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Immunoinformatics assisted design of a multi-epitope kit for detecting Cronobacter sakazakii in powdered infant formula

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Food Quality and Safety 2024年第2期8卷 322-333页

作者： Elijah K.Oladipo Olukayode I.Obisanya Victoria O.Owoeye Oyinlola G.Shittu Mautin G.Adamitonde Emmanuel C.Ikwuka Solomon O.Ojewale Adewale J.Tijani Feranmi A.Adedokun Amudatu A.Adedokun Temitope A.Oyedepo Helen Onyeaka Department of Microbiology Laboratory of Molecular BiologyImmunology and BioinformaticsAdeleke UniversityEdeOsun StateNigeria Genomics Unit Helix Biogen InstituteOgbomosoOyo StateNigeria Department of Microbiology Ladoke Akintola University of TechnologyOgbomosoOyo StateNigeria Department of Medical Laboratory Science College of MedicineUniversity of Lagos Department of Microbiology Obafemi Awolowo UniversityIle-IfeOsun stateNigeria Department of Physiology Ladoke Akintola University of TechnologyOgbomosoOyo StateNigeria Department of Human Anatomy Ladoke Akintola University of TechnologyOgbomosoOyo StateNigeria Department of Biotechnology Federal University of TechnologyAkureOndo StateNigeria Department of Biochemistry Ladoke Akintola University of TechnologyOgbomosoOyo StateNigeria Medical Laboratory Science Department Fountain UniversityOsogboOsun StateNigeria Department of Biochemistry Adeleke UniversityEdeOsun StateNigeria School of Chemical Engineering.University of Birmingham EdgbastonBirminghamUK

Objectives:Cronobacter sakazakii,formerly Enterobacter sakazakii,is an emerging ubiquitous and opportunistic foodborne pathogen with a high mortality *** has been implicated in cases of meningitis,septicaemia,and necrotizing enterocolitis among infants worldwide in association with powdered infant formula(PIF).This study was an insilico designed peptide base kit framework,using immunoinformatic techniques for quick detection of *** in *** and Methods:In the present study,a peptide-based kit was designed with a bioinformatic technique to rapidly identify *** in PIF using fhE,secY,and bcsC,which are genes responsible for its bioflm formation,as target *** antigenicity,membrane topology,and the presence of signal peptides of the target genes were analysed using VaxiJen,DeepTMHMM,and SignalP *** provide stability and fexibility to the multiple-epitope construct,the linear B cells and helper T cells(IL-4(interleukin 4)and IL-10(interleukin 10)inducing epitopes)were linked with a GSGSG linker followed by the addition of protein disulphide *** ascertain specifcity,the multi-epitope construct was molecularly docked against genes from sources other than PIF,like alfalfa,and the environment,with PIF being the highest:–***,the codons were modifed using the pET28a(+)vector,and the resultant multi-epitope construct was successfully cloned in ***:The fnal construct had a length of 486 bp,an instability index of 23.26,a theoretical pI of 9.34,a molecular weight of 16.5 kDa,and a Z-score of–***:The multi-epitope peptide construct could be a conceptual framework for creating a *** peptide-based detection kit,which has the potential to provide fast and effcient ***,there is a need for additional validation through the in vitro and in vivo techniques.

关键词： Powdered infant formula Cronobacter sakazakii bioinformatics immunoinformatics detection kit peptide-based epitopes analytical kits recombinant fusion protein peptides

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Intraoperative Detection of Surgical Gauze Using Deep Convolutional Neural Network

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Annals of Biomedical Engineering 2023年第2期51卷 352-362页

作者： Lai, Shuo-Lun Chen, Chi-Sheng Lin, Been-Ren Chang, Ruey-Feng Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University No.1 Sec.4 Roosevelt Road Taipei10617 Taiwan Division of Colorectal Surgery Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

During laparoscopic surgery, surgical gauze is usually inserted into the body cavity to help achieve hemostasis. Retention of surgical gauze in the body cavity may necessitate reoperation and increase surgical risk. Using deep learning technology, this study aimed to propose a neural network model for gauze detection from the surgical video to record the presence of the gauze. The model was trained by the training group using YOLO (You Only Look Once)v5x6, then applied to the testing group. Positive predicted value (PPV), sensitivity, and mean average precision (mAP) were calculated. Furthermore, a timeline of gauze presence in the video was drawn by the model as well as human annotation to evaluate the accuracy. After the model was well-trained, the PPV, sensitivity, and mAP in the testing group were 0.920, 0.828, and 0.881, respectively. The inference time was 11.3 ms per image. The average accuracy of the model adding a marking and filtering process was 0.899. In conclusion, surgical gauze can be successfully detected using deep learning in the surgical video. Our model provided a fast detection of surgical gauze, allowing further real-time gauze tracing in laparoscopic surgery that may help surgeons recall the location of the missing gauze. © 2022, The Author(s) under exclusive licence to Biomedical Engineering Society.

关键词： Neural network models

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Developing Algorithms for Predicting Gene-Disease Associations

Developing Algorithms for Predicting Gene-Disease Associatio...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Kaur, Jasdeep Maheshwari, B. Adnan, Myasar Mundher Kumhar, Bhupchand Maheswari, G. Sriyashwanth, A. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Hindustan Institute of Technology and Science Department of Information Technology Chennai603103 India Saveetha Institute of Medical and Technical Sciences Department of Bioinformatics Tamilnadu Chennai India

ISBN: (纸本)9798350390759

It is evident that identifying gene-disease relationships is essential to elucidate disease genomics and progression as well as aiding in developing targeted therapies. This work aims at training and testing various machine learning approaches for predicting these relations, as well as considering various datasets, and their integration involving multi-omics data. The process of data collection has been carried out from Genomic, Transcriptomic, and Clinical data stored in various data repositories and then preprocessed for feature selection and fine-tuning of the model along with the development of Supervised, Unsupervised, and Integrated Models. The models were built and tested using cross-validated data sets;model efficiency was assessed with respect to accuracy, precision, recall and F1-score. The findings showed that the models from the second group of hybrids obtained a systematically higher accuracy than models from the first and subsequent groups, especially when analyzing multiple types of 'omics data at once. Similar deployments on entirely separate datasets ensured the model's reliability and portability across different contexts. Furthermore, interpretability features and visualization modules were included into the software to improve assessment and interactivity for users and scholars. This research will be beneficial for the field of genomics since it proposes tools that will help in the better understanding of disease and the identification of genes involved in particular diseases, making it possible to start the process of targeting important genes in order to develop cures for such diseases. The study results preaciously highlight the significance of applying integrated strategies of data and analytic methodology to improving the accuracy of the prediction and implementing the personalized medicine concept. © 2024 IEEE.

关键词： Genome

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Region-based Saliency Explanations on the Recognition of Facial Genetic Syndromes 8

Region-based Saliency Explanations on the Recognition of Fac...

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8th Machine Learning for Healthcare Conference, MLHC 2023

作者： Sümer, Ömer Waikel, Rebekah L. Ledgister Hanchard, Suzanna E. Duong, Dat Krawitz, Peter Conati, Cristina Solomon, Benjamin D. André, Elisabeth Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Medical Genetics Branch National Human Genome Research Institute BethesdaMD United States Institute for Genomic Statistics and Bioinformatics University of Bonn Bonn Germany Department of Computer Science University of British Columbia BC Canada

Deep neural networks in computer vision have shown remarkable progress in recognizing facial genetic syndromes. Many genetic syndromes are difficult to detect, even for experienced clinicians, and computer-aided phenotyping can accelerate clinical diagnosis. High-stakes clinical tasks using deep learning, as in clinical genetics, require human understandable explanations for model decisions. Saliency methods are used to explain DNN predictions in various image analysis domains but have yet to be studied in facial genetics. The syndromic features of most genetic conditions are often localized to areas like the eyes, nose, and mouth. In this paper, to summarize the contribution of key facial regions to a specific disease prediction, we propose a face region relevance score that can be applied to any saliency method. We also investigate how prior knowledge, namely human phenotype ontology and DNN model explanations, align. Quantitative experiments are performed on a new database containing over 3,500 images of 11 rare facial syndromes, a healthy control group, and an additional test set of 171 facial images, whose respective facial phenotypes are labeled by clinicians. Current saliency methods are good at capturing dysmorphism in particular regions (parts of the face), but they may not completely capture all the relevant features in a given person or condition. Our study indicates which saliency explanations and face regions are more consistent with the phenotypes of specific genetic syndromes and could be used in large-scale clinical evaluations. © 2023 Ö. Sümer, R.L. Waikel, S.E.L. Hanchard, D. Duong, P. Krawitz, C. Conati, B.D. Solomon & E. André.

关键词： Deep neural networks

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights 9

Machine Learning-Assisted Protein Structure Prediction: An A...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Mahendran, S. Radha Dogra, Akriti Mendhe, Dinesh Babu, S. B. G. Tilak Dixit, Shriniket Singh, Surya Pratap Vels Institute of Science Technology And Advanced Studies Department of Bioinformatics Chennai Pallavaram India Manav Rachna Dental College Department of Oral & Maxillofacial Surgery Haryana Faridabad India Rutgers State University of New Jersey Office of Research Computing New BrunswickNJ United States Aditya Engineering College Department of Ece Surampalem India School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu Vellore India Upums Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350365092

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level. © 2024 IEEE.

关键词： Proteins

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Identifying Chlorella vulgaris and Chlorella sorokiniana as sustainable organisms to bioconvert glucosamine into valuable biomass

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Biotechnology Notes 2024年第1期5卷 13-22页

作者： Elhalis, Hosam Helmy, Mohamed Ho, Sherilyn Leow, Sharon Liu, Yan Selvarajoo, Kumar Chow, Yvonne Singapore Institute of Food and Biotechnology Innovation (SIFBI) Agency for Science Technology and Research (A*STAR) Singapore Singapore Bioinformatics Institute (BII) A*STAR Singapore Singapore Department of Computer Science Lakehead University ON Canada Institute of Sustainability for Chemicals Energy and Environment (ISCE2) A*STAR 1 Pesek Road Jurong Island 627833 Singapore Synthetic Biology for Clinical and Technological Innovation (SynCTI) National University of Singapore (NUS) Singapore Singapore School of Biological Sciences Nanyang Technological University (NTU) Singapore Singapore

Chitin is a major component of various wastes such as crustacean shells, filamentous fungi, and insects. Recently, food-safe biological and chemical processes converting chitin to glucosamine have been developed. Here, we studied microalgae that can uptake glucosamine as vital carbon and nitrogen sources for valuable alternative protein biomass. Utilizing data mining and bioinformatics analysis, we identified 29 species that contain the required enzymes for glucosamine to glucose conversion. The growth performance of the selected strains was examined, and glucosamine was used in different forms and concentrations. Glucose at a concentration of 2.5 g/L was required to initiate glucosamine metabolic degradation by Chlorella vulgaris and Chlorella sorokiniana. Glucosamine HCl and glucosamine phosphate showed maximum cell counts of about 8.5 and 9.0 log/mL for C. sorokiniana and C. vulgaris in 14 days, respectively. Enzymatic hydrolysis of glucosamine increased growth performance with C. sorokiniana by about 3 folds. The adapted strains were fast-growing and could double their dry biomasses during the same incubation time. In addition, adapted C. sorokiniana was able to tolerate three times glucosamine concentration in the medium. The study illustrated possible strategies for employing C. sorokiniana and C. vulgaris to convert glucosamine into valuable biomass in a more sustainable way. © 2024 The Authors

关键词： Chlorella sorokiniana Chlorella vulgaris Glucosamine-6-phosphate deaminase Microalgae Microalgae adaptation evolution

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An Analysis of YOLOvX Deep Learning Models for Colon Cancer Detection

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Procedia computer science 2025年 260卷 81-90页

作者： Raseena T.P. Aditya Kumar Soni Jitendra Kumar T. Akilan Ashutosh Kumar Singh Department of Computer Applications National Institute of Technology Tiruchirappalli 620015 Tamilnadu India Kotak Mahindra Bank Thane West Mumbai 400604 Maharastra India Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal 462003 M.P. India Department of Software Engineering Lakehead University Thunder Bay Canada Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal India

Around the world, Colorectal cancer is still a prevalent form of cancer, and effective treatment and its impact depends on early diagnosis. Recently, polyp detection methods employ a convolutional neural networks to identify precancerous or malignant polyps in colonoscopy images accurately with high speed and precision. This study evaluates four deep learning models namely YOLOv3n, YOLOv5s, YOLOv7, and YOLOv7x, to determine their effectiveness in detecting colorectal cancer polyp frames efficiently. Among these, YOLOv7x model exhibited outstanding performance on the Hyper Kavasir dataset compared to other models, achieving an F1 score of 88.0%, a recall of 86.4%, a precision of 89.5%, and a mAP of 92.0% at a confidence threshold of 0.353. The achieved results highlight the unique capability of models in detecting CRC polyps for diagnosis. Incorporating such detection models can help physicians significantly in clinical practice to improve their ability to identify malignant polyps, ultimately leading to better treatment for patients. Gastroenterologists can benefit considerably from these models as diagnostic tools because of their speed and accuracy.

关键词： Colorectal cancer Colonoscopy Deep learning YOLO

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CheXViT: CheXNet and Vision Transformer to Multi-Label Chest X-Ray Image Classification

CheXViT: CheXNet and Vision Transformer to Multi-Label Chest...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Faisal, Muhamad Darmawan, Jeremie Theddy Bachroin, Nabil Avian, Cries Leu, Jenq Shiou Tsai, Chia-Ti National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Indonesia International Institute for Life Sciences Department of Bioinformatics Jakarta Indonesia National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan National Taiwan University Hospital Department of Internal Medicine Taipei Taiwan

ISBN: (纸本)9781665493840

The popular technique in assisting radiologist to diagnose clinical thoracic for assessment of abnormalities is Chest X-Ray (CXR) imaging. The current automated system in CXR images relies on convolutional neural network (CNN) models, which focus on local features of images without considering global features. Most of the approaches utilize CNN which excels in generating inductive biases that specifically focus on potential regions of interest within an image. Although CNN models are able to achieve satisfactory performance, it is also a limiting factor to obtaining better performance in CXR classification. Recently, the adaptation of self-attention mechanism in transformer has been introduced to computer vision which enhances the performance of image classification by capturing short and long-range dependencies. Therefore, we propose a hybrid CNN-Transformer classifier for multi-label CXR images called CheXViT, a modification of CheXNet that integrates with the vision transformer (ViT) architecture. CheXNet and its remarkable performance is a perfect well-performing CXR classification model that could generate reliable and definitive feature maps for the ViT to widen the feature scope. The combination would propel the model performance by combining the inductive biases from CNN and long-range feature dependencies from the transformer. In the end, ChestX-Ray14 dataset is selected to evaluate the effectiveness of CheXViT. Our proposed method achieves a mean AUC of 0.838 and is superior to the existing methods. © 2023 IEEE.

关键词： Convolutional neural networks

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QT-GWAS:A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

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Molecular Plant 2023年第7期16卷 1212-1227页

作者： Marlies Brouckaert Meng Peng RenéHofer lias El Houari Chiarina Darrah Veronique Storme Yvan Saeys Ruben Vanholme Geert Goeminne Vitaliy I.Timokhin John Ralph Kris Morreel Wout Boerjan Ghent University Department of Plant Biotechnology and Bioinformatics9000 GhentBelgium VIB Center for Plant Systems Biology 9052 GhentBelgium Ghent University Department of Applied MathematicsComputer Science and Statistics9000 GhentBelgium VIB Center for Inflammation Research 9052 GhentBelgium vIB Metabolomics Core 9052 GhentBelgium Department of Biochemistry and US Department of Energy Great Lakes Bioenergy Research CenterWisconsin Energy InstituteUniversity of Wisconsin-MadisonMadisonWI53706USA Ghent University Department of Applied MathematicsComputer Science and StatisticsGhentBelgium ViB Center for Inflammation Research GhentBelgium BioNTech SE MainzRhineland-PalatinateGermany Eunomia Research&Consulting BristolUK VIB Agro-incubator NeveleBelgium Research Institute for Chromatography KortrijkBelgium

Although the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind,a large fraction of these metabolites and their biosynthetic pathways remain *** metabolite structures and their biosynthetic pathways is key to gaining biological understanding andto allow metabolic *** orderto retrieve novel biosynthetic genes involved in specialized metabolism,we developed a novel untargeted method designated as qualitative trait GWAs(QT-GWAS)that subjects qualitative metabolic traits to a genome-wide association study,while the conventional metabolite GWAS(mGWAS)mainly considers the quantitative variation of *** a proof of the validity of QT-GWAS,23 and 15of the retrieved associations identified in Arabidopsis thaliana by QTGWAS and mGWAS,respectively,were supported by previous ***,seven genemetabolite associations retrieved by QT-GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme *** such,we established that CYTOCHROME P450706A5(CYP706A5)is involved in the biosynthesis of chroman derivatives,UDP-GLYCOSYLTRANSFERASE 76C3(UGT76C3)is able tohexosylate guanine in vitro and in planta,and SULFOTRANSFERASE 202B1(SULT202B1)catalyzes the sulfation of neolignans in ***,our study demonstrates that the untargeted QT-GWAS method can retrieve valid gene-metabolite associations at the level of enzyme-encoding genes,even new associations that cannot be found by the conventional mGwAs,providing a new approach for dissecting qualitative metabolic traits.

关键词： qualitative method proof

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Beyond normal: on the evaluation of mutual information estimators 23

Beyond normal: on the evaluation of mutual information estim...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Paweł Czyż Frederic Grabowski Julia E. Vogt Niko Beerenwinkel Alexander Marx Department of Biosystems Science and Engineering ETH Zurich and ETH AI Center ETH Zurich Institute of Fundamental Technological Research Polish Academy of Sciences Department of Computer Science ETH Zurich and SIB Swiss Institute of Bioinformatics Department of Biosystems Science and Engineering ETH Zurich and SIB Swiss Institute of Bioinformatics ETH AI Center ETH Zurich and Department of Computer Science ETH Zurich

Mutual information is a general statistical dependency measure which has found applications in representation learning, causality, domain generalization and computational biology. However, mutual information estimators are typically evaluated on simple families of probability distributions, namely multivariate normal distribution and selected distributions with one-dimensional random variables. In this paper, we show how to construct a diverse family of distributions with known ground-truth mutual information and propose a language-independent benchmarking platform for mutual information estimators. We discuss the general applicability and limitations of classical and neural estimators in settings involving high dimensions, sparse interactions, long-tailed distributions, and high mutual information. Finally, we provide guidelines for practitioners on how to select appropriate estimator adapted to the difficulty of problem considered and issues one needs to consider when applying an estimator to a new data set.

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