bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of a covert evolutionary pathway between two protein folds
Authors: Chakravarty Devlina , Sreenivasan Shwetha , Swint-Kruse Liskin , Porter Lauren L. 
Published: December 2022

AbstractAlthough homologous protein sequences are expected to adopt similar structures, some amino acid substitutions can interconvert α-helices and β-sheets. Such fold switching may have occurred over evolutionary history, but supporting evidence has been limited by the: (1) abundance and diversity of sequenced genes, (2) quantity of experimentally determined protein structures, and (3) assumptions underlying the statistical methods used to infer homology. Here, we overcame these barriers by applying multiple statistical methods to a family of ~600,000 bacterial response regulator proteins. We found that their homologous DNA-binding subunits assume divergent structures: helix-turn-helix versus α-helix+β-sheet (winged helix). Phylogenetic analyses, ancestral sequence reconstruction, and AlphaFold2 models indicated that amino acid substitutions facilitated a switch from helix-turn-helix into winged helix. This structural transformation likely expanded DNA-binding specificity. Our approach uncovers an evolutionary pathway between two protein folds and provides methodology to identify secondary structure switching in other protein families.
Signatures of adaptive decreased virulence of deformed wing virus in an isolated population of wild honey bees (Apis mellifera)
Authors: Ray Allyson M. , Gordon Emma C. , Seeley Thomas D. , Rasgon Jason L. , Grozinger Christina M. 
Published: December 2022

AbstractUnderstanding the ecological and evolutionary processes that drive host-pathogen interactions is critical for combating epidemics and conserving species. TheVarroa destructormite and deformed wing virus (DWV) are two synergistic threats to Western honey bee (Apis mellifera) populations across the globe. Distinct honey bee populations have been found to self-sustain despiteVarroainfestations, including colonies within the Arnot Forest outside Ithaca, NY, USA. We hypothesized that in these honey bee populations, DWV has been selected to produce an avirulent infection phenotype, allowing for the persistence of both host and disease-causing agents. To investigate this, we assessed the presence and titer of viruses in bees from the Arnot Forest and managed apiaries, and assessed genomic variation and virulence differences between DWV isolates. Across groups, we found viral abundance was similar, but viral genotypes were distinct. We also found that infections with viral isolates from the Arnot Forest resulted in higher survival and lower rates of symptomatic deformed wings, compared to analogous isolates from managed colonies, providing preliminary evidence to support the hypothesis of adaptive decreased viral virulence. Overall, this multi-level investigation of virus genotype and phenotype across different contexts reveals critical insight into global bee health and the ecological and evolutionary processes that drive host-pathogen interactions.
Single cell guided deconvolution of bulk transcriptomics recapitulates differentiation stages of acute myeloid leukemia and predicts drug response
Authors: Karakaslar E Onur , Severens Jeppe , Sánchez-López Elena , van Veelen Peter A , Zlei Mihaela , van Dongen Jacques JM , Otte Annemarie M. , Halkes Constantijn JM , van Balen Peter , Veelken Hendrik , Reinders Marcel JT , Griffioen Marieke , van den Akker Erik B 
Published: December 2022

AbstractThe diagnostic spectrum for AML patients is increasingly based on genetic abnormalities due to their prognostic and predictive value. However, information on the AML blast phenotype regarding their maturational arrest has started to regain importance due to its predictive power on drug responses. Here, we deconvolute 1350 bulk RNA-seq samples from five independent AML cohorts on a single-cell healthy BM reference and demonstrate that the morphological differentiation stage (FAB classification) could be faithfully reconstituted using estimated cell compositions (ECCs). Moreover, we show that the ECCs reliably predictex-vivodrug resistances as demonstrated for Venetoclax, aBCL-2inhibitor, resistance specifically in AML with CD14+ monocyte phenotype. We further validate these predictions using in-house proteomics data by showing thatBCL-2protein abundance is split into two distinct clusters for NPM1-mutated AML at the extremes of CD14+ monocyte percentages, which could be crucial for the Venetoclax dosing for these patients. Our results suggest that Venetoclax resistance predictions can also be extended to AML without recurrent genetic abnormalities (NOS), and possibly to MDS-related AML and secondary AML. Collectively, we propose a framework for allowing a joint mutation and maturation stage modeling that could be used as a blueprint for testing sensitivity for new agents across the various subtypes of AML.
3D multiplexed tissue imaging reconstruction and optimized region-of-interest (ROI) selection through deep learning model of channels embedding
Authors: Burlingame Erik , Ternes Luke , Lin Jia-Ren , Chen Yu-An , Kim Eun Na , Gray Joe W. , Santagata Sandro , Sorger Peter K. , Chang Young Hwan 
Published: December 2022

ABSTRACTTissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that employ MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region-of-interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
Demographic impacts of low- and high-intensity fire in a riparian savanna bird: implications for ecological fire management
Authors: Teunissen Niki , McAlpine Hamish , Cameron Skye F. , Murphy Brett P. , Peters Anne 
Published: December 2022

ABSTRACTClimate change is driving changes in fire frequency and intensity, making it more urgent for conservation managers to understand how species and ecosystems respond to fire. In tropical monsoonal savannas – Earth’s most fire-prone landscapes – ecological fire management aims to prevent intense wildfires late in the dry season through prescribed low-intensity fire early in the dry season. Riparian habitats embedded within tropical savannas represent critical refuges for biodiversity, yet are particularly sensitive to fire. Better understanding of the impact of fire – including prescribed burns – on riparian habitats is therefore key, but requires long-term detailed post-fire monitoring of species’ demographic rates, as effects may persist and/or be delayed.Here, we quantify the multi-year impacts of prescribed low-intensity and high-intensity fire on the density, survival, reproduction and dispersal of the threatened western purple-crowned fairy-wren (Malurus coronatus coronatus), in an exceptionally well-studied individually-marked population.Following low-intensity fire, bird density was reduced in the burnt compared to adjacent unburnt riparian habitat for at least 2.5 years. This was a result of reduced breeding success and recruitment for two years immediately following the fire, rather than mortality of adults or dispersal away from burnt habitat.In contrast, a high-intensity fire (in a year with low rainfall) resulted in a sharp decline in population density 2-8 months after the fire, with no signs of recovery after 2.5 years. The decline in density was due to post-fire adult mortality, rather than dispersal. Breeding success of the (few) remaining individuals was low but not detectably lower than in unburnt areas, likely because breeding success was poor overall due to prevailing dry conditions.Hence, even if there is no or very low mortality during fire, and no movement of birds away from burnt areas post-fire, both low- and high-intensity fire in the riparian zone result in reduced population density. However, the mechanism by which this occurs, and recovery time, differs with fire intensity. To minimise the impacts of fire on riparian zones in tropical savannas, we suggest employing low-intensity prescribed burns shortly after the breeding season in years with good rainfall.
Phantasus: web-application for visual and interactive gene expression analysis
Authors: Kleverov Maksim , Zenkova Daria , Kamenev Vladislav , Sablina Margarita , Artyomov Maxim N. , Sergushichev Alexey A. 
Published: December 2022

AbstractTranscriptomic profiling became a standard approach to quantify a cell state, which led to accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires a significant technical expertise. Here we present Phantasus – a user-friendly web-application for interactive gene expression analysis which provide a streamlined access to more than 84000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows to go all the way from loading, normalizing and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed on-line athttps://ctlab.itmo.ru/phantasusorhttps://artyomovlab.wustl.edu/phantasusor can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available athttps://github.com/ctlab/phantasusunder MIT licence.
Transfer Learning for Neuroimaging via Re-use of Deep Neural Network Features
Authors: Holderrieth Peter , Smith Stephen , Peng Han 
Published: December 2022

AbstractA major problem in the application of machine learning to neuroimaging is the technological variability of MRI scanners and differences in the subject populations across studies. Transfer learning (TL) attempts to alleviate this problem. TL refers to a family of methods which acquire knowledge from related tasks to improve generalization in the tasks of interest. In this work, we pre-train a deep neural network on UK Biobank MRI data on age and sex prediction, and study the predictions of the network on three small MRI data sets. We find that the neural networks can extract meaningful features from unseen data sets under the necessary but also sufficient condition that the network was pre-trained to predict the label of interest (e.g. pre-trained on age prediction if age prediction is the task of interest). Based on this, we propose a transfer learning pipeline which relies on the re-use of deep neural network features across data sets for the same tasks. We find that our method outperforms classical regression methods and training a network from scratch. In particular, we improve state-of-the-art results on age and sex prediction. Our transfer learning method may therefore provide a simple and efficient pipeline to achieve high performance on small MRI data sets.
NIT: an open-source tool for information theoretic analysis of neural population data
Authors: Maffulli Roberto , Casal Miguel A. , Celotto Marco , Zucca Stefano , Safaai Houman , Fellin Tommaso , Panzeri Stefano 
Published: December 2022

AbstractInformation theory provides a popular and principled framework for the analysis of neural data. It allows to uncover in an assumption-free way how neurons encode and transmit information, capturing both linear and non-linear coding mechanisms and including the information carried by interactions of any order. To facilitate its application, here we present Neuroscience Information Toolbox (NIT), a new toolbox for the accurate information theoretical analysis of neural data. NIT contains widely used tools such as limited sampling bias corrections and discretization of neural probabilities for the calculation of stimulus coding in low-dimensional representation of neural activity (e.g. Local Field Potentials or the activity of small neural population).Importantly, it adds a range of recent tools for quantifying information encoding by large populations of neurons or brain areas, for the directed transmission of information between neurons or areas, and for the calculation of Partial Information Decompositions to quantify the behavioral relevance of neural information and the synergy and redundancy among neurons and brain areas. Further, because information theoretic algorithms have been previously validated mainly with electrophysiological recordings, here we used realistic simulations and analysis of real data to study how to optimally apply information theory to the analysis of two-photon calcium imaging data, which are particularly challenging due to their lower signal-to-noise and temporal resolution. We also included algorithms (based on parametric and non-parametric copulas) to compute robustly information specifically with analog signals such as calcium traces. We provide indications on how to best process calcium imaging traces and to apply NIT depending on the type of calcium indicator, imaging frame rate and firing rate levels. In sum, NIT provides a toolbox for the comprehensive and effective information theoretic analysis of all kinds of neural data, including calcium imaging.
Site-directed placement of three-dimensional DNA origami
Authors: Martynenko Irina , Erber Elisabeth , Ruider Veronika , Dass Mihir , Posnjak Gregor , Yin Xin , Altpeter Philipp , Liedl Tim 
Published: December 2022

ABSTRACTAssembling hybrid substrates with nanometer-scale precision and molecular addressability enables advances in such distant fields as material research and biosensing. As such, the combination of lithographic methods with 2D DNA origami self-assembly [1–4] has led, among others, to the development of photonic crystal cavity arrays [2] and the exploration of sensing nanoarrays where molecular devices are patterned on the sub-micron scale [5–7]. Here we extend this concept to the third dimension through mounting 3D DNA origami onto nano-patterned substrates followed by silicification [8, 9] to provide mechanical and chemical stability. Our versatile and scalable method relying on self-assembly at ambient temperatures offers the potential to 3D-position any inorganic and organic components that are compatible with DNA architectures [10–13]. This way, complex and 3D-patterend surfaces designed on the molecular level while reaching macroscopic dimensions could supersede energy-intensive manufacturing steps in substrate processing.
Using machine learning to detect coronaviruses potentially infectious to humans
Authors: Gonzalez-Isunza Georgina , Jawaid M. Zaki , Liu Pengyu , Cox Daniel L. , Vazquez Mariel , Arsuaga Javier 
Published: December 2022

AbstractEstablishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from the binding of the spike protein of alpha and beta coronaviruses to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among human coronaviruses. Two viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 (a MERS related virus) andRhinolophus affiniscoronavirus isolate LYRa3 a SARS related virus. We further analyze the binding properties of these viruses using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-COV-2 viral sequences. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.
Efficacy and safety of emapalumab in macrophage activation syndrome
Authors: De Benedetti Fabrizio , Grom Alexei A , Brogan Paul , Bracaglia Claudia , Pardeo Manuela , Marucci Giulia , Eleftheriou Despina , Papadopoulou Charalampia , Schulert Grant S , Quartier Pierre , Antón Jordi , Laveille Christian , Frederiksen Rikke , Asnaghi Veronica , Ballabio Maria , Jacqmin Philippe , de Min Cristina 
Published: December 2022

ABSTRACTObjectivesMacrophage activation syndrome (MAS) is a severe, life-threatening complication of systemic juvenile idiopathic arthritis (sJIA) and adult onset Still’s disease (AOSD). The objective of this study was to confirm the adequacy of an emapalumab dosing regimen in relation to interferon-γ (IFNγ) activity by assessing efficacy and safety. The efficacy outcome was MAS remission by week 8, based on clinical and laboratory criteria.MethodsWe studied emapalumab, a human anti–IFNγ antibody, administered with background glucocorticoids, in a prospective open-label, single-group trial involving patients who had MAS secondary to sJIA or AOSD and had previously failed high-dose glucocorticoids. The study foresaw 4-week treatment that could be shortened or prolonged based on investigator’s assessment of response. Patients could enter a long-term (12 months) follow-up study.ResultsFourteen patients received emapalumab. All patients completed the trial, entered the long-term follow-up and were alive at the end of the follow-up. The investigated dosing regimen, based on an initial loading dose followed by maintenance doses, was appropriate, as shown by rapid neutralisation of IFNγ activity, demonstrated by prompt decrease in serum C-X-C motif chemokine ligand 9 (CXCL9) levels. By week 8, remission of MAS was achieved in 13 of the 14 patients at a median time of 25 days. Viral infections and positive viral tests were observed during the trial and during the long-term follow-up.ConclusionsNeutralisation of IFNγ with emapalumab was efficacious in inducing remission of MAS secondary to sJIA or AOSD in patients who had failed high-dose glucocorticoids.KEY MESSAGESWhat is already known on this topic?Macrophage activation syndrome (MAS) is a severe and potentially life-threatening complication of systemic juvenile idiopathic arthritis and adult-onset Still’s disease. There are no therapeutic options that have been prospectively investigated in MAS. Data in animal models andex vivodata from humans with MAS led to the hypothesis that interferon-γ (IFNγ) has a pathogenic role in MAS.What does this study add?This open-label multicentre trial using emapalumab, an anti–IFNγ antibody, in patients who have failed to respond to high-dose glucocorticoids, demonstrates that IFNγ has a pathogenic role in MAS and that its neutralisation leads to MAS remission.How might this affect research, clinical practice or policy?The results of this study show that neutralisation of IFNγ with emapalumab is a therapeutic option for patients with severe MAS who have failed standard of care with high-dose glucocorticoids.
PaleoProPhyler: a reproducible pipeline for phylogenetic inference using ancient proteins
Authors: Patramanis Ioannis , Ramos-Madrigal Jazmín , Cappellini Enrico , Racimo Fernando 
Published: December 2022

AbstractAncient proteins from fossilized or semi-fossilized remains can yield phylogenetic information at broad temporal horizons, in some cases even millions of years into the past. In recent years, peptides extracted from archaic hominins and long-extinct mega-fauna have enabled unprecedented insights into their evolutionary history. In contrast to the field of ancient DNA - where several computational methods exist to process and analyze sequencing data - few tools exist for handling ancient protein sequence data. Instead, most studies rely on loosely combined custom scripts, which makes it difficult to reproduce results or share methodologies across research groups. Here, we present PaleoProPhyler: a new fully reproducible pipeline for aligning ancient peptide data and subsequently performing phylogenetic analyses. The pipeline can not only process various forms of proteomic data, but also easily harness genetic data in different formats (CRAM, BAM, VCF) and translate it, allowing the user to create reference panels for phyloproteomic analyses. We describe the various steps of the pipeline and its many functionalities, and provide some examples of how to use it. PaleoProPhyler allows researchers with little bioinformatics experience to efficiently analyze palaeoproteomic sequences, so as to derive insights from this valuable source of evolutionary data.