• Safety and Health at Work
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• v.7 no.2
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• pp.102-110
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• 2016

Background: This work presents an experimental and modeling study of the electromagnetic environment in the vicinity of a high voltage substation located in eastern Algeria (Annaba city) specified with a very high population density. The effects of electromagnetic fields emanating from the coupled multi-lines high voltage power systems (MLHV) on the health of the workers and people living in proximity of substations has been analyzed. Methods: Experimental Measurements for the Multi-lines power system proposed have been conducted in the free space under the high voltage lines. Field's intensities were measured using a referenced and calibrated electromagnetic field meter PMM8053B for the levels 0 m, 1 m, 1.5 m and 1.8 m witch present the sensitive's parts as organs and major functions (head, heart, pelvis and feet) of the human body. Results: The measurement results were validated by numerical simulation using the finite element method and these results are compared with the limit values of the international standards. Conclusion: We project to set own national standards for exposure to electromagnetic fields, in order to achieve a regional database that will be at the disposal of partners concerned to ensure safety of people and mainly workers inside high voltage electrical substations.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• Journal of Applied Biological Chemistry
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• v.65 no.3
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• pp.159-166
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• 2022

Epicoccum nigrum produces epipyrone A (orevactaene), a yellow polyketide pigment. Its biosynthetic gene cluster was previously characterized in E. nigrum KACC 40642. The YES liquid culture of this strain revealed high-level production of epicoccamide (EPC), with an identity that was determined using liquid chromatography-mass spectrometry analysis and molecular mass search using the SuperNatural database V2 webserver. The production of EPC was further confirmed by compound isolation and nuclear magnetic resonance spectroscopy. EPC is a highly reduced polyketide with tetramic acid and mannosyl moieties. The EPC structure guided us to localize the hypothetical EPC biosynthetic gene cluster (BGC) in E. nigrum ICMP 19927 genome sequence. The BGC contains genes encoding highly reducing (HR)-fungal polyketide synthase (fPKS)-nonribosomal peptide synthetase (NRPS), glycosyltransferase (GT), enoylreductase, cytochrome P450, and N-methyltrasnferase. Targeted inactivation of the HR-fPKS-NRPS and GT genes abolished EPC production, supporting the successful localization of EPC BGC. This study provides a platform to explore the hidden biological activities of EPC, a bolaamphiphilic compound.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.12
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• pp.5089-5095
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• 2014

Background: As a common and essential contrast medium at present, gadobenate dimeglumine has shown better performance than some other agents when applied to Breast Magnetic Resonance Imaging Screening (Breast MRI Screening). Nevertheless, reports on the diagnostic performance of these two mediums (gadobenate dimeglumine and gadopentetate dimeglumine) are not completely consistent. Objective: To assess the diagnostic value of gadobenate dimeglumine and gadopentetate dimeglumine for Breast MRI Screening in patients suffering from breast cancer and to provide more convinced evidence to guide clinical practice in terms of appropriate contrast agents. Data Sources and Review Methods: Original articles in English and Chinese published before January 2013 were selected from available databases (The Cochrane Library, PUBMED, EMBASE, Chinese Biomedical Literature Database, Chinese Scientific Journals Full-text Database, Chinese Journal Full-text). The criteria for inclusion and exclusion were based on the standard for diagnosis tests. Meta-Disc software (Version 1.4) was used for data analysis. Then, the area under curve (AUC) of SROC and the spearman rank correlation of sensitivity against (1-specificity) were calculated. Results: Total of 17 researches involving 1934 patients were included. The pooled sensitivity of gadobenate dimeglumine and gadopentetate dimeglumine were 0.99 (0.97, 1.00) and 0.93 (0.88, 1.00) respectively. The pooled specificity for these two contrast agents were 0.924 (0.902, 0.943) and 0.838 (0.817, 0.858) respectively, and the AUC of SROC curve were 0.9781 and 0.9215 respectively. Conclusions: Gadobenate dimeglumine can be regarded as a more effective and feasible contrast medium for Breast MRI Screening. At least 5% differences in diagnostic performance are usually considered as clinically relevant.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.403-410
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• 2015

Since 1970, MRI has greatly been developing in terms of strength of magnetic field, the number of receipt channels, and short time of examination. With the development of digital systems and wireless network, hospitals have also acquired, saved, and managed digital images taken by various kinds of medical imaging equipment. However, domestic universities fail to provide practice training course independently thanks to expensive practice equipment and high maintenance cost, and rely on clinical training. Therefore, this study developed a MR patient diagnosis program based on Windows PC to help out students before their working in clinical filed. The designed Relational Database of MRI Simulator is made up of seven tables according to functions and data characteristics. Regarding the designed patient information, each stepwise function was classified by the patient registration method in clinical field. In addition, on the assumption of the basic information for diagnosis, each setting and content were classified. The menu by execution step was arrayed on the left side for easy view. For patient registration, a patient's name, gender, unique ID, birth date, weight, and other types of basic information were entered, and the patient's posture and diagnosis direction were set up. In addition, the body regions for diagnosis and Pulse Sequence were listed for selection. Also, Protocol name and other additional factors were allowed to be entered. The final window was designed to check diagnosis images, patient information, and diagnosis conditions. By learning how to enter patient information and change diagnosis conditions in this program, users will be able to understand more theories and terms learned in practice and thereby to shorten their learning time in actual clinical work.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.144-150
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• 2014

Purpose : Intramedullary spinal lesions in the conus medullaris (CM), including tumors and vascular lesion, are rarely reported. We reported various MR features of intramedullary spinal cord lesions involving the CM including ependymoma, hemangioblastomas, dermoid cyst, ventriculus terminalis and spinal AVF and tried to discuss them for differential diagnosis. Materials and Methods: Six patients (male: female = 4:2, mean age = 44.3 year old) were enrolled from the clinical database of our institute from 2004 to 2010 and their radiological images and clinical symptoms were reviewed retrospectively. All patients had taken initial and postoperative MRI with contrast enhancement using gadopentate dimeglumine (Gd-DTPA). These images were analyzed by tumor size, location, signal intensity relative to the spinal cord, vascular flow voids, syrinx or cyst, edema and enhancement pattern. Results: Contrast enhancement was seen in all intramedullary masses. An eccentric enhancing nodule was noted in two hemangioblastomas and unusual peripheral rim enhancement with septation was seen in ventriculus terminalis. Patchy enhancement of the CM was observed in spinal arteriovenous fistula (AVF). Extensive cord edema adjacent to the intramedullary lesions was seen in four cases and syrinx was noted in three cases. Vascular signal voids were found in two hemangioblastomas and one spinal AVF. Conclusion: In evaluation of intramedullary spinal lesions in the CM, it is necessary to consider these unusual MR findings and discriminate various pathologies with prudence and caution.

• Journal of Preventive Medicine and Public Health
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• v.49 no.4
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• pp.230-239
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• 2016

Objectives: We aimed to evaluate the effect of geographic units of analysis on measuring geographic variation in medical services utilization. For this purpose, we compared geographic variations in the rates of eight major procedures in administrative units (districts) and new areal units organized based on the actual health care use of the population in Korea. Methods: To compare geographic variation in geographic units of analysis, we calculated the age-sex standardized rates of eight major procedures (coronary artery bypass graft surgery, percutaneous transluminal coronary angioplasty, surgery after hip fracture, knee-replacement surgery, caesarean section, hysterectomy, computed tomography scan, and magnetic resonance imaging scan) from the National Health Insurance database in Korea for the 2013 period. Using the coefficient of variation, the extremal quotient, and the systematic component of variation, we measured geographic variation for these eight procedures in districts and new areal units. Results: Compared with districts, new areal units showed a reduction in geographic variation. Extremal quotients and inter-decile ratios for the eight procedures were lower in new areal units. While the coefficient of variation was lower for most procedures in new areal units, the pattern of change of the systematic component of variation between districts and new areal units differed among procedures. Conclusions: Geographic variation in medical service utilization could vary according to the geographic unit of analysis. To determine how geographic characteristics such as population size and number of geographic units affect geographic variation, further studies are needed.

• Journal of Korean Medicine Rehabilitation
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• v.26 no.2
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• pp.61-74
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• 2016

Objectives The purpose of this research is to investigate recent clinical studies of Temporomandibular Joint Disorder in China. Methods We investigated recent clinical studies of Oriental Medicine therapies in traditional Chinese medical journals for Temporomandibular Joint Disorder through CNKI search. 20 clinical articles published from 2010 to 2015 were analyzed. This study examined the authors, published years, types of study designs, criteria for diagnosis and evaluation, periods, purposes of study and classified articles by techniques of treatment. Results Most of articles were classified as RCT. TMD was diagnosed by symptoms in a high proportion of articles. The criteria for evaluation that most frequently used were grading scale, but there was a lack of objectivity. The techniques of treatment were Tuina, acupuncture, herb medicine, electrotherapy, splint, PNF, congnitive behavior therapy, laser therapy, magnetic therapy. Conclusions In order to develop treatment of TMD in the Korean medicine, clinical studies for various therapies on a high level and cooperative studies between medical communities are needed.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.