosmological models like Einstein model, De Sitter and Planck survey demonstrate for a superior age estimation of respective clusters. Colour-Magnitude Diagram of these clusters was obtained by photometric analysis in g and r bands which further transformed into BV bands which will unravel the idea of stars exhibit in the individual clusters.

Keywords: color magnitude diagram, globular clusters, open clusters, Einstein model

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Authors: M. Lesage, G. Raîche, M. Riopel, F. Fortin, D. Sebkhi

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This paper describes a Web server implementation of the hierarchical aggregate assessment process in the field of education. This process describes itself as a field of teamwork assessment where teams can have multiple levels of hierarchy and supervision. This process is applied everywhere and is part of the management, education, assessment and computer science fields. The E-Assessment website named “Cluster” records in its database the students, the course material, the teams and the hierarchical relationships between the students. For the present research, the hierarchical relationships are team member, team leader and group administrator appointments. The group administrators have the responsibility to supervise team leaders. The experimentation of the application has been performed by high school students in geology courses and Canadian army cadets for navigation patrols in teams. This research extends the work of Nance that uses a hierarchical aggregation process similar as the one implemented in the “Cluster” application.

Keywords: e-learning, e-assessment, teamwork assessment, hierarchical aggregate assessment

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Authors: Maciej Szeląg, Stanisław Fic

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The impact of elevated temperatures on the construction materials manifests in change of their physical and mechanical characteristics. Stresses and thermal deformations that occur inside the volume of the material cause its progressive degradation as temperature increase. Finally, the reactions and transformations of multiphase structure of cementitious composite cause its complete destruction. A particularly dangerous phenomenon is the impact of thermal shock – a sudden high temperature load. The thermal shock leads to a high value of the temperature gradient between the outer surface and the interior of the element in a relatively short time. The result of mentioned above process is the formation of the cracks and scratches on the material’s surface and inside the material. The article describes the use of computer image analysis techniques to identify and assess the structure of the cluster cracks on the surfaces of modified cement pastes, caused by thermal shock. Four series of specimens were tested. Two Portland cements were used (CEM I 42.5R and CEM I 52,5R). In addition, two of the series contained metakaolinite as a replacement for 10% of the cement content. Samples in each series were made in combination of three w/b (water/binder) indicators of respectively 0.4; 0.5; 0.6. Surface cracks of the samples were created by a sudden temperature load at 200°C for 4 hours. Images of the cracked surfaces were obtained via scanning at 1200 DPI; digital processing and measurements were performed using ImageJ v. 1.46r software. In order to examine the cracked surface of the cement paste as a system of closed clusters – the dispersal systems theory was used to describe the structure of cement paste. Water is used as the dispersing phase, and the binder is used as the dispersed phase – which is the initial stage of cement paste structure creation. A cluster itself is considered to be the area on the specimen surface that is limited by cracks (created by sudden temperature loading) or by the edge of the sample. To describe the structure of cracks two stereological parameters were proposed: A ̅ – the cluster average area, L ̅ – the cluster average perimeter. The goal of this study was to compare the investigated stereological parameters with the mechanical properties of the tested specimens. Compressive and tensile strength testes were carried out according to EN standards. The method used in the study allowed the quantitative determination of defects occurring in the examined modified cement pastes surfaces. Based on the results, it was found that the nature of the cracks depends mainly on the physical parameters of the cement and the intermolecular interactions on the dispersal environment. Additionally, it was noted that the A ̅/L ̅ relation of created clusters can be described as one function for all tested samples. This fact testifies about the constant geometry of the thermal cracks regardless of the presence of metakaolinite, the type of cement and the w/b ratio.

Keywords: cement paste, cluster cracks, elevated temperature, image analysis, metakaolinite, stereological parameters

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Xianpei Li, Yanmin Yuan, Tracy Lin Huan

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Researchers are doing enchanting research into the inherited features of Alzheimer’s disease and probable consistent therapies. In Alzheimer’s, memories are extinct in reverse order; memories formed lately are more transitory than those from formerly. Reminiscence therapy includes the conversation of past actions, trials and knowledges with another individual or set of people, frequently with the help of perceptible reminders such as photos, household and other acquainted matters from the past, music and collection of tapes. In this manuscript, the competence of reminiscence therapy for Alzheimer’s disease is measured using logistic regression based linear bootstrap aggregating. Logistic regression is used to envisage the experiential features of the patient’s memory through various therapies. Linear bootstrap aggregating shows better stability and accuracy of reminiscence therapy used in statistical classification and regression of memories related to validation therapy, supportive psychotherapy, sensory integration and simulated presence therapy.

Keywords: Alzheimer’s disease, linear bootstrap aggregating, logistic regression, reminiscence therapy

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Mualla McManus, Jenna Luche Thaye

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World Health Organisation International Classification of Disease codes were created to define disease including infections in order to guide and educate diagnosticians. Most infectious diseases such as syphilis are clearly defined by their ICD 10 codes and aid/help to educate the clinicians in syphilis diagnosis and treatment globally. However, current ICD 10 codes for relapsing fever Borreliosis and Lyme disease are less clearly defined and can impede appropriate diagnosis especially if the clinician is not familiar with the symptoms of these infectious diseases. This is despite substantial number of scientific articles published in peer-reviewed journals about relapsing fever and Lyme disease. In the USA there are estimated 380,000 people annually contacting Lyme disease, more cases than breast cancer and 6x HIV/AIDS cases. This represents estimated 0.09% of the USA population. If extrapolated to the global population (7billion), 0.09% equates to 63 million people contracting relapsing fever or Lyme disease. In many regions, the rate of contracting some form of infection from tick bite may be even higher. Without accurate and appropriate diagnostic codes, physicians are impeded in their ability to properly care for their patients, leaving those patients invisible and marginalized within the medical system and to those guiding public policy. This results in great personal hardship, pain, disability, and expense. This unnecessarily burdens health care systems, governments, families, and society as a whole. With accurate diagnostic codes in place, robust data can guide medical and public health research, health policy, track mortality and save health care dollars. Better defined ICD codes are the way forward in educating the diagnosticians about relapsing fever and Lyme diseases.

Keywords: WHO ICD codes, relapsing fever, Lyme diseases, World Health Organisation

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Authors: Davies Obaromi, Qin Yongsong, James Ndege, Azeez Adeboye, Akinwumi Odeyemi

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The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter.

Keywords: Besag2, CAR models, disease mapping, INLA, spatial models

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Jyotismita Talukdar

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In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free environment and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient’s data has been collected and studied. The data is analysed and the results have been compared with the Doctor’s diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). It has been found that highest accuracy(93%), has been achieved in case of PPI( Post-Permanent-Pacemaker Implementation ), around 79% in case of CAD(Coronary Artery disease), 87% in DCM (Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75 % in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP (Ischemic Cardiomyopathy), about 38% and AF( Atrial Fibrillation), about 60 to 62%.

Keywords: coronary heart disease, chronic stable angina, sick sinus syndrome, cardiovascular disease, cholesterol, Thalach

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Authors: Ofosuhene O. Apenteng, Noor Azina Ismail

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Recently tourism is a major foreign exchange earner in the World. In this paper, we propose the mathematical model to study the impact of tourists on the spread of HIV incidences using compartmental differential equation models. Simulation studies of reproduction number are used to demonstrate new insights on the spread of HIV disease. The periodogram analysis of a time series was used to determine the speed at which the disease is spread. The results indicate that with the persistent flow of tourism into a country, the disease status has increased the epidemic rate. The result suggests that the government must put more control on illegal prostitution, unprotected sexual activity as well as to emphasis on prevention policies that include the safe sexual activity through the campaign by the tourism board.

Keywords: HIV/AIDS, mathematical transmission modeling, tourists, stability, simulation

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Authors: Jyoti Agrawal

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Introduction: Renal diseases in children and young adult can be difficult to diagnose early as it may present only with few symptoms, tends to have different course than adult and respond variously to different treatment. The pattern of renal disease in children is different from developing countries as compared to developed countries. Methods: This study was a hospital based prospective observational study carried from March, 2014 to February 2015 at BP Koirala institute of health sciences. Patients with renal disease, both inpatient and outpatient from birth to 14 years of age were enrolled in the study. The diagnosis of renal disease was be made on clinical and laboratory criteria. Results: Total of 120 patients were enrolled in our study which contributed to 3.74% % of total admission. The commonest feature of presentation was edema (75%), followed by fever (65%), hypertension (60%), decreased urine output (45%) and hematuria (25%). Most common diagnosis was acute glomerulonephritis (40%) followed by Nephrotic syndrome (25%) and urinary tract infection (25%). Renal biopsy was done for 10% of cases and most of them were steroid dependent nephrotic syndrome. 5% of our cases expired because of multiorgan dysfunction syndrome, sepsis and acute kidney injury. Conclusion: Renal disease contributes to a large part of hospital pediatric admission as well as mortality and morbidity to the children.

Keywords: glomerulonephritis, nephrotic syndrome, renal disease, urinary tract infection

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Authors: Sara Sahraoui

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This research studied the effect of Alzheimer's disease (AD) on language information processing in subjects with Alzheimer’s disease (AD) who were bilingual (French and dialectical Arabic). The results show a disorder of certain semantic aspects of their mother tongue (L1). On the other hand, grammatical levels appeared to be relatively unaffected in oral speech in L1 but were disturbed in the second language (L2). In consequence, we constructed a cognitive-language stimulation protocol for bilingual patients (PSCLAB) to respond to this disorder. The efficacy of this protocol in terms of rehabilitation was assessed in 30 such patients through discourse analysis carried out before and after initiating the protocol. The results show that cognitive/language training using the PSCLAB appears to improve the language behaviour of bilingual patients with AD. However, this survey study aims to verify the satisfaction of patients’ relatives with the results of cognitive language training by PSCLAB. We developed a brief instrument to measure the satisfaction of family members. The results report that the patient's relatives are satisfied with the results of cognitive training by PSCLAB.

Keywords: satisfaction, Alzheimer's disease, rehabilitation, levels language

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Authors: Ali Bayram, Burak Uz, Remzi Yiğiter

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The impairment of lipid metabolism in the central nervous system has been suggested as a critical factor of Alzheimer’s disease (AD) pathogenesis. Homo sapiens acyl-coenyme A synthetase short-chain family member 2 (ACSS2) gene encodes the enzyme acetyl-Coenzyme A synthetase (AMP forming; AceCS) providing acetyl-coenzyme A (Ac-CoA) for various physiological processes, such as cholesterol and fatty acid synthesis, as well as the citric acid cycle. We investigated ACSS2, transcript variant 1 (ACSS2*1), mRNA levels in the peripheral blood mononuclear cells (PBMC) of patients with AD and compared them with the controls. The study group comprised 50 patients with the diagnosis of AD who have applied to Gaziantep University Faculty of Medicine, and Department of Neurology. 49 healthy individuals without any neurodegenerative disease are included as controls. ACSS2 mRNA expression in PBMC of AD/control patients was 0.495 (95% confidence interval: 0.410-0.598), p= .000000001902). Further studies are needed to better clarify this association.

Keywords: Alzheimer’s disease, ACSS2 Genes, mRNA expression, RT-PCR

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Authors: Ireneusz Majsterek, Dariusz Pytel, J. Alan Diehl

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PKR-like ER kinase (PERK) is serine/threonie endoplasmic reticulum (ER) transmembrane kinase activated during ER-stress. PERK can activate signaling pathways known as unfolded protein response (UPR). Attenuation of translation is mediated by PERK via phosphorylation of eukaryotic initiation factor 2α (eIF2α), which is necessary for translation initiation. PERK activation also directly contributes to activation of Nrf2 which regulates expression of anti-oxidant enzymes. An increased phosphorylation of eIF2α has been reported in Alzheimer disease (AD) patient hippocampus, indicating that PERK is activated in this disease. Recent data have revealed activation of PERK signaling in non-Hodgkins lymphomas. Results also revealed that loss of PERK limits mammary tumor cell growth in vitro and in vivo. Consistent with these observations, activation of UPR in vitro increases levels of the amyloid precursor protein (APP), the peptide from which beta-amyloid plaques (AB) fragments are derived. Finally, proteolytic processing of APP, including the cleavages that produce AB, largely occurs in the ER, and localization coincident with PERK activity. Thus, we expect that PERK-dependent signaling is critical for progression of many types of diseases (human cancer, neurodegenerative disease and other). Therefore, modulation of PERK activity may be a useful therapeutic target in the treatment of different diseases that fail to respond to traditional chemotherapeutic strategies, including Alzheimer’s disease. Our goal will be to developed therapeutic modalities targeting PERK activity.

Keywords: PERK kinase, small molecule inhibitor, neurodegenerative disease, Alzheimer’s disease

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Authors: Djamila Meghraoui, Bachir Boudraa, Thouraya Meksen, M.Boudraa

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Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.

Keywords: Parkinson’s disease recognition, PCA, MDVP, multinomial Naive Bayes

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Authors: Farzaneh Rajabighamchi, Stan van Hoesel, Christof Defryn

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The generalized traveling salesman problem (GTSP) is an extension of the traveling salesman problem (TSP) where the set of nodes is partitioned into clusters, and the salesman must visit exactly one node per cluster. In this research, we apply the definition of the GTSP to an order picker routing problem with multiple locations per product. As such, each product represents a cluster and its corresponding nodes are the locations at which the product can be retrieved. To pick a certain product item from the warehouse, the picker needs to visit one of these locations during its pick tour. As all products are scattered throughout the warehouse, the product clusters not separated geographically. We propose an exact LP model as well as heuristic and meta-heuristic solution algorithms for the order picking problem with multiple product locations.

Keywords: warehouse optimization, order picking problem, generalised travelling salesman problem, heuristic algorithm

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Authors: Debesh R. Roy

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The fundamental issue in understanding the origin and growth mechanism of nanomaterials, from a fundamental unit is a big challenging problem to the scientists. Recently, an immense attention is generated to the researchers for prediction of exceptionally stable atomic cluster units as the building units for future smart materials. The present study is a systematic investigation on the stability and electronic properties of a series of bimetallic (semiconductor-alkaline earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for exceptional and/ or unusual stable motifs. A very popular hybrid exchange-correlation functional, B3LYP as proposed by A. D. Becke along with a higher basis set, viz., 6-31+G[d,p] is employed for this purpose under the density functional formalism. The magic stability among the concerned clusters is explained using the jellium model. It is evident from the present study that the magic stability of B4Mg3 cluster arises due to the jellium shell closure.

Keywords: atomic clusters, density functional theory, jellium model, magic clusters, smart nanomaterials

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Authors: Arghavan Tonkaboni, Shamsolmolouk Najafi, Mohmmad Taghi Kiani, Mehrzad Gholampour, Touraj Goli

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Introduction: Recurrent aphthous stomatitis (RAS) is a multifactorial recurrent oral lesion; which is an autoimmune disease. TH1 cytokines are the most important etiological factors. Autoimmune thyroid disease (ATD) is one of the most common autoimmune diseases and generally coexists with other autoimmune diseases. This study assessed the prevalence of thyroid disease in patients with recurrent aphthous stomatitis. Materials and Methods: This case control study assessed 100 known RAS patients who were diagnosed clinically by oral medicine specialists; venous blood samples were analyzed for thyroid stimulating hormone (TSH), free triiodothyronine (fT3), total thyroxine (fT4), thyroglobulin, anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-TG) levels. Results: Fifty patients with RAS aged between 18-42 years (28.5±5.8) and 50 healthy volunteers aged 19-45 years (27.3±5.4) participated. In RAS patients, fT3 and TSH levels were significantly higher (P=0.031, P=0.706); however, fT4 level was lower in the RAS group (P=0.447). Anti TG and anti-TPO levels were significantly higher in the RAS group (P=0.008, P=0.067). Conclusion: Our study showed that ATD prevalence was significantly higher in RAS patients. Based on this study, we recommend assessment of thyroid hormones and antibodies in RAS patients.

Keywords: recurrent aphthous stomatitis, thyroid antibodies, thyroid hormone, thyroid autoimmune disease

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Authors: Ghada E. Esheba, Ghadeer F. Alharthi, Duaa A. Alhejaili, Rawan E. Hudairy, Wafaa A. Altaezi, Raghad M. Alhejaili

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Background: Inflammatory bowel disease (IBD) consists of two specific gastrointestinal disorders: ulcerative colitis (UC) and Crohn's disease (CD). Despite their distinct natures, these two diseases share many similar etiologic, clinical and pathological features, as a result, their accurate differential diagnosis may sometimes be difficult. Correct diagnosis is important because surgical treatment and long-term prognosis differ from UC and CD. Aim: This study aims to study the characteristic clinicopathological features which help in the differential diagnosis between UC and CD, and assess the disease activity in ulcerative colitis. Materials and methods: This study was carried out on 50 selected cases. The cases included 27 cases of UC and 23 cases of CD. All the cases were examined using H& E and immunohistochemically for bcl-2 expression. Results: Characteristic features of UC include: decrease in mucous content, irregular or villous surface, crypt distortion, and cryptitis, whereas the main cardinal histopathological features seen in CD were: epitheloid granuloma, transmural chronic inflammation, absence of mucin depletion, irregular surface, or crypt distortion. 3 cases of UC were found to be associated with dysplasia. UC mucosa contains fewer Bcl-2+ cells compared with CD mucosa. Conclusion: This study using multiple parameters such clinicopathological features and Bcl-2 expression as studied by immunohistochemical stain, helped to gain an accurate differentiation between UC and CD. Furthermore, this work spotted the light on the activity and different grades of UC which could be important for the prediction of relapse.

Keywords: Crohn's disease, dysplasia, inflammatory bowel disease, ulcerative colitis

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Authors: Lawong Damian Bernsah

Abstract:

Neglected Tropical Diseases (NTDs) are most predominant among the poor and rural populations and are endemic in 149 countries. These diseases are the most prevalent and responsible for infecting 1.4 billion people worldwide. There are 17 neglected tropical diseases recognized by WHO that constitute the fourth largest disease health and economic burden of all communicable diseases. Five of these 17 diseases are considered for the cost analysis of this paper: lymphatic filariasis, onchocerciasis, trachoma, schistosomiasis, and soil transmitted helminth infections. WHO has proposed a roadmap for eradication and elimination by 2020 and treatments have been donated through the London Declaration by pharmaceutical manufacturers. The paper estimates the cost of NTD control programme and elimination for each NTD disease and total in Nigeria. This is necessary as it forms the bases upon which programme budget and expenditure could be based. Again, given the opportunity cost the resources for NTD face it is necessary to estimate the cost so as to provide bases for comparison. Cost of NTDs control and elimination programme is estimated using the population at risk for each NTD diseases and for the total. The population at risk is gotten from the national master plan for the 2015 - 2020, while the cost per person was gotten for similar studies conducted in similar settings and ranges from US$0.1 to US$0.5 for Mass Administration of Medicine (MAM) and between US$1 to US$1.5 for each NTD disease. The combined cost for all the NTDs was estimated to be US$634.88 million for the period 2015-2020 and US$1.9 billion for each NTD disease for the same period. For the purpose of sensitivity analysis and for robustness of the analysis the cost per person was varied and all were still high. Given that health expenditure for Nigeria (% of GDP) averages 3.5% for the period 1995-2014, it is very clear that efforts have to be made to improve allocation to the health sector in general which is hoped could trickle to NTDs control and elimination. Thus, the government and the donor partners would need to step-up budgetary allocation and also to be aware of the costs of NTD control and elimination programme since they have alternative uses. Key Words: Neglected Tropical Disease, Cost Analysis, NTD Programme Control and Elimination, Cost per Person

Keywords: Neglected Tropical Disease, Cost Analysis, Neglected Tropical Disease Programme Control and Elimination, Cost per Person

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

Abstract:

Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Oluwaponmile D. Alao

Abstract:

In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Getachew Tilahun, Oluwole Makinde, David Malonza

Abstract:

We propose and analyze a non-linear mathematical model for the transmission dynamics of pneumonia disease in a population of varying size. The deterministic compartmental model is studied using stability theory of differential equations. The effective reproduction number is obtained and also the local and global asymptotically stability conditions for the disease free and as well as for the endemic equilibria are established. The model exhibit a backward bifurcation and the sensitivity indices of the basic reproduction number to the key parameters are determined. Using Pontryagin’s maximum principle, the optimal control problem is formulated with three control strategies; namely disease prevention through education, treatment and screening. The cost effectiveness analysis of the adopted control strategies revealed that the combination of prevention and treatment is the most cost effective intervention strategies to combat the pneumonia pandemic. Numerical simulation is performed and pertinent results are displayed graphically.

Keywords: cost effectiveness analysis, optimal control, pneumonia dynamics, stability analysis, numerical simulation

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Authors: Francky Fouedjio

Abstract:

Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Muhammad Kamran Hanif Khan, Fahad Mushtaq

Abstract:

Globally, the death rate from cardiovascular disease has risen over the past 20 years, but especially in low and middle-income countries (LMICS), reports the World Health Organization (WHO). Around 17.5 million deaths, or 31% of all deaths worldwide in 2012, were attributed to CVD, 80% of which occurred in low- and middle-income nations, and eighty five percent of all worldwide disability is attributable to cardiovascular disease. This study assessed the dietary habit and Cardiovascular Risk factors among the patients of coronary artery disease against matched controls. The research was a case-control study. Sample size for this case-control study was 410 CAD cases and 410 healthy controls. The case-control ratio was 1:1. Patients diagnosed with coronary artery disease were recruited from the outpatient departments and emergency rooms of four hospitals in Pakistan. The ages of people who were diagnosed with coronary artery disease were not significantly different from (mean 57.97 7.39 years) the healthy controls (mean 57.12 6.73 years). In order to determine the relationship between food consumption and the two binary outcomes, logistic regression analysis was carried out. Chicken (0.340 (0.245-0.47), p-value 0.0001), beef (0.38 (0.254-0.56), p-value 0.0001), eggs (0.297 (0.208-0.426), p-value 0.0001), and junk food (0.249 (0.167-0.372), p-value 0.0001)) were protective, while yogurt consumption more than twice weekly was risk. Conclusion: In conclusion, poor dietary habits are closely linked to the risk of CAD. Investigations based on dietary trends offer vital and practical knowledge about societal patterns.

Keywords: dietary habbits, cardiovasculardisease, CVD risk factors, hypercholesterolemia

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