bluuebunny/crossref_metadata_2025_split huggingface dataset:
Direct inference and control of genetic population structure from RNA sequencing data
Authors: Fachrul Muhamad , Karkey Abhilasha , Shakya Mila , Judd Louise M. , Harshegyi Taylor , Sim Kar Seng , Tonks Susan , Dongol Sabina , Shrestha Rajendra , Salim Agus ,   STRATAA study group, Baker Stephen , Pollard Andrew J. , Khor Chiea Chuen , Dolecek Christiane , Basnyat Buddha , Dunstan Sarah J. , Holt Kathryn E. , Inouye Michael 
Published: September 2022

AbstractRNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.
Protease-independent production of poliovirus virus-like particles in Pichia pastoris: Implications for efficient vaccine development and insights into capsid assembly
Authors: Sherry Lee , Swanson Jessica J. , Grehan Keith , Xu Huijun , Uchida Mai , Jones Ian M. , Stonehouse Nicola J. , Rowlands David J. 
Published: September 2022

AbstractThe production of enterovirus virus-like particles (VLPs) which lack the viral genome have great potential as vaccines for a number of diseases, such as poliomyelitis and hand, foot-and-mouth disease. These VLPs can mimic empty capsids, which are antigenically indistinguishable from mature virions, produced naturally during viral infection. Both in infection and in vitro, capsids and VLPs are generated by the cleavage of the P1 precursor protein by a viral protease. Here, using a stabilised poliovirus 1 (PV-1) P1 sequence as an exemplar, we show the production of PV-1 VLPs in Pichia pastoris in the absence of the potentially cytotoxic protease, 3CD, instead using the porcine teschovirus 2A (P2A) peptide sequence to terminate translation between individual capsid proteins. We compare this to protease-dependent production of PV-1 VLPs. Analysis of all permutations of the order of the capsid protein sequences revealed that only VP3 could be tagged with P2A and maintain native antigenicity. Transmission electron microscopy of these VLPs reveals the classic picornaviral icosahedral structure. Furthermore, these particles were thermostable above 37°C, demonstrating their potential as next generation vaccine candidates for PV. Finally, we believe the demonstration that native antigenic VLPs can be produced using protease-independent methods opens the possibility for future enteroviral vaccines to take advantage of recent vaccine technological advances, such as adenovirus-vectored vaccines and mRNA vaccines, circumventing the potential problems of cytotoxicity associated with 3CD, allowing for the production of immunogenic enterovirus VLPs in vivo.
Multi-omics data integration via novel interpretable k-hop graph attention network for signaling network inference
Authors: Yuan Ruoying , Feng Jiarui , Zhang Heming , Chen Yixin , Payne Philip , Li Fuhai 
Published: September 2022

AbstractWith the advent of sequencing technology, large-scale multi-omics data have been generated to understand the diversity and heterogeneity of genetic targets and associated complex signaling pathways at multiple levels in diseases, which are critical targets to guide the development of personalized precision medicine. However, it remains a challenging task to computationally mine a few essential targets and pathways from a large number of variables characterized by the multi-level multi-omics data. In this study, we proposed a novel interpretable k-hop graph attention network model, k-hop GAT, to integrate the multi-omics data to infer the essential targets and related signaling networks. We evaluated the proposed model using the multi-omics data, i.e., genetic mutation, copy number variation, methylation, gene expression data, of 332 cancer lines; and the experimentally identified essential targets. The validation and comparison results indicated that the proposed model outperformed the GAT and graph convolutional network (GCN) models.
Production and characterisation of stabilised PV-3 virus-like particles using Pichia pastoris
Authors: Sherry Lee , Grehan Keith , Swanson Jessica J. , Bahar Mohammad W. , Porta Claudine , Fry Elizabeth E. , Stuart David I. , Rowlands David J. , Stonehouse Nicola J. 
Published: September 2022

AbstractFollowing the success of global vaccination programmes using the live-attenuated oral and inactivated poliovirus vaccines (OPV and IPV), wild poliovirus (PV) is now only endemic in Afghanistan and Pakistan. However, the continued use of these vaccines poses potential risks to the eradication of PV. The production of recombinant PV virus-like particles (VLPs), which lack the viral genome offer great potential as next-generation vaccines for the post-polio world. We have previously reported production of PV VLPs using Pichia pastoris, however, these VLPs were in the non-native conformation (C Ag), which would not produce effective protection against PV. Here, we build on this work and show that it is possible to produce wt PV-3 and thermally-stabilised PV-3 (referred to as PV-3 SC8) VLPs in the native conformation (D Ag) using Pichia pastoris. We show that the PV-3 SC8 VLPs provide a much-improved D:C antigen ratio as compared to wt PV-3, whilst exhibiting greater thermostability than the current IPV vaccine. Finally, we determine the cryo-EM structure of the yeast-derived PV-3 SC8 VLPs and compare this to previously published PV-3 D Ag structures, highlighting the similarities between these recombinantly-expressed VLPs and the infectious virus, further emphasising their potential as a next-generation vaccine candidate for PV.
SLC25A46 localizes to sites of mitochondrial fission and fusion and loss of function variants alter the oligomerization states of MFN2 and OPA1
Authors: Schuettpelz Jana , Janer Alexandre , Antonicka Hana , Shoubridge Eric A. 
Published: September 2022

ABSTRACTMutations in SLC25A46, coding for an outer mitochondrial membrane protein, underlie a wide spectrum of neurodegenerative diseases associated with alterations in mitochondrial morphology, but the precise role of the protein remains unknown. We established an SLC25A46 knock-out cell line in human fibroblasts and studied the pathogenicity of three different variants (p.T142I, p.R257Q, p.E335D) introduced into the null background. Mitochondria were fragmented in the knock-out cell line and hyperfused in all pathogenic variants. The loss of SLC25A46 led to impaired cellular proliferation and striking abnormalities in mitochondrial cristae ultrastructure that were not rescued by expression of the pathogenic variants. SLC25A46 was present in discrete puncta at mitochondrial branch points and at tips of mitochondrial tubules, co-localizing with DRP1 and OPA1. Virtually all fission/fusion events were demarcated by the presence of an SLC25A46 focus. SLC25A46 co-immunoprecipitated with the fusion machinery, and loss of function altered the oligomerization state of OPA1 and MFN2. Proximity interaction mapping identified components of the ER membrane, lipid transfer proteins, and mitochondrial outer membrane proteins indicating that it is present at interorganellar contact sites important for lipid exchange. Consistent with this, SLC25A46 loss of function led to altered mitochondrial lipid composition, suggesting that it may facilitate interorganellar lipid flux or play a role in membrane remodeling associated with mitochondrial fusion and fission.
Omicron sublineage BA.2.75.2 exhibits extensive escape from neutralising antibodies
Authors: Sheward Daniel J. , Kim Changil , Fischbach Julian , Muschiol Sandra , Ehling Roy A. , Björkström Niklas K. , Karlsson Hedestam Gunilla B. , Reddy Sai T. , Albert Jan , Peacock Thomas P. , Murrell Ben 
Published: September 2022

AbstractSeveral sublineages of omicron have emerged with additional mutations that may afford further antibody evasion. Here, we characterise the sensitivity of emerging omicron sublineages BA.2.75.2, BA.4.6, and BA.2.10.4 to antibody-mediated neutralisation, and identify extensive escape by BA.2.75.2. BA.2.75.2 was resistant to neutralisation by Evusheld (tixagevimab + cilgavimab), but remained sensitive to bebtelovimab. In recent serum samples from blood donors in Stockholm, Sweden, BA.2.75.2 was neutralised, on average, at titers approximately 6.5-times lower than BA.5, making BA.2.75.2 the most neutralisation resistant variant evaluated to date. These data raise concerns that BA.2.75.2 may effectively evade humoral immunity in the population.
Neuromodulatory silencing of nerve terminals
Authors: Cook Daniel C. , Ryan Timothy A. 
Published: September 2022

AbstractControl of neurotransmission efficacy is central to theories of how the brain computes and stores information. Neuromodulators are critical in this problem as they locally influence synaptic strength and can operate on a wide range of time scales. Presynaptic function is heavily influenced by G-protein coupled receptors (GPCRs) that in part restrict voltage-gated calcium (Ca2+) influx in the active zone. Here, using quantitative analysis of both single bouton Ca2+ influx and exocytosis, we uncovered an unexpected non-linear relationship between the magnitude of action potential driven Ca2+ influx and the concentration of external Ca2+ ([Ca2+]e). We find that this unexpected relationship is leveraged by neuromodulator signaling when operating at the nominal physiological set point for [Ca2+]e, 1.2 mM, to achieve complete silencing of nerve terminals. These data imply that the information throughput in neural circuits can be readily modulated in an all-or none fashion at the single synapse level when operating at the physiological set point.
Small protein modules dictate prophage fates during polylysogeny
Authors: Silpe Justin E. , Duddy Olivia P. , Hussain Fatima A. , Forsberg Kevin J. , Bassler Bonnie L. 
Published: September 2022

SUMMARYDNA-damaging agents are the pervasive inducers of temperate phages in model bacteria. However, most bacteria in the biosphere are predicted to carry multiple prophages, a state called polylysogeny, making it unclear how co-residing prophages compete for host cell resources if they all respond to the identical trigger. We discover regulatory modules encoded on phage genomes that control prophage induction independently of the DNA damage cue. Genes specifying these pathways exist in linear plasmid-like phages at sites essential for phage propagation. The modules lack sequence similarity but display a shared regulatory logic of a transcription factor that activates expression of a neighboring gene encoding a small protein. The small protein inactivates the master repressor of lysis, leading to prophage induction. In some phages, the regulatory unit detects sensory information including quorum-sensing autoinducers, making lysis host-cell-density dependent. Exposure of the polylysogens studied here to different induction scenarios reveals that mixed phage populations emerge following DNA damage, however, induction through the SOS-independent module drives near-exclusive production of the phage sensitive to that specific cue. Considering the lack of potent DNA-damaging agents in natural habitats, we propose that additional phage-encoded sensory pathways that drive lysis play fundamental roles in phage-host biology and inter-prophage competition.
The maize pathogen Ustilago maydis secretes glycoside hydrolases and carbohydrate oxidases directed towards components of the fungal cell wall
Authors: Reyre Jean-Lou , Grisel Sacha , Haon Mireille , Navarro David , Ropartz David , Le Gall Sophie , Record Eric , Sciara Giuliano , Tranquet Olivier , Berrin Jean-Guy , Bissaro Bastien 
Published: September 2022

AbstractFilamentous fungi are keystone microorganisms in the regulation of many processes occurring on Earth, such as plant biomass decay, pathogenesis as well as symbiotic associations. In many of these processes, fungi secrete carbohydrate-active enzymes (CAZymes) to modify and/or degrade carbohydrates. Ten years ago, while evaluating the potential of a secretome from the maize pathogen Ustilago maydis to supplement lignocellulolytic cocktails, we noticed it contained many unknown or poorly characterized CAZymes. Here, and after re-annotation of this dataset and detailed phylogenetic analyses, we observed that several CAZymes (including glycoside hydrolases and carbohydrate oxidases) are predicted to act on the fungal cell wall (FCW), notably on β-1,3-glucans. We heterologously produced and biochemically characterized two new CAZymes, called UmGH16_1-A and UmAA3_2-A. We show that UmGH16_1-A displays β-1,3-glucanase activity, with a preference for β-1,3-glucans with short β-1,6 substitutions, and UmAA3_2-A is a dehydrogenase catalyzing the oxidation of β-1,3- and β-1,6-gluco-oligosaccharides into the corresponding aldonic acids. Working on model β-1,3-glucans, we show that the linear oligosaccharide products released by UmGH16_1-A are further oxidized by UmAA3_2-A, bringing to light a putative biocatalytic cascade. Interestingly, analysis of available transcriptomics data indicates that both UmGH16_1-A and UmAA3_2-A are co-expressed, only during early stages of U. maydis infection cycle. Altogether, our results suggest that both enzymes are connected and that additional accessory activities still need to be uncovered to fully understand the biocatalytic cascade at play and its physiological role.ImportanceFilamentous fungi play a central regulatory role on Earth, notably in the global carbon cycle. Regardless of their lifestyle, filamentous fungi need to remodel their own cell wall (mostly composed of polysaccharides) to grow and proliferate. To do so, they must secrete a large arsenal of enzymes, most notably carbohydrate-active enzymes (CAZymes). However, research on fungal CAZymes over past decades has mainly focused on finding efficient plant biomass conversion processes while CAZymes directed at the fungus itself have remained little explored. In the present study, using the maize pathogen Ustilago maydis as model, we set off to evaluate the prevalence of CAZymes directed towards the fungal cell wall during growth of the fungus on plant biomass and characterized two new CAZymes active on fungal cell wall components. Our results suggest the existence of a biocatalytic cascade that remains to be fully understood.
This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks
Authors: Booker William W. , Ray Dylan D. , Schrider Daniel R. 
Published: September 2022

ABSTRACTNumerous studies over the last decade have demonstrated the utility of machine learning methods when applied to population genetic tasks. More recent studies show the potential of deep learning methods in particular, which allow researchers to approach problems without making prior assumptions about how the data should be summarized or manipulated, instead learning their own internal representation of the data in an attempt to maximize inferential accuracy. One type of deep neural network, called Generative Adversarial Networks (GANs), can even be used to generate new data, and this approach has been used to create individual artificial human genomes free from privacy concerns. In this study, we further explore the application of GANs in population genetics by designing and training a network to learn the statistical distribution of population genetic alignments (i.e. data sets consisting of sequences from an entire population sample) under several diverse evolutionary histories—the first GAN capable of performing this task. After testing multiple different neural network architectures, we report the results of a fully differentiable Deep-Convolutional Wasserstein GAN with gradient penalty that is capable of generating artificial examples of population genetic alignments that successfully mimic key aspects of the training data, including the site frequency spectrum, differentiation between populations, and patterns of linkage disequilibrium. We demonstrate consistent training success across various evolutionary models, including models of panmictic and subdivided populations, populations at equilibrium and experiencing changes in size, and populations experiencing either no selection or positive selection of various strengths, all without the need for extensive hyperparameter tuning. Overall, our findings highlight the ability of GANs to learn and mimic population genetic data and suggest future areas where this work can be applied in population genetics research that we discuss herein.
Machine-Learning Based Optimisation of a Biomimiced Herringbone Microstructure for Superior Aerodynamic Performance
Authors: Patel Rushil Samir , Akolekar Harshal D. 
Published: September 2022

AbstractBiomimicry involves taking inspiration from existing designs in nature to generate new and efficient systems. The feathers of birds which form a characteristic herringbone riblet shape are known to effectively reduce drag. This paper aims to optimise the individual constituent structure of a herringbone riblet pattern using a combination of computational fluid dynamics (CFD) and supervised machine learning algorithms to achieve the best possible reduction in drag. Initially, a herringbone riblet design is made by computer aided designing and is parameterised. By randomly varying these parameters, 107 additional designs are made and are subjected to CFD calculations to derive their drag coefficients (Cd). These designs are used to train a supervised learning model which is employed as an alternative to CFD for predicting the Cd of other 10000 randomly generated herringbone riblet designs. Amongst these, the design with the least predicted Cd is considered as the optimised design. The Cd prediction for the optimised design had an error of 4 % with respect to its true Cd which was calculated by using CFD. The optimised design of this microstructure can be utilised for drag reduction of aeronautical, automotive or oceanic crafts by integrating them onto their surfaces.
The Role of Students’ Situational Interest in Classroom Learning: An Empirical Study based on both Laboratory and Naturalistic Paradigms
Authors: Ye Jingmeiqi , Liu Xiaobo , Wei Jun , Zhang Yu 
Published: September 2022

AbstractInterest has always been an important research object in the field of education. Moreover, understanding the role of interest in students’ classroom learning is conducive to teaching practices. By constructing a theoretical model of interest based on relevant theories and prior empirical evidence, this study examined the differences within research paradigms and explored the potential function of neurophysiological signals in representing students’ interest in learning. In both laboratory and naturalistic paradigms, self-report and neurophysiological data were collected from 10th graders of an urban secondary school in Beijing, China. Through the data-driven approach of machine learning, the neurophysiological data were employed to fit self-report situational interest, and relevant theoretical models were examined based on all data using structural equation modeling. The path coefficients in the models were subsequently compared to determine the similarities and differences between the two paradigms. Combined with the results of multiple linear regressions established via machine learning, this study demonstrates the ability of neurophysiological data to represent situational interest.
Crop suitability analysis by adopting geo-spatial algorithm: a case study of Sirajganj district (flood-prone area) in Bangladesh
Authors: Haque Md. Nazmul , Fatema Kaniz , Joy Md. Ashikur Rahman 
Published: September 2022

PurposeCrop suitability analysis is vital for identifying a piece of land’s potential for sustainable crop production and aids in the formulation of an effective agricultural management plan. This study aims to conduct crop suitability analysis of prominent Kharif (rice and maize) and Rabi (potato and wheat) crops in Sirajganj district, a flood-prone area of Bangladesh, and recommend a suitable cropping pattern to mitigate the detrimental effects of flooding.Design/methodology/approachVarious factors such as soil drainage, soil depth, soil moisture, soil texture, soil permeability, soil pH, erosion hazard, nutrient status and flooding risk were considered for this study. For all four crops, the weights of each factor were determined using the analytical hierarchy process approach, and the scores of each subfactor were assigned on the basis of favorable circumstances of crop cultivation. Using the weighted overlay analysis in the ArcGIS 10.3 environment, the crop suitability maps were generated and were divided into four suitable classes. Geographic information system integration of crop suitability for all the crops determined the suitable cropping pattern of the study area in Kharif and Rabi seasons.FindingsA vast portion of the study area covering 64.80% of the total land is suitable for cultivating either rice or maize in Kharif season followed by either potato or wheat in Rabi season. Other suitable cropping pattern for Kharif and Rabi seasons found in the study area are rice-wheat, rice-wheat/potato, rice/maize-wheat and rice/maize-potato, which covers a little portion of the study area.Originality/valueThis research validates the suitable location of crop cultivation on the basis of flooding occurrences in the locality.
Effect of surface modification of polyester fabric on microfiber shedding from household laundry
Authors: Rathinamoorthy R. , Raja Balasaraswathi S. 
Published: September 2022

PurposeSynthetic textile materials are noted as one of the major contributors to microfiber pollution through laundry. Though many research works evaluated microfiber pollution, the solutions provided to control microfiber shedding are meager. The existing products collect or filter the microfiber from laundry effluent and restrict the direct leaching. However, no methods were proposed to effectively reduce the shedding from the textile itself.Design/methodology/approachThis research is aimed to analyze the influence of surface modification of polyester knitted textiles by sodium hydroxide, on microfiber shedding. Response surface methodology was adapted to optimize different treatment parameters (alkali concentration, treatment time and temperature).FindingsThe results show that the sodium hydroxide concentration and treatment time had a negative correlation with microfiber shedding reduction. Whereas, treatment temperature had a positive correlation with microfiber shedding reduction. The statistical analysis revealed that 0.4 M concentration, 90°C temperature and 24 min of treatment time was the best process condition for minimum microfiber release. The same was confirmed with a practical experiment and a significant reduction of 80.63% in microfiber shedding after alkali treatment was found.Originality/valueAlkali treatment of different knitted polyester fabrics with various knit structures and mass per square meter showed a significant reduction in microfiber shedding. The repeated laundry performed for 20 washes with surface-modified samples showed a significant reduction in microfiber release at every wash cycle and ensured the longevity of the effect.