bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving T-cell mediated immunogenic epitope identification via machine learning: the neoIM model
Authors: Pfitzer Lena , Lybaert Lien , Bogaert Cedric , Fant Bruno 
Published: June 2022

AbstractThe identification of immunogenic peptides that will elicit a CD8+ T cell-specific immune response is a critical step for various immunotherapeutic strategies such as cancer vaccines. Significant research effort has been directed towards predicting whether a peptide is presented on class I major histocompatibility complex (MHC I) molecules. However, only a small fraction of the peptides predicted to bind to MHC I turn out to be immunogenic. Prediction of immunogenicity, i.e. the likelihood for CD8+ T cells to recognize and react to a peptide presented on MHC I, is of high interest to reduce validation costs, de-risk clinical studies and increase therapeutic efficacy especially in a personalized setting where in vitro immunogenicity pre-screening is not possible.To address this, we present neoIM, a random forest classifier specifically trained to classify short peptides as immunogenic or non-immunogenic. This first-in-class algorithm was trained using a positive dataset of more than 8000 non-self immunogenic peptide sequences, and a negative dataset consisting of MHC I-presented peptides with one or two mismatches to the human proteome for a closer resemblance to a background of mutated but non-immunogenic peptides. Peptide features were constructed by performing principal component analysis on amino acid physicochemical properties and stringing together the values of the ten main principal components for each amino acid in the peptide, combined with a set of peptide-wide properties. The neoIM algorithm outperforms the currently publicly available methods and is able to predict peptide immunogenicity with high accuracy (AUC=0.88). neoIM is MHC-allele agnostic, and in vitro validation through ELISPOT experiments on 33 cancer-derived neoantigens have confirmed its predictive power, showing that 71% of all immunogenic peptides are contained within the top 30% of neoIM predictions and all immunogenic peptides were included when selecting the top 55% of peptides with the highest neoIM score. Finally, neoIM results can help to better predict the response to checkpoint inhibition therapy, especially in low TMB tumors, by focusing on the number of immunogenic variants in a tumor.Overall, neoIM enables significantly improved identification of immunogenic peptides allowing the development of more potent vaccines and providing new insights into the characteristics of immunogenic peptides.
Nanopore sequencing for N1-methylpseudouridine in RNA reveals sequence-dependent discrimination of the modified nucleotide triphosphate during transcription
Authors: Fleming Aaron M. , Burrows Cynthia J. 
Published: June 2022

AbstractDirect RNA sequencing with a commercial nanopore platform was used to sequence RNA containing uridine (U), pseudouridine (Ψ), or N1-methylpseudouridine (m1Ψ) generated by in vitro transcription (IVT). The base calling data as well as the ionic currents and dwell times for U, Ψ, or m1Ψ as they translocated through the helicase and nanopore proteins identified diagnostic signatures for Ψ and m1Ψ; however, the two modifications yielded similar patterns although both were different from U. Understanding the nanopore signatures for Ψ and m1Ψ enabled a running start T7 RNA polymerase assay to study how competing mixtures of UTP with ΨTP or m1ΨTP lead to nucleotide selection in all possible adjacent sequence contexts. For UTP vs. ΨTP, ΨTP was favorably incorporated in singly-modified contexts, while doubly-modified contexts found high yields of ΨTP insertion on the 5′ side and lower yields on the 3′ side. For UTP vs. m1ΨTP, UTP was favorably selected except in 5′-XA (X = U or m1Ψ) where the ratio was determined by their relative NTP concentrations. Experiments with chemically-modified triphosphates and DNA templates designed based on the structure of T7 RNA polymerase provide a model to explain the observations. These results may aid in future efforts that employ IVT to make therapeutic mRNAs with sub-stochiometric amounts of m1Ψ.
Inter-layer and inter-subject variability of circadian gene expression in human skin
Authors: del Olmo Marta , Spörl Florian , Korge Sandra , Jürchott Karsten , Felten Matthias , Grudziecki Astrid , de Zeeuw Jan , Nowozin Claudia , Reuter Hendrik , Blatt Thomas , Herzel Hanspeter , Kunz Dieter , Kramer Achim , Ananthasubramaniam Bharath 
Published: June 2022

AbstractThe skin is the largest human organ with a circadian clock that regulates its function. Although circadian rhythms in specific functions are known, rhythms in the proximal clock output, gene expression, in human skin have not been thoroughly explored. This work reports circadian gene expression in two skin layers, epidermis and dermis, in a cohort of young, healthy adults, who maintained natural, regular sleep schedules. 10% of the expressed genes showed rhythms at the population level, of which only a third differed between the two layers. Broadly, expression magnitudes of circadian genes were consistent across subjects in each layer. Amplitude and phases of circadian gene expression, however, varied more across subjects than layers, with amplitude being more variable than phases. Expression amplitudes in the epidermis were larger and more subject-variable, while they were smaller and more consistent in the dermis. Core clock gene expression was similar across layers at the population-level, but were heterogeneous in the their variability across subjects. We used this data to identify small sets of biomarkers for internal clock phase in each layer, which consisted of layer-specific non-core clock genes. This work provides a valuable resource to advance our understanding of human skin to realize the potential of circadian medicine as well as a novel methodology to quantify sources of variability in human circadian rhythms.
Laser particle barcoding for multi-pass high-dimensional flow cytometry
Authors: Kwok Sheldon J.J. , Forward Sarah , Fahlberg Marissa D. , Cosgriff Sean , Lee Seung Hyung , Abbott Geoffrey , Zhu Han , Minasian Nicolas H. , Vote A. Sean , Martino Nicola , Yun Seok-Hyun 
Published: June 2022

ABSTRACTFlow cytometry is a standard technology in life science and clinical laboratories used to characterize the phenotypes and functional status of cells, especially immune cells. Recent advances in immunology and immuno-oncology as well as drug and vaccine discovery have increased the demand to measure more parameters. However, the overlap of fluorophore emission spectra and one-time measurement nature of flow cytometry are major barriers to meeting the need. Here, we present multi-pass flow cytometry, in which cells are tracked and measured repeatedly through barcoding with infrared laser-emitting microparticles. We demonstrate the benefits of this approach on several pertinent assays with human peripheral blood mononuclear cells (PBMCs). First, we demonstrate unprecedented time-resolved flow characterization of T cells before and after stimulation. Second, we show 33-marker deep immunophenotyping of PBMCs, analyzing the same cells in 3 back-to-back cycles. This workflow allowed us to use only 10-13 fluorophores in each cycle, significantly reducing spectral spillover and simplifying panel design. Our results open a new avenue in multi-dimensional single-cell analysis based on optical barcoding of individual cells.
Metagenomics vs. total RNA sequencing: most accurate data-processing tools, microbial identification accuracy, and implications for freshwater assessments
Authors: Hempel Christopher A. , Wright Natalie , Harvie Julia , Hleap Jose S. , Adamowicz Sarah J. , Steinke Dirk 
Published: June 2022

ABSTRACTMetagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine freshwater assessments. However, these targeted-PCR- free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 768 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for targeted-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine freshwater assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into freshwater assessments.
Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile
Authors: Patiyal Sumeet , Dhall Anjali , Bajaj Khushboo , Sahu Harshita , Raghava Gajendra P.S. 
Published: June 2022

AbstractThis paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R, and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76 when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with MCC of 0.49 on the validation dataset. Our best model outperform existing methods when evaluated on the validation dataset. A user-friendly standalone software and web based server named “Pprint2” has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2andhttps://github.com/raghavagps/pprint2)Key PointsMachine learning based models were developed using different profilesPSSM profile of a protein was created to extract evolutionary informationPSSM profiles of proteins were generated using PSI-BLASTConvolutional neural network based model was developed using PSSM profileWebserver, Python- and Perl-based standalone package, and GitHub is availableAuthor’s BiographySumeet Patiyal is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Anjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Khushboo Bajaj is currently working as MTech in Computer Science and Engineering from Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi, India.Harshita Sahu is currently working as MTech in Computer Science and Engineering from Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi, India.Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Identification of structural features of selected flavonoids responsible for neuroprotection using a Drosophila model of Parkinson’s disease
Authors: Maitra Urmila , Conger John , (Maggie) Owens Mary Magdalene , Ciesla Lukasz 
Published: June 2022

AbstractNature-derived bioactive compounds have emerged as promising candidates for the prevention and treatment of diverse chronic illnesses, including neurodegenerative diseases. However, the exact molecular mechanisms underlying their neuroprotective effects remain unclear. Most studies focus solely on the antioxidant activities of natural products which translate to poor outcome in clinical trials. Current therapies against neurodegeneration only provide symptomatic relief thereby underscoring the need for novel strategies to combat disease onset and progression. We have employed an environmental toxin-induced Drosophila Parkinson’s disease (PD) model as an inexpensive in vivo screening platform to explore neuroprotective potential of selected dietary flavonoids. We have identified a specific group of flavonoids known as flavones displaying protection against paraquat (PQ)-induced neurodegenerative phenotypes, involving reduced survival, mobility defects and enhanced oxidative stress. Interestingly, the other groups of investigated flavonoids, namely, the flavonones and flavonols failed to provide protection indicating a requirement of specific structural features that confer protection against PQ-mediated neurotoxicity in Drosophila. Based on our screen, the neuroprotective flavones lack a functional group substitution at the C3 and contain α,β-unsaturated carbonyl group. Furthermore, flavones-mediated neuroprotection is not solely dependent on antioxidant properties but also involves regulation of neuroinflammatory responses. Our data identify specific structural features of selected flavonoids that provide neuroprotection against environmental toxin-induced PD pathogenesis that can be explored for novel therapeutic interventions.Graphical Abstract
AdaLiftOver: High-resolution identification of orthologous regulatory elements with adaptive liftOver
Authors: Dong Chenyang , Keleş Sündüz 
Published: June 2022

AbstractElucidating orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from the genome-wide association studies. Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets and GWAS SNPs yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs. The R package AdaLiftOver is available from https://github.com/ThomasDCY/AdaLiftOver.
Diagnosis and early prediction of lymphoma using high-throughput clonality analysis of bovine leukemia virus-infected cells
Authors: Okagawa Tomohiro , Shimakura Honami , Konnai Satoru , Saito Masumichi , Matsudaira Takahiro , Nao Naganori , Yamada Shinji , Murakami Kenji , Maekawa Naoya , Murata Shiro , Ohashi Kazuhiko 
Published: June 2022

AbstractBovine leukemia virus (BLV), a retrovirus, infects into B cells of ruminants and causes aggressive leukemia or lymphoma in cattle, enzootic bovine leukosis (EBL). Clonal expansion of BLV-infected cells is a promising marker for early detection and diagnosis of EBL. Recently, we developed rapid amplification of the integration site without interference by genomic DNA contamination (RAISING) and CLOVA, a software to analyze clonality. RAISING-CLOVA could assess the risk of adult T-cell leukemia/lymphoma development in human T-cell leukemia virus-I-infected individuals through its clonality analysis. Thus, we herein examined the performance of RAISING-CLOVA for the clonality analysis of BLV-infected cells and conducted a comprehensive clonality analysis by RAISING-CLOVA in EBL and non-EBL cattle. RAISING-CLOVA successfully distinguished EBL from non-EBL cattle with high sensitivity and specificity. A longitudinal clonality analysis in BLV-infected sheep, an EBL model, also confirmed the effectiveness of BLV clonality analysis with RAISING-CLOVA for early detection of EBL development. Therefore, our study emphasizes the usefulness of RAISING-CLOVA as a routine clinical test for monitoring virus-related cancers.
The effects of dynamic prompt and background transparency of hover feedback design on the user interface of shopping websites
Authors: Chen Chien-Hsiung , Zhai Weimin 
Published: June 2022

PurposeThe implementation of interaction design concepts to facilitate communication between users and shopping websites has gained increasing attention in recent years. Mouse hover is a vital interaction method for users to access shopping sites and significantly impacts their decision-making experience. A well-designed mouse hover function can effectively enhance the user's search performance and improve the user experience. The purpose of this study is to investigate whether the dynamic prompt designs at the hover position and the degree of feedback transparency may affect the user's task performance and personal feelings when operating the hover function on the shopping website.Design/methodology/approachThe study employed two independent variables in the experiments: dynamic prompt and background transparency of hover feedback. A between-subject design of 2 (single flicker and continuous flicker) × 3 (transparency at 0%, 25%, and 50%) was adopted in the experiment. A total of 60 participants were invited to participate in the experiment using the purposive sampling method. Participants were asked to complete four operation tasks, and the time of each was recorded. They were then asked to complete the system usability scale (SUS) questionnaire and conduct subjective evaluations before they were briefly interviewed.FindingsThe generated results revealed that: (1) In the interaction with a shopping website, the degree of background transparency of hover feedback affected the participants' task performance. (2) The effect of a single flicker had significantly higher subjective evaluation results regarding the degree of rationality compared with a continuous flicker. (3) The participants' perceptions of the information clarity were affected when the background transparency of hover feedback was 50%. (4) The effect of a continuous flicker was better than that of a single flicker when the background transparency of hover feedback was 25%. The participants' attraction to a continuous flicker was significantly higher than that of a single flicker. Nonetheless, when the degree of background transparency was at 0% or 50%, the results were the opposite.Originality/valueThe findings generated from the research can be a reference for the development of hover operation in the user interface design for shopping websites.
Impacts of COVID-19 on construction project management: a life cycle perspective
Authors: Li Zhen , Jin Yutong , Li Wenjing , Meng Qingfeng , Hu Xin 
Published: June 2022

PurposeThe impacts of COVID-19 on construction projects have attracted much attention in the construction management research community. Nevertheless, a systematic review of these studies is still lacking. The purpose of this paper is to systematically analyze the impacts of COVID-19 on the different stages of a project life-cycle, and comprehensively sort out the epidemic response measures adopted by project participants. In addition, the study also attempts to explore the challenges and opportunities faced by project management practitioners under the context of COVID-19.Design/methodology/approachThis study comprehensively demonstrates the systematic review process of COVID-19 related research in the construction industry, systematically summarizes the research status of the impact of COVID-19 on construction projects, and defines the strategies to deal with COVID-19 in project management; and through the visualization research, determines the current key research topics and future research trends.FindingsThis study identifies 11 construction activities in the project management life cycle that are affected by COVID-19 and finds that the COVID-19 epidemic has the greatest impact on construction workers, construction standards, construction contracts and construction performance. The study further summarizes the six main epidemic countermeasures and mitigation measures taken within the construction industry following the arrival of the epidemic. In addition, the results of this study identify opportunities and future trends in intelligent construction technology, rapid manufacturing engineering and project management in the construction industry in the post-epidemic era through literature results, which also provide ideas for related research.Practical implicationsCOVID-19 has brought severe challenges to society. It is of great significance for the future sustainable development of the construction industry to identify the impact of COVID-19 on all phases of the project and to promote the development of coping strategies by project stakeholders.Originality/valueFirst of all, there is little study comprehensively reviewing the impacts of COVID-19 on the different stages of construction projects and the strategies to deal with the negative impacts. In addition, from a life cycle perspective, the used articles in this study were grouped into different categories based on project stages. This promotes an integrated and comprehensive understanding of historical studies. Moreover, on the basis of a comprehensive review, this paper puts forward future research directions to promote the sustainable development of the construction sector.