Prevalence and associated factors of intestinal parasitic infections among asymptomatic food handlers working at Haramaya University cafeterias, eastern Ethiopia

Dadi Marami,Konjit Hailu,Moti Tolera 대한직업환경의학회 2018 대한직업환경의학회지 Vol.30 No.-

Background: Intestinal parasitic infections are major public health problems worldwide, with high prevalence in low income countries where substandard food hygiene practices are common. Asymptomatic food handlers with poor personal hygiene could be potential sources of parasitic infections. This study was aimed to assess the prevalence of intestinal parasitic infections and associated factors among asymptomatic food handlers working at Haramaya University cafeterias, eastern Ethiopia. Methods: A cross-sectional study was conducted among asymptomatic food handlers working at Haramaya University cafeterias from August 2015 to January 2016. Population proportion to size allocation and systematic random sampling techniques were used to identify the study participants. Stool samples were collected and examined simultaneouly using direct and modified formol ether concentration wet smear techniques. Data were entered and analyzed using SPSS version 20.0 software. Logistic regressions were applied to assess association between independent variable and intestinal parasitic infections. Statistical significance was declared at a p-value less than 0.05. Results: A total of 417 asymptomatic food handlers were enrolled in this study. Of these, females comprised 79.4%. Large proportion (39.3%) of food handlers were in the age group of 31–40 years. The overall prevalence of intestinal parasitic infections was 25.2% (95% CI: 18.3, 29.6). Entamoeba histolytica/ dispar (46.7%) and A. lumbricoides (14.3%) were the most frequent isolates. Having no formal education [AOR: 2.13, 95% CI: 1.24, 3.67], monthly income of less than 45.7 USD [AOR: 3.86, 95% CI: 1.62, 9.20], lack of hand washing after the use of the toilet with soap [AOR: 2.43, 95% CI: 1.22, 4.86] and untrimmed fingernails [AOR: 3.31, 95% CI: 1.99, 5.49] have significant association with intestinal parasitic infections. Conclusions: The high prevalence of intestinal parasitic infections in this study highlights the importance of food handlers as probable sources of parasitic infections. Public health measures and sanitation programs should be strengthened to control the spread of intestinal parasitic infections.

Harmonics Elimination in a Multilevel Inverter with Unequal DC Sources Using a Genetic Algorithm

Iranaq, Ali Reza Marami,Kouhshahi, Mojtaba Bahrami,Kouhshahi, Mehdi Bahrami,Sharifian, Mohammad Bagher Bannae,Sabahi, Mehran Journal of International Conference on Electrical 2012 Journal of international Conference on Electrical Vol.1 No.1

In this paper, an optimal solution to the harmonic reduction problem in a cascaded multilevel inverter with non-equal DC sources using a genetic algorithm (GA) is presented. Switching angles are generated for different values of modulation index by the proposed algorithm, considering minimum voltage total harmonic distortion (THD) whereas selected harmonics are controlled within the allowable limits at all desired modulation indices including the point of discontinuity. Results are stored as a look-up table to be used to control the inverter for a certain operating point. The computed angles are used in a simulated circuit in Matlab\Simulink to validate the results.

A New Switching Pattern for Multilevel Inverter Based on Selective Harmonic Elimination Using Genetic Algorithm

Fekari, Seyyed Amir,Iranaq, Ali Reza Marami,Sabahi, Mehran Journal of International Conference on Electrical 2014 Journal of international Conference on Electrical Vol.3 No.3

In this paper, a new switching pattern is presented for multilevel inverters. With changing off-angel of each switch, the on time interval of all switches will approximately be equal and then the lifetime of inverter will increase, also using this method can reduce electrical stress on switches in higher levels of inverter. Switching angels as for desired modulation index are calculated using genetic algorithm whereas selective harmonics are controlled within the allowable range. The computed angels are simulated in Matlab/Simulink for respective circuits to validate the results.

A Novel Algorithm for Restricting the Complexity of Virus Typing via PCR-RFLP Gel Electrophoresis

Anastasios N. Delopoulos,Christos F. Maramis 대한의용생체공학회 2011 Biomedical Engineering Letters (BMEL) Vol.1 No.4

Purpose PCR-RFLP gel electrophoresis is a popular method for virus typing (i.e., for identifying the types of a virus that have infected a biological sample), which has been automated recently owing to a computerized typing methodology. However, even with the help of this methodology, the PCRRFLP method suffers from low throughput, when compared to other typing methods. In this paper, we tackle this issue by introducing a novel algorithm for conducting the most computationally demanding phase of the aforementioned typing methodology (testing phase). Methods The testing phase requires the execution of an optimization task on a 1d signal (intensity profile) for a number of type combinations. The introduced algorithm first partitions the signal into individually treatable segments. This parcels the optimization task into a set of more lightweight subproblems, thus reducing the computational effort required for testing a single intensity profile. Then, it eliminates any duplicate optimization subproblems among the type combinations. This way, the computational complexity of the testing phase is significantly restricted. Results A dataset of 70 natural samples infected by the human papillomavirus are employed to evaluate the complexity and the accuracy of the proposed algorithm. The obtained results are very promising, indicating that the proposed algorithm is able to octuple or more the speed of virus typing via the PCR-RFLP method, without essentially compromising the accuracy of the employed typing methodology. Conclusions The proposed algorithm can be seamlessly integrated into the state-of-the-art typing methodology to significantly increase the throughput of virus typing via the PCR-RFLP method, without harming the methodology’s accuracy. Moreover, it has the potential to be employed in real-time typing applications - one such application has just been reported.

BACH: Grand challenge on breast cancer histology images

Aresta, Guilherme,Araú,jo, Teresa,Kwok, Scotty,Chennamsetty, Sai Saketh,Safwan, Mohammed,Alex, Varghese,Marami, Bahram,Prastawa, Marcel,Chan, Monica,Donovan, Michael,Fernandez, Gerardo,Zeineh, J Elsevier 2019 Medical image analysis Vol.56 No.-

<P><B>Abstract</B></P> <P>Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The BACH challenge was organized to push forward methods for automatic classification of breast cancer biopsies using clinical hematoxylin-eosin stained histopathological images. </LI> <LI> A large public dataset, composed of 400 microscopy images and 30 whole-slide images, was specifically compiled for the BACH challenge. </LI> <LI> A total of 64 methods were submitted, out of 677 registration, and a detailed comparative analysis was carried out for the methods with higher accuracy scores. </LI> <LI> Several submitted algorithms performed better than the state-of-the-art in terms of accuracy (top score of 87%). </LI> <LI> Convolutional neural networks dominated the submissions, and was the method of choice in the algorithm that won the challenge. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>