Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.

Lung cancer is a leading cause of cancer death in the world. Key to survival of patients is early diagnosis. Studies have demonstrated that screening high risk patients with Low-dose Computed Tomography (CT) is invaluable for reducing morbidity and mortality. Computer Aided Diagnosis (CADx) systems can assist radiologists and care providers in reading and analyzing lung CT images to segment, classify, and keep track of nodules for signs of cancer. In this thesis, we propose a CADx system for this purpose. To predict lung nodule malignancy, we propose a new deep learning framework that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to learn best in-plane and inter-slice visual features for diagnostic nodule classification. Since a nodule\u27s volumetric growth and shape variation over a period of time may reveal information regarding the malignancy of nodule, separately, a dictionary learning based approach is proposed to segment the nodule\u27s shape at two time points from two scans, one year apart. The output of a CNN classifier trained to learn visual appearance of malignant nodules is then combined with the derived measures of shape change and volumetric growth in assigning a probability of malignancy to the nodule. Due to the limited number of available CT scans of benign and malignant nodules in the image database from the National Lung Screening Trial (NLST), we chose to initially train a deep neural network on the larger LUNA16 Challenge database which was built for the purpose of eliminating false positives from detected nodules in thoracic CT scans. Discriminative features that were learned in this application were transferred to predict malignancy. The algorithm for segmenting nodule shapes in serial CT scans utilizes a sparse combination of training shapes (SCoTS). This algorithm captures a sparse representation of a shape in input data through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation. The discriminative nature of sparse representation, affords us the opportunity to compare nodules\u27 variations in consecutive time points and to predict malignancy. Experimental validations of the proposed segmentation algorithm have been demonstrated on 542 3-D lung nodule data from the LIDC-IDRI database which includes radiologist delineated nodule boundaries. The effectiveness of the proposed deep learning and dictionary learning architectures for malignancy prediction have been demonstrated on CT data from 370 biopsied subjects collected from the NLST database. Each subject in this database had at least two serial CT scans at two separate time points one year apart. The proposed RNN CAD system achieved an ROC Area Under the Curve (AUC) of 0.87, when validated on CT data from nodules at second sequential time point and 0.83 based on dictionary learning method; however, when nodule shape change and appearance were combined, the classifier performance improved to AUC=0.89

Pneumatic Rupture of Rectosigmoid; a Case Report

Pneumatic rectosigmoid rapture is usually occurred following the inappropriate fun by direct entering a high volume of the air through the pneumatic device to the anus. Such an event was reported for the first time in 1904 by Stone. Diagnosis and treatment of such injuries are often delayed because of some social limitations and preventing the patient form explaining the event. Colon sigmoid rupture and pneumoperitoneum is one of the most dangerous and life treating complications of entering a high volume of the air to the rectum in a short time. There are only a few reports regarding the similar cases. Here, a case of pneumatic rectosigmoid rapture was reported in a 53 year-old male following an inappropriate fun

Ranking different barriers influencing on media privatization

For years, there have been growing interests on cost reduction for products and services. Privatization is considered as one of the most important techniques to increase relative efficiencies of publically held firms. In this paper, we present an empirical investigation to rank important barriers on privatization of television (TV) media industry in Iran. The proposed study of this paper designs and distributes a questionnaire using a sample of 234 out of 600 graduate students who were enrolled in media communication studies. The survey considers social, cultural, economic as well as rules and regulations factors influencing privatization of TV media industry. The survey uses the ranking method presented by Cook and Kress (1990) [Cook, W. D., & Kress, M. (1990). A data envelopment model for aggregating preference rankings. Management Science, 36(11), 1302-1310.]. The results of the investigation indicate rules and regulations are the most important barriers on privatization of Iranian TV followed by cultural, social and economic factors

Introducing PACT Model of Transformative Persuasion: Re-emergence of Learning Approach to Persuasive Communications

The learning approach to persuasion was considered obsolete following the emergence of new paradigms such as cognitive and constructive approaches. However, according to the evolutions of learning theories and especially the re-emergence of the connectivism paradigm, mainly due to what new technologies have provided, the learning approach to persuasion seems to have reappeared as a powerful approach that has a lot to offer yet. Based on research conducted on transformative learning patterns and algorithms, this paper investigates: i) the applicability of using the patterns and algorithms as well as techniques developed in the transformative learning approach for transformative persuasion, ii) how media can be used in the transformation process. The components of a persuasive transformation model, the factors involved, and major elements of each factor are extracted by constructivist grounded theory (CGT), which is used for theory building, accumulating knowledge and experiences of scholars, practitioners, and experts in adult learning. We show how media can use these factors and elements and notions and techniques developed in transformative learning for the persuasive transformation of their respective audience. Borrowing the concepts of transformative learning concerning the states of mind of the adult students in different stages of the transformation process, we suggest how media can appropriately act in each stage to facilitate a transformation through persuasion

The Effect of Social Responsibility and Corporate image on Enhancing the Brand Equity

Customers are one of the pillars of success in organizations and have been studied from different aspects. The aim of this study is scrutinizing the factors influencing brand equity in the tobacco industry in Tehran. Therefore, by referring to previous studies, dimensions and components of associated with variables have been identified and a standard questionnaire based on these variables has been used. The validity of the questionnaire has been approved by the elites of management and the reliability of them has been calculated through the software. The statistical population of research consisted of all customers of the tobacco industry in Tehran. In order to determining the sample size the Cochran formula, to the extent of 384 individuals, has been used. In order to test the research hypotheses Structural equation modeling through Lisrel software has been used. The results showed that different variables have significant impact on brand equity and customer loyalty is an important mediating factor in influencing on brand equity

Strategic planning in media organizations of Iran

Organizations with activities of all kinds are influenced by environmental conditions, and external environment is in fact the beginning point of the strategy. Strategic management is an approach resulting from fast changing age and can consider it as a view and a technique for flexible planning to fast changes, and balanced score card is regarded as one of the strong instruments in this zone. Balanced score card can truly plays an important role in all stages of strategic management and the efficiency of this model is considerably regarded in management performance evaluation in different organizations. However, strong instrument such as balance score card is hardly used because of long term dominance of political approaches in the management of Iran media organizations. This paper conceptualizes administrational trend of strategic planning by implementing balanced score card and we draw strategy map and determine performance indexes in a written media organization (Hamshahri Newspaper)

Propofol Pretreatment Protects Hippocampal CA1 Neurons from Ischemia-reperfusion Injury in Rat

Background: The number of brain strokes induced by ischemia has increased significantly in recent years as a result of brain vascular disorders. Some of these patients will require brain vascular surgery. Brain ischemia, large-scale bleeding, and hypoxia are all severe risks that must be avoided when using an anesthetic medicine that has the best protective benefits for the patient's brain and vascular system during the surgical process. One of the most critical pathogenic events in ischemia-reperfusion is apoptosis, and the CA1 region of the hippocampus is one of the most vulnerable parts of the brain to ischemia. Propofol is a neuroprotective intravenous anesthetic for cerebral ischemia-reperfusion (I/R) injury. Few studies have been conducted on the neuroprotective and neurobehavioral effects of propofol, and the underlying mechanism remains unclear. However, few studies have looked into the dose and injection timing of the drug to achieve neuroprotective effects. Aim: The purpose of this study was to see if propofol could protect male Wistar rat hippocampal CA1pyramidal cells from ischemia and brief overall reperfusion damage. Methods: The 18 male Wistar rats were placed into three groups: control, ischemia, and experimental. 1 hour before ischemia, 40 mg/kg propofol was given intraperitoneally. Ischemia was induced by blocking the common carotid arteries on both sides for 20 minutes. For histomorphologic alterations, the Hematoxylin-Eosin, Nissl, and TUNEL techniques were used. Results: The researchers discovered that 40mg/kg propofol has protective effects on hippocampus pyramidal neurons in ischemia/reperfusion-induced lab rats. Conclusion: Propofol can drastically reduce neuron death while also protecting them from ischemia damage

Evaluating the Behaviour of Centrally Perforated Unreinforced Masonry Walls: Applications of Numerical Analysis, Machine Learning, and Stochastic Methods

The presence of openings greatly affects the response of unreinforced masonry (URM) walls. This topic greatly attracts the attention of many researchers. Perforated unreinforced masonry (PURM) walls under in-plane loads through the truss discretization method (TDM) along with several machine learning approaches such as Multilayer perceptron (MLP), Group of Method Data Handling (GMDH), and Radial basis function (RBF) are described in this paper. A new method named Multi-pier (MP) that is fast and accurate, is used to determine the behavior of PURM walls. The results of the MP method are expressed as a ratio of lateral load-bearing capacity and initial stiffness of PURM walls to the solid wall in order to generalize the obtained results to other PURM walls. The outcomes of the MP method are employed to predict the behavior of PURM walls using various machine learning approaches. Using the validated network with suitable accuracy, empirical functions and curves are presented in an effort to provide a simplified and practical approach to assess the reduction in the load-bearing capacity and initial stiffness of PURM walls. Results indicate that the adjacent piers of opening have a remarkable impact on the overall response of the PURM wall. Finally, the ability of the MP method to conduct stochastic analysis is evaluated. Moreover, the effect of randomness in the mechanical characteristics and their spatial variation within the PURM wall is presented

Classification of organizational failure root causes producing human error

The formal study of human error is relatively recent, especially in medical domain, and is tied closely to a several other relatively new fields. Organizational root cause of human error is less considered. Despite growing social, industrial and scientific interest in the organizational causes of incidents, the concept of organizational failure and related tools are still less considered in many developing countries e.g. Iran. Also, there is few incident record-keeping in medical domain on human error. Therefore, this study draws on case study research to investigate the applicability of a European taxonomy of organizational failure in Iran, in aviation domain with a fair incident record-keeping. This case study resulted in 10 incident in-depth descriptions, which occurred during one year in a part of civil aviation due to operator error. Within each case study, an explanation building method is used to develop a tool for classifying organizational root causes. Results include 100 root causes. The distribution of organizational root causes over the main categories of the former taxonomy shows a need to add a new sub-category to improve its applicability in Iran. The new sub-category is related to culture

Mental health in high-tech system

Stress and mental health at the place of work have received great attention by researchers. In spite of technology improvement in high-tech systems, the operators face new problems, which can affect mental health. There is hardly any published research about stress or mental health in such workplaces in developing countries. This paper presents the application of the self-rating scale General Health Questionnaire (GHQ-28) to study mental health of 160 controllers working in a part of Air Traffic Control (ATC) as a high tech system in Iran. Logistic regression analysis showed that demographic variables did not exhibit a statistically significant effect on scores of the test. In order to compare mental health of these operators with general population, an exposure / non-exposure study was designed. Three age groups (less than 29 years, 30 through 39 y, and more than 40 y) were compared in exposed and non-exposed groups. The results of Fisher’s exact test showed that mental distress symptoms were significantly higher in the exposed group. There were significant job effects on somatization, anxiety and depression as well as on the total score of GHQ-28 for the two first age groups (P<.05). No significant effects of the job were found on social dysfunction symptoms in any age groups. The risk ratio of expressing depression and anxiety symptoms were more than three times greater in these operators than general population