bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke
Authors: Schlemm Eckhard , Ingwersen Thies , Königsberg Alina , Boutitie Florent , Ebinger Martin , Endres Matthias , Fiebach Jochen B. , Fiehler Jens , Galinovic Ivana , Lemmens Robin , Muir Keith W. , Nighoghossian Norbert , Pedraza Salvador , Puig Josep , Simonsen Claus Z. , Thijs Vincent , Wouters Anke , Gerloff Christian , Thomalla Götz , Cheng Bastian 
Published: May 2021

AbstractThrombolysis with recombinant tissue plasminogen activator in acute ischemic stroke aims to restore compromised blood flow and prevent further neuronal damage. Despite the proven clinical efficacy of this treatment, little is known about the short-term effects of systemic thrombolysis on structural brain connectivity. In this secondary analysis of the WAKE-UP trial, we used MRI-derived measures of infarct size and estimated structural network disruption to establish that thrombolysis is associated not only with less infarct growth, but also with reduced loss of large-scale connectivity between grey-matter areas after stroke. In a causal mediation analysis, infarct growth mediated a non-significant 8.3% (CI95% [−8.0, 32.6]%) of the clinical effect of thrombolysis on functional outcome. The proportion mediated jointly through infarct growth and change of structural connectivity, especially in the border zone around the infarct core, however, was as high as 33.4% (CI95% [8.8, 77.4]%). Preservation of structural connectivity is thus an important determinant of treatment success and favourable functional outcome in addition to lesion volume. It might, in the future, serve as an imaging endpoint in clinical trials or as a target for therapeutic interventions.
Decoupling epithelial-mesenchymal transitions from stromal profiles by integrative expression analysis
Authors: Tyler Michael , Tirosh Itay 
Published: May 2021

AbstractEpithelial-to-mesenchymal transition (EMT) is the most commonly cited mechanism for cancer metastasis, but it is difficult to distinguish from profiles of normal stromal cells in the tumour microenvironment. In this study we use published single cell RNA-seq data to directly compare mesenchymal signatures from cancer and stromal cells. Informed by these comparisons, we developed a computational framework to decouple these two sources of mesenchymal expression profiles using bulk RNA-seq datasets. This deconvolution offers the opportunity to characterise EMT across hundreds of tumours and examine its association with metastasis and other clinical features. With this approach, we find three distinct patterns of EMT, associated with squamous, gynaecological and gastrointestinal cancer types. Surprisingly, in most cancer types, EMT patterns are not associated with increased chance of metastasis, suggesting that other steps in the metastatic cascade may represent the main bottleneck. This work provides a comprehensive evaluation of EMT profiles and their functional significance across hundreds of tumours while circumventing the confounding effect of stromal cells.
Identification and characterization of a SARS-CoV-2 specific CD8+ T cell response with immunodominant features
Authors: Gangaev Anastasia , Ketelaars Steven L. C. , Isaeva Olga I. , Patiwael Sanne , Dopler Anna , Hoefakker Kelly , De Biasi Sara , Gibellini Lara , Mussini Cristina , Guaraldi Giovanni , Girardis Massimo , Ormeno Cami M. P. Talavera , Hekking Paul J. M. , Lardy Neubury M. , Toebes Mireille , Balderas Robert , Schumacher Ton N. , Ovaa Huib , Cossarizza Andrea , Kvistborg Pia 
Published: May 2021

AbstractThe COVID-19 pandemic caused by SARS-CoV-2 is a continuous challenge worldwide, and there is an urgent need to map the landscape of immunogenic and immunodominant epitopes recognized by CD8+ T cells. Here, we analyze samples from 31 patients with COVID-19 for CD8+ T cell recognition of 500 peptide-HLA class I complexes, restricted by 10 common HLA alleles. We identify 18 CD8+ T cell recognized SARS-CoV-2 epitopes, including an epitope with immunodominant features derived from ORF1ab and restricted by HLA-A*01:01. In-depth characterization of SARS-CoV-2-specific CD8+ T cell responses of patients with acute critical and severe disease reveals high expression of NKG2A, lack of cytokine production and a gene expression profile inhibiting T cell re-activation and migration while sustaining survival. SARS-CoV-2-specific CD8+ T cell responses are detectable up to 5 months after recovery from critical and severe disease, and these responses convert from dysfunctional effector to functional memory CD8+ T cells during convalescence.
LRIG1 is a gatekeeper to exit from quiescence in adult neural stem cells
Authors: Marqués-Torrejón María Ángeles , Williams Charles A. C. , Southgate Benjamin , Alfazema Neza , Clements Melanie P. , Garcia-Diaz Claudia , Blin Carla , Arranz-Emparan Nerea , Fraser Jane , Gammoh Noor , Parrinello Simona , Pollard Steven M. 
Published: May 2021

AbstractAdult neural stem cells (NSCs) must tightly regulate quiescence and proliferation. Single-cell analysis has suggested a continuum of cell states as NSCs exit quiescence. Here we capture and characterize in vitro primed quiescent NSCs and identify LRIG1 as an important regulator. We show that BMP-4 signaling induces a dormant non-cycling quiescent state (d-qNSCs), whereas combined BMP-4/FGF-2 signaling induces a distinct primed quiescent state poised for cell cycle re-entry. Primed quiescent NSCs (p-qNSCs) are defined by high levels of LRIG1 and CD9, as well as an interferon response signature, and can efficiently engraft into the adult subventricular zone (SVZ) niche. Genetic disruption of Lrig1 in vivo within the SVZ NSCs leads an enhanced proliferation. Mechanistically, LRIG1 primes quiescent NSCs for cell cycle re-entry and EGFR responsiveness by enabling EGFR protein levels to increase but limiting signaling activation. LRIG1 is therefore an important functional regulator of NSC exit from quiescence.
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
Authors: Oxley Mark P. , Ziatdinov Maxim , Dyck Ondrej , Lupini Andrew R. , Vasudevan Rama , Kalinin Sergei V. 
Published: May 2021

AbstractThe 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors. We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders (rrVAE), which disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures. The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis. This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.
Pesticides in a case study on no-tillage farming systems and surrounding forest patches in Brazil
Authors: da Silva Karlo Alves , Nicola Vitoria Beltrame , Dudas Rafaela Tavares , Demetrio Wilian Carlo , Maia Lilianne dos Santos , Cunha Luis , Bartz Marie Luise Carolina , Brown George Gardner , Pasini Amarildo , Kille Peter , Ferreira Nuno G. C. , de Oliveira Cíntia Mara Ribas 
Published: May 2021

AbstractWith the growing global concern on pesticide management, the relationship between its environmental recalcitrance, food security and human health has never been more relevant. Pesticides residues are known to cause significant environmental contamination. Here, we present a case study on long-term no-tillage farming systems in Brazil, where Glyphosate (GLY) has been applied for more than 35 years. GLY and its main breakdown product, aminomethylphosphonic acid (AMPA) were determined in topsoil (0–10 cm) samples from no-tillage fields and nearby subtropical secondary forests by high-performance liquid chromatography coupled with a fluorescence detector. In addition, the presence of carbamates, organochlorines, organophosphates and triazines were also screened for. GLY and AMPA were present in all soil samples, reaching values higher than those described for soils so far in the literature. A significant decrease for AMPA was observed only between the secondary forest and the farm's middle slope for site B. GLY and AMPA were observed respectively at peak concentrations of 66.38 and 26.03 mg/kg soil. GLY was strongly associated with forest soil properties, while AMPA associated more with no-tillage soil properties. Soil texture was a significant factor contributing to discrimination of the results as clay and sand contents affect GLY and AMPA retention in soils. This was the first study to report DDT and metabolites in consolidated no-tillage soils in Brazil (a pesticide fully banned since 2009). Based on human risk assessment conducted herein and the potential risk of GLY to local soil communities, this study offers a baseline for future studies on potential adverse effects on soil biota, and mechanistic studies.
COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images
Authors: Zargari Khuzani Abolfazl , Heidari Morteza , Shariati S. Ali 
Published: May 2021

AbstractChest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.
Fingerprinting shock-induced deformations via diffraction
Authors: Mishra Avanish , Kunka Cody , Echeverria Marco J. , Dingreville Rémi , Dongare Avinash M. 
Published: May 2021

AbstractDuring the various stages of shock loading, many transient modes of deformation can activate and deactivate to affect the final state of a material. In order to fundamentally understand and optimize a shock response, researchers seek the ability to probe these modes in real-time and measure the microstructural evolutions with nanoscale resolution. Neither post-mortem analysis on recovered samples nor continuum-based methods during shock testing meet both requirements. High-speed diffraction offers a solution, but the interpretation of diffractograms suffers numerous debates and uncertainties. By atomistically simulating the shock, X-ray diffraction, and electron diffraction of three representative BCC and FCC metallic systems, we systematically isolated the characteristic fingerprints of salient deformation modes, such as dislocation slip (stacking faults), deformation twinning, and phase transformation as observed in experimental diffractograms. This study demonstrates how to use simulated diffractograms to connect the contributions from concurrent deformation modes to the evolutions of both 1D line profiles and 2D patterns for diffractograms from single crystals. Harnessing these fingerprints alongside information on local pressures and plasticity contributions facilitate the interpretation of shock experiments with cutting-edge resolution in both space and time.
Dermal bacterial LPS-stimulation reduces susceptibility to intradermal Trypanosoma brucei infection
Authors: Alfituri Omar A. , Mararo Enock M. , Steketee Pieter C. , Morrison Liam J. , Mabbott Neil A. 
Published: May 2021

AbstractInfections withTrypanosoma bruceisp. are established after the injection of metacyclic trypomastigotes into the skin dermis by the tsetse fly vector. The parasites then gain access to the local lymphatic vessels to infect the local draining lymph nodes and disseminate systemically via the bloodstream. Macrophages are considered to play an important role in host protection during the early stage of systemic trypanosome infections. Macrophages are abundant in the skin dermis, but relatively little is known of their impact on susceptibility to intradermal (ID) trypanosome infections. We show that although dermal injection of colony stimulating factor 1 (CSF1) increased the local abundance of macrophages in the skin, this did not affect susceptibility to IDT. bruceiinfection. However, bacterial LPS-stimulation in the dermis prior to ID trypanosome infection significantly reduced disease susceptibility. In vitro assays showed that LPS-stimulated macrophage-like RAW264.7 cells had enhanced cytotoxicity towardsT. brucei, implying that dermal LPS-treatment may similarly enhance the ability of dermal macrophages to eliminate ID injectedT. bruceiparasites in the skin. A thorough understanding of the factors that reduce susceptibility to ID injectedT. bruceiinfections may lead to the development of novel strategies to help reduce the transmission of African trypanosomes.
Macrosynteny analysis between Lentinula edodes and Lentinula novae-zelandiae reveals signals of domestication in Lentinula edodes
Authors: Smith Christopher Alan 
Published: May 2021

AbstractThe basidiomycete fungus Lentinula novae-zelandiae is endemic to New Zealand and is a sister taxon to Lentinula edodes, the second most cultivated mushroom in the world. To explore the biology of this organism, a high-quality chromosome level reference genome of L. novae-zelandiae was produced. Macrosyntenic comparisons between the genome assembly of L. novae-zelandiae, L. edodes and a set of three genome assemblies of diverse species from the Agaricomycota reveal a high degree of macrosyntenic restructuring within L. edodes consistent with signal of domestication. These results show L. edodes has undergone significant genomic change during the course of its evolutionary history, likely a result of its cultivation and domestication over the last 1000 years.
Multistability in a star network of Kuramoto-type oscillators with synaptic plasticity
Authors: Ratas Irmantas , Pyragas Kestutis , Tass Peter A. 
Published: May 2021

AbstractWe analyze multistability in a star-type network of phase oscillators with coupling weights governed by phase-difference-dependent plasticity. It is shown that a network with N leaves can evolve into $$2^N$$

2
N

various asymptotic states, characterized by different values of the coupling strength between the hub and the leaves. Starting from the simple case of two coupled oscillators, we develop an analytical approach based on two small parameters $$\varepsilon$$
ε
and $$\mu$$
μ
, where $$\varepsilon$$
ε
is the ratio of the time scales of the phase variables and synaptic weights, and $$\mu$$
μ
defines the sharpness of the plasticity boundary function. The limit $$\mu \rightarrow 0$$

μ
→
0

corresponds to a hard boundary. The analytical results obtained on the model of two oscillators are generalized for multi-leaf star networks. Multistability with $$2^N$$

2
N

various asymptotic states is numerically demonstrated for one-, two-, three- and nine-leaf star-type networks.
Rift Valley fever virus detection in susceptible hosts with special emphasis in insects
Authors: Gregor K. M. , Michaely L. M. , Gutjahr B. , Rissmann M. , Keller M. , Dornbusch S. , Naccache F. , Schön K. , Jansen S. , Heitmann A. , König R. , Brennan B. , Elliott R. M. , Becker S. , Eiden M. , Spitzbarth I. , Baumgärtner W. , Puff C. , Ulrich R. , Groschup M. H. 
Published: May 2021

AbstractRift Valley fever phlebovirus (RVFV, Phenuiviridae) is an emerging arbovirus that can cause potentially fatal disease in many host species including ruminants and humans. Thus, tools to detect this pathogen within tissue samples from routine diagnostic investigations or for research purposes are of major interest. This study compares the immunohistological usefulness of several mono- and polyclonal antibodies against RVFV epitopes in tissue samples derived from natural hosts of epidemiologic importance (sheep), potentially virus transmitting insect species (Culex quinquefasciatus, Aedes aegypti) as well as scientific infection models (mouse, Drosophila melanogaster, C6/36 cell pellet). While the nucleoprotein was the epitope most prominently detected in mammal and mosquito tissue samples, fruit fly tissues showed expression of glycoproteins only. Antibodies against non-structural proteins exhibited single cell reactions in salivary glands of mosquitoes and the C6/36 cell pellet. However, as single antibodies exhibited a cross reactivity of varying degree in non-infected specimens, a careful interpretation of positive reactions and consideration of adequate controls remains of critical importance. The results suggest that primary antibodies directed against viral nucleoproteins and glycoproteins can facilitate RVFV detection in mammals and insects, respectively, and therefore will allow RVFV detection for diagnostic and research purposes.
Feasibility of large-scale population testing for SARS-CoV-2 detection by self-testing at home
Authors: Iruzubieta Paula , Fernández-Lanas Tatiana , Rasines Laura , Cayon Lorena , Álvarez-Cancelo Ana , Santos-Laso Alvaro , García-Blanco Agustín , Curiel-Olmo Soraya , Cabezas Joaquín , Wallmann Reinhard , Fábrega Emilio , Martínez-Taboada Víctor M. , Hernández José L. , López-Hoyos Marcos , Lazarus Jeffrey V. , Crespo Javier 
Published: May 2021

AbstractThe simplicity and low cost of rapid point-of-care tests greatly facilitate large-scale population testing, which can contribute to controlling the spread of the COVID-19 virus. We evaluated the applicability of a self-testing strategy for SARS-CoV2 in a population-based, cross-sectional study in Cantabria, Spain, between April and May 2020. For the self-testing strategy, participants received the necessary material for the self-collection of blood and performance of a rapid antibody test using lateral flow immunoassay at home without the supervision of healthcare personnel. A total of 1,022 participants were enrolled. Most participants correctly performed the COVID-19 self-test the first time (91.3% [95% CI 89.4–92.9]). Only a minority of the participants (0.7%) needed the help of healthcare personnel, while 6.9% required a second kit delivery, for a total valid test result in 96.9% of the participants. Incorrect use of the self-test was not associated with the educational level, age over 65, or housing area. Prevalence of IgG antibodies against SARS-CoV2 for subjects with a valid rapid test result was 3.1% (95% CI 2.2–4.4), similar to the seroprevalence result obtained using a conventional approach carried out by healthcare professionals. In conclusion, COVID-19 self-testing should be considered as a screening tool.
Mitigating belief projection in explainable artificial intelligence via Bayesian teaching
Authors: Yang Scott Cheng-Hsin , Vong Wai Keen , Sojitra Ravi B. , Folke Tomas , Shafto Patrick 
Published: May 2021

AbstractState-of-the-art deep-learning systems use decision rules that are challenging for humans to model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts for how people typically reason about unfamiliar agents. We propose explicitly modelling the human explainee via Bayesian teaching, which evaluates explanations by how much they shift explainees’ inferences toward a desired goal. We assess Bayesian teaching in a binary image classification task across a variety of contexts. Absent intervention, participants predict that the AI’s classifications will match their own, but explanations generated by Bayesian teaching improve their ability to predict the AI’s judgements by moving them away from this prior belief. Bayesian teaching further allows each case to be broken down into sub-examples (here saliency maps). These sub-examples complement whole examples by improving error detection for familiar categories, whereas whole examples help predict correct AI judgements of unfamiliar cases.
The microbial profile of a tissue necrosis affecting the Atlantic invasive coral Tubastraea tagusensis
Authors: Zanotti Aline Aparecida , Gregoracci Gustavo Bueno , Kitahara Marcelo Visentini 
Published: May 2021

AbstractThe Southwestern Atlantic rocky reef ecosystems are undergoing significant changes due to sun-corals (Tubastraea tagusensis and T. coccinea) invasion. At Búzios Island, on the northern coast of São Paulo State, where the abundance of T. tagusensis is particularly high, some colonies are displaying tissue necrosis, a phenomenon never reported for this invasive nor any other azooxanthellate coral species. Using next-generation sequencing, we sought to understand the relationship between T. tagusensis tissue necrosis and its microbiota. Thus, through amplicon sequencing, we studied both healthy and diseased coral colonies. Results indicate a wide variety of bacteria associated with healthy colonies and an even higher diversity associated with those corals presenting tissue necrosis, which displayed nearly 25% more microorganisms. Also, as the microbial community associated with the seven healthy colonies did not alter composition significantly, it was possible to verify the microbial succession during different stages of tissue necrosis (i.e., initial, intermediate, and advanced). Comparing the microbiome from healthy corals to those in early tissue necrosis suggests 21 potential pathogens, which might act as the promoters of such disease.
Developmental features and predicting airway failure risk in critically ill children with mandibular hypoplasia using 3D computational tomographic analysis
Authors: Kim Doo-Hwan , Gwon Eunseo , Ock Junheok , Choi Jong-Woo , Lee Jee Ho , Kim Sung-Hoon , Kim Namkug 
Published: May 2021

AbstractIn children with mandibular hypoplasia, airway management is challenging. However, detailed cephalometric assessment data for this population are sparse. The aim of this study was to find risk factors for predicting difficult airways in children with mandibular hypoplasia, and compare upper airway anatomical differences using three-dimensional computed tomography (3D CT) between children with mandibular hypoplasia and demographically matched healthy controls. There were significant discrepancies in relative tongue position (P < 0.01) and anterior distance of the hyoid bone (P < 0.01) between patients with mandibular hypoplasia and healthy controls. All mandibular measures were significantly different between the two groups, except for the height of the ramus of the mandible. After adjusting for age and sex, the anterior distance of hyoid bone and inferior pogonial angle were significantly associated with a difficult airway (P = 0.01 and P = 0.02). Quantitative analysis of upper airway structures revealed significant discrepancies, including relative tongue position, hyoid distance, and mandible measures between patients with mandibular hypoplasia and healthy controls. The anterior distance of the hyoid bone and inferior pogonial angle may be risk factors for a difficult airway in patients with mandibular hypoplasia.