EAI Endorsed Transactions on Bioengineering and Bioinformatics https://publications.eai.eu/index.php/bebi <p>EAI Endorsed Transactions on Bioengineering and Bioinformatics (BEBI), formerly EAI Endorsed Transactions on Ambient Systems, aims to build intelligent synergies and systems among Bioinformatics, Computational Biology, Bioengineering, and Biomedicine these complementary disciplines that hold great promise for the advancement of research and development in complex medical and biological systems, agriculture, environment, public health, drug design. Research and development in these areas are impacting the science and technology in fields such as medicine, food production, forensics, etc. by advancing fundamental concepts in molecular biology, helping us understand living organisms at multiple levels, developing innovative implants and bio-prosthetics, and improving tools and techniques for the detection, prevention, and treatment of diseases. The BEBI will provide a common platform for cross-fertilizing ideas and shaping knowledge and scientific achievements by bridging these important and complementary disciplines into an interactive and attractive forum.</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Bioengineering and Bioinformatics 2709-4111 <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> An Approach to Improve the Water Quality on Industrial Effluent by Phytoremediation with Water Hyacinth (Eichhornia Crassipes) https://publications.eai.eu/index.php/bebi/article/view/1377 <p>INTRODUCTION: Industries and factories are the backbone of any nation to increase the economy through its productivity and develop the county globally. But the wastages and sewages produced by each industry are creating pollution and make cleanliness environment on the society.</p><p>OBJECTIVES: The recycling process and decontamination process of that industrial effluent is a challenging task for all the industries in and around the world. Ancient techniques are followed in industrial effluent cleaning processes but the output of those is not sufficient in terms of recycling.</p><p>METHODS: Phytoremediation is the new technique used to control industrial effluent with the help of water hyacinth available in the water places. This paper discussed all possible solutions of industrial effluent removal along with water quality improvement techniques. Decontamination of the industrial effluent using water hyacinth is mainly dealt in leaves, stems, and root parts. There are only water hyacinth is used to remove industrial effluent, no other products like powder, liquid plants, and other chemicals are used.</p><p>RESULTS: Total duration of 21 days has to be considered for the growth of water hyacinth used in water places to monitor the recycling of industrial effluent at different parts. A water quality test has been conducted to identify the level of contaminants in the Industrial effluent. Furthermore, BOD (Biological oxygen demand), COD (Chemical oxygen demand), TDS (Total dissolved solids), TSS (Total suspended solids), Turbidity, and chloride levels are taken as a parameter from the industrial effluents.</p><p>CONCLUSION: Every 24hours, for continuous 3-5days the color of effluent has been notified to predict the changes. Turbidity and other impurities are noted by the effects of the water hyacinth used as results.</p> M R Sundarakumar S. Sankar Digvijay Pandey Binay Kumar Pandey A Shaji George Bhishma Karki Pankaj Dadeech Copyright (c) 2022 EAI Endorsed Transactions on Bioengineering and Bioinformatics 2021-10-12 2021-10-12 1 4 e1 e1 10.4108/eai.12-10-2021.171251 Influences of seasonal and demographic factors on the COVID-19 pandemic dynamics https://publications.eai.eu/index.php/bebi/article/view/1379 <p>INTRODUCTION: The number of COVID-19 cases per capita (accumulated (CC) and daily (DCC)) are important characteristics of the pandemic dynamics indicating the effectiveness of quarantine, testing, and vaccination. They also indicate the appearance of new waves (e.g., caused by new coronavirus strains) and may be the result of various demographic and seasonal factors.</p><p>OBJECTIVES: We investigate the influence of the volume and the density of population and the urbanisation level on the CC values accumulated in European countries and regions of Ukraine at the end of June 2021 and the impact of seasonal factors on the DCC values by comparing their dynamics in the spring and summer of 2020 and 2021 for northern and southern regions.</p><p>METHODS: The influence of demographic factors on CC values was investigated with the use of linear regression. Since DCC values are very random and demonstrate some weekly period, we have used the 7-days smoothing proposed before. The second year of the pandemic allows us to compare its dynamics in the spring and the summer of 2020 with the same period in 2021 and investigate the influence of seasonal factors. We have chosen some northern countries and regions: Ukraine, EU, the UK, USA and some countries located in the tropical zone and Southern Hemisphere: India, Brazil, South Africa and Argentina. The dynamics in these regions was compared with the global one.</p><p>RESULTS: The accumulated number of cases per capita CC does not depend on the demographic factors used for analysis, although it may differ by about 4 times for different regions of Ukraine and more than 9 times for different European countries. The number of COVID-19 per capita registered in Ukraine is comparable with the same characteristic in other European countries but much higher than in China, South Korea and Japan. Some seasonal similarities are visible for global dynamics, EU and South Africa. Before July 2020, the southern countries demonstrated exponential growth, but northern regions showed some stabilization trends.</p><p>CONCLUSION: The CC values in Europe do not show any visible dependence on the volume of population, its density and the urbanization level. More or less similar seasonal behaviour of DCC values are visible for global dynamics in July and August. Unfortunately, we cannot conclude where the quarantine restrictions were the most effective since the dynamics of the pandemic are influenced by many other factors not considered in this study, in particular, the emergence of new strains and the large number of unreported cases.</p> I. Nesteruk O. Rodionov A. V. Nikitin S. Walczak Copyright (c) 2022 EAI Endorsed Transactions on Bioengineering and Bioinformatics 2021-12-08 2021-12-08 1 4 e2 e2 10.4108/eai.8-12-2021.172364 A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using Deep Learning https://publications.eai.eu/index.php/bebi/article/view/1380 <p>INTRODUCTION: Although accurate segmentation of the prostatic subregions is a crucial step for prostate cancer diagnosis, it remains a challenge.</p><p>OBJECTIVES: To propose a deep learning (DL)-based cropping pipeline to improve the performance of DL networks for segmenting the prostate’s peripheral zone.</p><p>METHODS: A U-net network was trained to crop the area around the peripheral zone on MRI in order to reduce the class imbalance between foreground and background pixels. The DL-cropping was compared with the standard center-cropping using three segmentation networks.</p><p>RESULTS: The DL-cropping improved significantly the segmentation performance in terms of Dice score, Sensitivity, Hausdorff Distance, and Average Surface Distance, for all three networks. The improvement in Dice Score was 34%, 13% and 16% for the U-net, Dense U-net and Bridged U-net, respectively. CONCLUSION: For all the evaluated networks, the proposed DL-cropping technique outperformed the standard center-cropping.</p> Dimitris Zaridis Eugenia Mylona Nikolaos Tachos Kostas Marias Manolis Tsiknakis Dimitios I. Fotiadis Copyright (c) 2022 EAI Endorsed Transactions on Bioengineering and Bioinformatics 2022-02-24 2022-02-24 1 4 e3 e3 10.4108/eai.24-2-2022.173546 Determination of VEGF and CXCR4 in Tumor and Peritumoral Tissue of Patients with Breast Cancer as a Predictive Factor https://publications.eai.eu/index.php/bebi/article/view/1381 <p>INTRODUCTION: Despite the obvious progress in the field of diagnosis and therapy, further measures are needed to increase the effectiveness of treatment and reduce morbidity and mortality from breast cancer.</p><p>OBJECTIVES: To study the influence of peritumoral tissue on the growth and development of the tumor itself.</p><p>METHODS: An immunofluorescence method was used to determine the protein expression of VEGF and CXCR-4 in tumor and peritumoral tissue.</p><p>RESULTS: Peritumoral tissue is not only a passive factor, but actively participates in the process of tumor growth and development, as well as in the processes of recurrence and metastasis.</p><p>CONCLUSION: Markers of neoangiogenesis in tumor and peritumoral tissue such as protein expression of VEGF and CXCR-4 receptors may serve as reliable predictors of disease outcome in breast cancer patients, which may provide useful suggestions in treatment choices.</p> Danijela Cvetković Aleksandar Cvetković Danijela Nikodijević Jovana Jovankić Milena Milutinović Vladislava Stojić Nataša Zdravković Slobodanka Mitrović Copyright (c) 2022 EAI Endorsed Transactions on Bioengineering and Bioinformatics 2022-03-25 2022-03-25 1 4 e4 e4 10.4108/eai.25-3-2022.173714 Estimating animal pose using deep learning: a trained deep learning model outperforms morphological analysis https://publications.eai.eu/index.php/bebi/article/view/1382 <p>INTRODUCTION: Analyzing animal behavior helps researchers understand their decision-making process and helper tools are rapidly becoming an indispensable part of many interdisciplinary studies. However, researchers are often challenged to estimate animal pose because of the limitation of the tools and its vulnerability to a specific environment. Over the years, deep learning has been introduced as an alternative solution to overcome these challenges.</p><p>OBJECTIVES: This study investigates how deep learning models can be applied for the accurate prediction of animal behavior, comparing with traditional morphological analysis based on image pixels.</p><p>METHODS: Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, is used to record videos with a wide range of animal behavior. Recorded videos contain two insects: a walking red imported fire ant (Solenopsis invicta) and a walking fruit fly (Drosophila melanogaster). Body parts such as the head, legs, and thorax, are estimated by using an open-source deep-learning toolbox. A deep learning model, ResNet-50, is trained to predict the body parts of the fire ant and the fruit fly respectively. 500 image frames for each insect were annotated by humans and then compared with the predictions of the deep learning model as well as the points generated from the morphological analysis.</p><p>RESULTS: The experimental results show that the average distance between the deep learning-predicted centroids and the human-annotated centroids is 2.54, while the average distance between the morphological analysis-generated centroids and the human-annotated centroids is 6.41 over the 500 frames of the fire ant. For the fruit fly, the average distance of the centroids between the deep learning- predicted and the human-annotated is 2.43, while the average distance of the centroids between the morphological analysis-generated and the human-annotated is 5.06 over the 477 image frames.</p><p>CONCLUSION: In this paper, we demonstrate that the deep learning model outperforms traditional morphological analysis in terms of estimating animal pose in a series of video frames.</p> S. Lee J. Banzon K. Le D. H. Kim Copyright (c) 2022 EAI Endorsed Transactions on Bioengineering and Bioinformatics 2022-04-22 2022-04-22 1 4 e5 e5 10.4108/eai.22-4-2022.173951