Bioscience Program

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  • Article

    BioBBC: a multi-feature model that enhances the detection of biomedical entities

    (Springer Science and Business Media LLC, 2024-04-02) Alamro, Hind; Gojobori, Takashi; Essack, Magbubah; Gao, Xin; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Computer Science; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Computational Bioscience Research Center; Computational Bioscience Research Center (CBRC); Biological, Environmental Sciences and Engineering; Biological and Environmental Science and Engineering (BESE) Division; Bioscience; Bioscience Program; College of Computing, Umm Al-Qura University, Mecca, Saudi Arabia

    The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

  • Article

    BioBBC: a multi-feature model that enhances the detection of biomedical entities

    (Springer Science and Business Media LLC, 2024-04-02) Alamro, Hind; Gojobori, Takashi; Essack, Magbubah; Gao, Xin; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Computer Science; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Computational Bioscience Research Center; Computational Bioscience Research Center (CBRC); Biological, Environmental Sciences and Engineering; Biological and Environmental Science and Engineering (BESE) Division; Bioscience; Bioscience Program; College of Computing, Umm Al-Qura University, Mecca, Saudi Arabia

    The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

  • Article

    The Role Of Side Chains and Hydration on Mixed Charge Transport in N-Type Polymer Films

    (Wiley, 2024-03-30) Jokubas, Surgailis; Flagg, Lucas Q.; Richter, Lee J.; Druet, Victor; Griggs, Sophie; Wu, Xiaocui; Moro, Stefania; Ohayon, David; Kousseff, Christina J.; Marks, Adam; Maria, Iuliana P.; Chen, Hu; Moser, Maximilian; Costantini, Giovanni; McCulloch, Iain; Inal, Sahika; King Abdullah University of Science and Technology (KAUST) Biological and Environmental Science and Engineering Division Organic Bioelectronics Lab Thuwal 23955–6900 Saudi Arabia; KAUST KAUST Solar Center Physical Science and Engineering Division Thuwal 23955–6900 Saudi Arabia; Electrical and Computer Engineering; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division; Bioengineering; Bioengineering Program; Biological, Environmental Sciences and Engineering; Biological and Environmental Science and Engineering (BESE) Division; Bioscience; Bioscience Program; KAUST Solar Center; KAUST Solar Center (KSC); Physical Sciences and Engineering; Physical Science and Engineering (PSE) Division; Chemistry; Chemical Science Program; National Institute of Standards and Technology (NIST) Materials Science and Engineering Division Gaithersburg Maryland 20899 USA; University of Oxford Department of Chemistry Chemistry Research Laboratory Oxford OX1 3TA UK; Department of Chemistry University of Warwick Coventry CV4 7AL UK; School of Chemistry University of Birmingham Birmingham B15 2TT UK; Department of Materials Science and Engineering Stanford University 450 Serra Mall Stanford CA 94305 USA

    Introducing ethylene glycol (EG) side chains to a conjugated polymer backbone is a well-established synthetic strategy for designing organic mixed ion-electron conductors (OMIECs). However, the impact that film swelling has on mixed conduction properties has yet to be scoped, particularly for electron-transporting (n-type) OMIECs. Here, we investigate the effect of the length of branched EG chains on mixed charge transport of n-type OMIECs based on a naphthalene-1,4,5,8-tetracarboxylic-diimide-bithiophene backbone. We use atomic force microscopy, grazing-incidence wide-angle X-ray scattering (GIWAXS), and scanning tunneling microscopy to establish the similarities between the common-backbone films in dry conditions. Electrochemical quartz crystal microbalance with dissipation monitori1ng (EQCM-D) and in situ GIWAXS measurements reveal stark changes in film swelling properties and microstructure during electrochemical doping, depending on the side chain length. We find that even in the loss of the crystallite content upon contact with the aqueous electrolyte, the films can effectively transport charges and that it is rather the high water content that harms the electronic interconnectivity within the OMIEC films. These results highlight the importance of controlling water uptake in the films to impede the charge transport in n-type electrochemical devices.This article is protected by copyright. All rights reserved

  • Preprint

    Structure of Aquifex aeolicus Lumazine Synthase by Cryo-Electron Microscopy to 1.42 Angstrom Resolution

    (Cold Spring Harbor Laboratory, 2024-03-23) Savva, Christos G; Sobhy, Mohamed Abdelmaboud; De Biasio, Alfredo; Hamdan, Samir; Biological, Environmental Sciences and Engineering; Biological and Environmental Science and Engineering (BESE) Division; Bioscience; Bioscience Program

    Single particle Cryo-Electron microscopy (Cryo-EM) has become an essential structural determination technique with recent hardware developments making it possible to reach atomic resolution at which individual atoms, including hydrogen atoms, can be resolved. Thus Cryo-EM allows not only unprecedented detail regarding the structural architecture of complexes but also a better understanding surrounding their chemical states. In this study we used the enzyme involved in the penultimate step of riboflavin biosynthesis as a test specimen to benchmark a recently installed microscope and determine if other protein complexes could reach a resolution of 1.5 Angstrom or better which so far has only been achieved for the iron carrier ferritin. Using state of the art microscope and detector hardware as well as the latest software techniques to overcome microscope and sample limitations, a 1.42 Angstrom map of Aquifex aeolicus lumazine synthase (AaLS) was obtained from a 48-hour microscope session. In addition to water molecules and ligands involved in AaLS function, we can observe positive density for ~50% of hydrogen atoms. A small improvement in resolution was achieved by Ewald sphere correction which was expected to limit the resolution to ~1.5 Angstrom for a molecule of this diameter. Our study confirms that other protein complexes can be solved to near-atomic resolution. Future improvements in specimen preparation and protein complex stabilization may allow more flexible macromolecules to reach this level of resolution and should become a priority of study in the field.

  • Article

    Conductive magnetic nanowires accelerated electron transfer between C1020 carbon steel and Desulfovibrio vulgaris biofilm

    (Elsevier BV, 2024-03-16) Alrammah, Farah; Xu, Lingjun; Patel, Niketan Sarabhai; Kontis, Nicholas; Rosado, Alexandre S.; Gu, Tingyue; Biological and Enviromental Science and Engineering Division, Red Sea Research Center, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia; Environmental Science and Engineering; Environmental Science and Engineering Program; Biological, Environmental Sciences and Engineering; Biological and Environmental Science and Engineering (BESE) Division; Bioscience; Bioscience Program; Red Sea Research Center; Red Sea Research Center (RSRC); Department of Biology, Imam Abdulrhman Bin Faisal University, Dammam 34212, Saudi Arabia; Department of Chemical & Biomolecular Engineering, Institute for Corrosion and Multiphase Technology, Ohio University, Athens, Ohio 45701, USA

    Microbial biofilms are behind microbiologically influenced corrosion (MIC). Sessile cells in biofilms are many times more concentrated volumetrically than planktonic cells in the bulk fluids, thus providing locally high concentrations of chemicals. More importantly, “electroactive” sessile cells in biofilms are capable of utilizing extracellularly supplied electrons (e.g., from elemental Fe) for intracellular reduction of an oxidant such as sulfate in energy metabolism. MIC directly caused by anaerobic biofilms is classified into two main types based on their mechanisms: extracellular electron transfer MIC (EET-MIC) and metabolite MIC (M-MIC). Sulfate-reducing bacteria (SRB) are notorious for their corrosivity. They can cause EET-MIC in carbon steel, but they can also secrete biogenic H2S to corrode other metals such as Cu directly via M-MIC. This study investigated the use of conductive magnetic nanowires as electron mediators to accelerate and thus identify EET-MIC of C1020 by Desulfovibrio vulgaris. The presence of 40 ppm (w/w) nanowires in ATCC 1249 culture medium at 37 °C resulted in 45 % higher weight loss and 57 % deeper corrosion pits after 7-day incubation. Electrochemical tests using linear polarization resistance and potentiodynamic polarization supported the weight loss data trend. These findings suggest that conductive magnetic nanowires can be employed to identify EET-MIC. The use of insoluble 2 μm long nanowires proved that the extracellular section of the electron transfer process is a bottleneck in SRB MIC of carbon steel.