• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.8
• /
• pp.3273-3277
• /
• 2015

Network scale up (NSU) is a novel approach to estimate parameters in hard to reach populations through asking people the number of individuals they know in their active social network. Although the method have been used in hidden populations, advantages of NSU indicate that exploration of applicability to disease like cancer might be feasible. The aim of this study was to assess the application of NSU to estimate the size of the population of breast, ovarian/cervical, prostate, and bladder cancers in the South-east of Iran. A total of 3,052 (99% response rate) Kermanian people were interviewed in 2012-2013. Based on NSU, participants were asked about if they know any people on their social network who suffered from breast, ovarian/cervical, prostate, and bladder cancers, if yes, they should enumerate them. A total of 1,650 persons living with four types of cancers (breast, ovary/cervix, prostate, and bladder) were identified by the respondents. Totally, the prevalence of people living with the four types of cancers was 228.4 per 100,000 Kermanian inhabitants. The most prevalent cancer was breast cancer, at 168.9 per 100,000, followed by prostate cancer with 116.9, ovarian/cervical cancer with 99.8, and bladder cancer with 36.3 per 100000 Kerman city population. NSU values provide a usable but not very precise way of estimating the size of subpopulations in the context of the four major cancers (breast, ovary/cervix, prostate, and bladder).

• Journal of Service Research and Studies
• /
• v.10 no.2
• /
• pp.1-15
• /
• 2020

As big data become more important socially and economically in recent years, many problems have been derived from the indiscriminate application of big data. Big data are valuable because it can figure out the meaning of informative information hidden within the data. In particular, to predict customer behavior patterns and experiences, structured data that were extracted from Customer Relationship Management (CRM) or unstructured data that were extracted from Social Network Service(SNS) can be defined as a population to interpret the data, during which many errors can occur. However, those errors are usually overlooked. In addition to data analysis techniques, some data, which should be considered in the analysis, are not included in the population and thus do not show any meaningful patterns. Therefore, this study presents the measurement and interpretation of the data generated when the cause of error in the population setting is strong relationship and interaction between people or a person and an object. In other words, it will be shown that if the relationship and interaction are strong, it is important to include data collected from the perspective of user experience and ethnography in the population by comparing various cases of big data application, through which the meaning will be derived and the best direction will be suggested.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.sup3
• /
• pp.197-200
• /
• 2016

On an international scale, the burden of cancer in absolute numbers continues to increase, mainly due to aging of population in many countries, the overall growth of the world population, changing lifestyle with increasing cancer-causing behavior, like cigarette smoking, changing dietary habits and sedentary life. Cancer is the second-leading cause of death and disability in the world, after only heart disease. Recently, increasing incidence and mortality of cancer have also become evident in the developing world. In Iraq and particularly in Basrah in the southern part of the country, the burden has definitely increased and deserves extensive research. The present paper is part of an extensive household survey carried out in Basrah in 2013. Among the objectives was to validate official cancer registration in the governorate. The cross-sectional survey had a retrospective component to inquire about the incidence of cancer and cancer-related deaths during the three years preceding the date of inquiry (2010-2012). A convenient sample of 6,999 households with 40,688 inhabitants using multistage cluster sampling was surveyed involving all urban and rural areas of Basrah. The official cancer registration activities in Basrah seemed to have attained a high level of registration coverage (70-80%) but the gap, represented by missed cases, is still high enough to criticize the system. Most of the missing cases were either not notified by treating facilities or they were diagnosed and treated outside Basrah. Using a set of parameters, the pattern of cancer was consistent based on data of the household survey and data of the cancer registry but a gap still existed in the coverage of incident cancer and mortality by cancer registration. Integrated serious steps are required to contain the risk of cancer and its burden on the patient through improving the registration process, improving early detection, diagnostic and management capabilities and encouraging scientific research to explore the hidden risk factors and possible causes of low registration coverage. Periodic household surveys seemed feasible and essential to support routine registration.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.11
• /
• pp.4913-4916
• /
• 2016

Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

• Journal of Multimedia Information System
• /
• v.6 no.4
• /
• pp.217-224
• /
• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

• Journal of Ecology and Environment
• /
• v.29 no.2
• /
• pp.175-183
• /
• 2006

The discovery of deep-sea hydrothermal vents and their ecosystems is a monumental landmark in the history of Ocean Sciences. Deep-sea hydrothermal vents are scattered along the global mid-ocean ridges and back-arc basins. Under sea volcanic phenomena related to underlying magma activities along mid-ocean ridges generate extreme habitats for highly specialized communities of animals. Multidisciplinary research efforts during past three decades since the first discovery of hydrothermal vents along the Galapagos Rift in 1977 revealed fundamental components of physiology, ecology, and evolution of specialized vent communities of micro and macro fauna. Heterogeneous regional geological settings and tectonic plate history have been considered as important geophysical and evolutionary factors for current patterns of taxonomic composition and distribution of vent faunas among venting sites in the World Ocean basins. It was found that these communities are based on primary production of chemosynthetic bacteria which directly utilize reduced compounds, mostly $H_2S$ and $CH_4$, mixed in vent fluids. Symbioses between these bacteria and their hosts, vent invertebrates, are foundation of the vent ecosystem. Gene flow and population genetic studies in parallel with larval biology began to unveil hidden dispersal barrier under deep sea as well as various dispersal characteristics cross taxa. Comparative molecular phylogenetics of vent animals revealed that vent faunas are closely related to those of cold-water seeps in general. In perspective additional interesting discoveries are anticipated particularly with further refined and expanded studies aided by new instrumental technologies.

• Asian-Australasian Journal of Animal Sciences
• /
• v.24 no.4
• /
• pp.457-462
• /
• 2011

The purpose of this study was to detect QTL for carcass quality on bovine chromosome (BTA) 6 using a high density SNP map in a Hanwoo population. The data set comprised 45 sires and their 427 Hanwoo steers that were born between spring of 2005 and fall of 2007. The steers that were used for progeny testing in the Hanwoo Improvement Center in Seosan, Korea, were genotyped with the 2,535SNPs on BTA6 that were embedded in the Illumina bovine SNP 50K chip. Four different linkage disequilibrium (LD) mapping models were applied to detect significant SNPs for carcass quality traits; the fixed model with a single marker, the random model with a single marker, the random model with haplotype effects using two adjacent markers, and the random model at hidden state. A total of twelve QTL were detected, for which four, one, three and four SNPs were detected on BTA6 under the respective models (p<0.001). Among the detected QTL, four, two, five and one QTL were associated with carcass weight, backfat thickness, longissimus dorsi muscle area, and marbling score, respectively (p<0.001). Our results suggest that the use of multiple LD mapping approaches may be beneficial in increasing power to detect QTL given a limited sample size and magnitude of QTL effect.

• Smart Structures and Systems
• /
• v.25 no.6
• /
• pp.765-778
• /
• 2020

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R² results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R² was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

• Clinical and Experimental Otorhinolaryngology
• /
• v.11 no.4
• /
• pp.233-241
• /
• 2018

Objectives. As endoscopic instrumentation, techniques and knowledges have significantly improved recently, endoscopic ear surgery has become increasingly popular. Transcanal endoscopic ear surgery (TEES) can provide better visualization of hidden areas in the middle ear cavity during congenital cholesteatoma removal. We aimed to describe outcomes for TEES for congenital cholesteatoma in a pediatric population. Methods. Twenty-five children (age, 17 months to 9 years) with congenital cholesteatoma confined to the middle ear underwent TEES by an experienced surgeon; 13 children had been classified as Potsic stage I, seven as stage II, and five as stage III. The mean follow-up period was 24 months. Recurrence of congenital cholesteatoma and surgical complication was observed. Results. Congenital cholesteatoma can be removed successfully via transcanal endoscopic approach in all patients, and no surgical complications occurred; only one patient with a stage II cholesteatoma showed recurrence during the follow-up visit, and the patient underwent revision surgery. The other patients underwent one-stage operations and showed no cholesteatoma recurrence at their last visits. Two patients underwent second-stage ossicular reconstruction. Conclusion. Although the follow-up period and number of patients were limited, pediatric congenital cholesteatoma limited to the middle ear cavity could be safely and effectively removed using TEES.

• Journal of Biomedical Engineering Research
• /
• v.42 no.5
• /
• pp.232-240
• /
• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.