bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel MRI-based data fusion methodology for efficient, personalised, compliant simulations of aortic haemodynamics
Authors: Stokes Catriona , Bonfanti Mirko , Li Zeyan , Xiong Jiang , Chen Duanduan , Balabani Stavroula , Díaz-Zuccarini Vanessa 
Published: May 2021

AbstractWe present a novel, cost-efficient methodology to simulate aortic haemo-dynamics in a patient-specific, compliant aorta using an MRI data fusion process. Based on a previously-developed Moving Boundary Method, this technique circumvents the high computational cost and numerous structural modelling assumptions required by traditional Fluid-Structure Interaction techniques. Without the need for Computed Tomography (CT) data, the MRI images required to construct the simulation can be obtained during a single imaging session. Black Blood MR Angiography and 2D Cine-MRI data were used to reconstruct the luminal geometry and calibrate wall movement specifically to each region of the aorta. 4D-Flow MRI and non-invasive pressure measurements informed patient-specific inlet and outlet boundary conditions. Luminal area closely matched 2D Cine-MRI measurements with a mean error of less than 4.6% across the cardiac cycle, while physiological pressure and flow distributions were simulated to within 3.3% of patient-specific targets. Moderate agreement with 4D-Flow MRI velocity data was observed. Despite lower peak velocity, an equivalent rigid-wall simulation predicted a mean Time-Averaged Wall Shear Stress (TAWSS) 13% higher than the compliant simulation. The agreement observed between compliant simulation results and MRI data is testament to the accuracy and efficiency of this MRI-based simulation technique.
Differential sphingosine-1-phosphate receptor-1 protein expression in the dorsolateral prefrontal cortex between schizophrenia Type 1 and Type 2
Authors: Chand Ganesh B. , Jiang Hao , Miller J. Philip , Rhodes C. Harker , Tu Zhude , Wong Dean F. 
Published: May 2021

AbstractUnderstanding the etiology and treatment approaches in schizophrenia is challenged in part by the heterogeneity of this disorder. One encouraging progress is the growing evidence that there are subtypes of schizophrenia that may relate to disease duration and premorbid severity. Recent in vitro findings of messenger ribonucleic acid (mRNA) gene expression on postmortem dorsolateral prefrontal cortex (DLPFC) showed that schizophrenia has two subtypes, those with a relatively normal DLPFC transcriptome (Type 1) and those with differentially expressed genes (Type 2). Sphingosine-1-phosphate receptor-1 (S1PR1) is one of the genes that was highly upregulated in Type 2 compared to Type 1 and controls. The impact of that finding is limited because it only can be confirmed through analysis of autopsy tissue, and the clinical characteristics such as symptoms severity or illness duration was not available from that Medical Examiner based autopsy study. However, S1PR1 has great potential because it is a target gene that can be accessed via positron emission tomography (PET) in vivo using specific radioligands (starting with [11C]CS1P1) successfully developed at our center in human brain imaging. As a preliminary study to validate this PET target in schizophrenia, S1PR1 protein expression was assessed by receptor autoradiography (ARG) using [3H]CS1P1 and immunohistochemistry (IHC) in the DLPFC from patients with schizophrenia classified as Type 1 or Type 2 based on their DLPFC transcriptomes and from controls. Our analyses demonstrate that ARG S1PR1 protein expression is significantly higher in Type 2 compared to Type 1 (p < 0.05) and controls (p < 0.05), which was consistent with previous mRNA S1PR1. These findings support the possibility that PET S1PR1 can be used as a future imaging biomarker to distinguish these subgroups of schizophrenic patients during life with obvious implications for both patient management and the design of clinical trials to validate novel pharmacologic therapies.
DeepImageTranslator: a free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis
Authors: Ye Run Zhou , Noll Christophe , Richard Gabriel , Lepage Martin , Turcotte Éric E. , Carpentier André C. 
Published: May 2021

ABSTRACTObjectivesThe advent of deep-learning has set new standards in an array of image translation applications. At present, the use of these methods often requires computer programming experience. Non-commercial programs with graphical interface usually do not allow users to fully customize their deep-learning pipeline. Therefore, our primary objective is to provide a simple graphical interface that allows students and researchers with no programming experience to easily create, train, and evaluate custom deep-learning models for image translation. We also aimed to test the applicability of our tool (the DeepImageTranslator) in two different tasks: semantic segmentation and noise reduction of CT images.MethodsThe DeepImageTranslator was implemented using the Tkinter library; backend computations were implemented using Pillow, Numpy, OpenCV, Augmentor, Tensorflow, and Keras libraries. Convolutional neural networks (CNNs) were trained using DeepImageTranslator and assessed with three-way cross-validation. The effects of data augmentation, deep-supervision, and sample size on model accuracy were also systematically assessed.ResultsThe DeepImageTranslator a simple tool that allows users to customize all aspects of their deep-learning pipeline, including the CNN, the training optimizer, the loss function, and the types of training image augmentation scheme. We showed that DeepImageTranslator can be used to achieve state-of-the-art accuracy and generalizability in semantic segmentation and noise reduction. Highly accurate 3D segmentation models for body composition can be obtained using training sample sizes as small as 17 images. Therefore, for studies with small datasets, researchers can randomly select a very small subset of images for manual labeling, which can then be used to train a specialized CNN model with DeepImageTranslator to fully automate segmentation of the entire dataset, thereby saving tremendous time and effort.ConclusionsAn open-source deep-learning tool for accurate image translation with a user-friendly graphical interface was presented and evaluated. This standalone software can be downloaded for Windows 10 at: https://sourceforge.net/projects/deepimagetranslator/
Modulation of P2X4 pore closure by magnesium, potassium, and ATP
Authors: Immadisetty Kalyan , Alenciks Josh , Kekenes-Huskey Peter 
Published: May 2021

ABSTRACTThe P2X4 receptor plays a prominent role in cellular responses to extracellular ATP. Through classical all-atom molecular dynamics (MD) simulations totaling 24 µs we have investigated how metal-complexed ATP stabilizes the channel’s open state and prevents its closing. We have identified two metal-binding sites, magnesium (Mg2+) and potassium (K+), one at the intersection of the three subunits in the ectodomain (MBS1) and the second one near the ATP binding site (MBS2), similar to those characterized in Gulf coast P2X. Our data indicate that when Mg2+ and K+ ions are complexed with ATP, the channel is locked into an open state. Interestingly, irrespective of the number of bound ATP molecules, Mg2+ ions bound to the MBS2 resisted collapsing of the open state protein to a closed state by stabilizing the ATP-protein interactions. However, when Mg2+ in the MBS2 was replaced with K+ ions, as might be expected when in equilibrium with an extracellular solution, the interactions between the subunits were weakened and we found evidence of pore collapse. This collapse was apparent when fewer than two ATP were bound to MBS2 in the presence of K+. Therefore, the different capacities of common cations to stabilize the channel may underlie a mechanism governing P2X4 channel gating in physiological systems. This study provides structural insights into the differential modulation of ATP activation of P2X4 by Mg2+ and K+.
Spatial modeling of tidal and land use impacts on Escherichia coli contamination and fluctuation in Asia’s urban river environmental gradient
Authors: Wibowo Andri 
Published: May 2021

AbstractIn the river, pathogens are the leading cause for rivers to exceed water and health quality standards. The GIS based spatial modeling and analysis were conducted to estimate Escherichia coli (E. coli) contaminated river bodies based on environmental and spatial gradients such as dissolved oxygen, pH, tidal, temperature and current in Asia’s urban river located in Kapuas river of Kalimantan. The E. coli was sampled from the river mouth up to the upstream land uses dominated by residential. The E. coli contamination was higher in the river mouth and in residential area as well. Likewise, E. coli contamination was higher during the low tide than high tide. During the high tide, the E. coli contamination were significantly affected by temperature and current (r2>0.5). Meanwhile, during the low tide, there were no dominant environmental factors that affect E. coli contamination. Hence, by knowing the spatial model of E. coli contamination driven by tidal, land use and environmental gradients, this paper has contributed to the advance management of water and river system.
OmpF Downregulation Mediated by Sigma E or OmpR Activation Confers Cefalexin Resistance in Escherichia coli in the Absence of Acquired β-Lactamases
Authors: Alzayn Maryam , Dulyayangkul Punyawee , Satapoomin Naphat , Heesom Kate J. , Avison Matthew B. 
Published: May 2021

AbstractCefalexin is a widely used 1st generation cephalosporin, and resistance in Escherichia coli is caused by Extended-Spectrum (e.g. CTX-M) and AmpC β-lactamase production and therefore frequently coincides with 3rd generation cephalosporin resistance. However, we have recently identified large numbers of E. coli isolates from human infections, and from cattle, where cefalexin resistance is not β-lactamase mediated. Here we show, by studying laboratory selected mutants, clinical isolates, and isolates from cattle, that OmpF porin disruption or downregulation is a major cause of cefalexin resistance in E. coli. Importantly, we identify multiple regulatory mutations that cause OmpF downregulation. In addition to mutation of ompR, already known to downregulate OmpF and OmpC porin production, we find that rseA mutation, which strongly activates the Sigma E regulon, greatly increasing DegP production, which degrades OmpF, OmpC and OmpA porins. Furthermore, we reveal that mutations affecting lipopolysaccharide structure, exemplified by the loss of GmhB, essential for lipopolysaccharide heptosylation, also modestly activate DegP production, resulting in OmpF degradation. Remarkably, given the critical importance attached to such systems for normal E. coli physiology, we find evidence for DegP-mediated OmpF downregulation, gmhB and rseA loss of function mutation in E. coli isolates derived from human infections. Finally, we show that these regulatory mutations enhance the ability of group 1 CTX-M β-lactamase to confer reduced carbapenem susceptibility, particularly those mutations that cause OmpC in addition to OmpF downregulation.
Identification of SSX2IP/Msd1 as a Wtip-binding partner by targeted proximity biotinylation
Authors: Xiang Bo , Reis Alice H. , Itoh Keiji , Sokol Sergei Y. 
Published: May 2021

AbstractWilms tumor-1-interacting protein (Wtip) is a LIM-domain-containing adaptor that links cell junctions with actomyosin complexes and modulates actomyosin contractility and ciliogenesis in Xenopus embryos. The Wtip C-terminus with three LIM domains binds binds Shroom3 and modulates Shroom3-induced apical constriction in ectoderm cells. We found that the N-terminal domain localizes to the basal bodies in skin multiciliated cells, but its interacting partners remain largely unknown. Using a novel targeted proximity biotinylation approach with anti-GFP antibody attached to the biotin ligase BirA in the presence of GFP-Wtip-N, we identified SSX2IP as the candidate binding protein. SSX2IP, also known as Msd1 or ADIP, is a centriolar satellite protein that functions as a targeting factor for ciliary membrane proteins. Wtip physically associated with SSX2IP and the two proteins formed mixed spherical aggregates in overexpressing cells in a dose-dependent manner, in a process that resembles phase separation. These results suggest that the interaction between SSX2IP and Wtip is relevant to their functions at the centrosome and basal bodies. The described antibody targeting of biotin ligase should be applicable to other GFP-tagged proteins.
Mutational signatures of complex genomic rearrangements in human cancer
Authors: Bao Lisui , Zhong Xiaoming , Yang Yang , Yang Lixing 
Published: May 2021

AbstractComplex genomic rearrangements (CGRs) are common in cancer and are known to form via two aberrant cellular structures—micronuclei and chromatin bridge. However, which mechanism is more relevant to CGR formation in cancer cells and whether there are other undiscovered mechanisms remain open questions. Here, we analyze 2,014 CGRs from 2,428 whole-genome sequenced tumors and deconvolute six CGR signatures based on the topology of CGRs. Through rigorous benchmarking, we show that our CGR signatures are highly accurate and biologically meaningful. Three signatures can be attributed to known biological processes—micronuclei- and chromatin-bridge-induced chromothripsis and extrachromosomal DNA. More than half of the CGRs belong to the remaining three newly discovered signatures. A unique signature (we named “hourglass chromothripsis”) with highly localized breakpoints and small amount of DNA loss is abundant in prostate cancer. Through genetic association analysis, we find SPOP as a candidate gene causing hourglass chromothripsis and playing important role in maintaining genome integrity. Our study offers valuable insights into the formation of CGRs.
Detection of fusion transcripts and their genomic breakpoints from RNA sequencing data
Authors: Hoogstrate Youri , Komor Malgorzata A. , Böttcher René , van Riet Job , van de Werken Harmen J. G. , van Lieshout Stef , Hoffmann Ralf , van den Broek Evert , Bolijn Anne S. , Dits Natasja , Sie Daoud , van der Meer David , Pepers Floor , Bangma Chris H. , van Leenders Geert J. L. H. , Smid Marcel , French Pim , Martens John W.M. , van Workum Wilbert , van der Spek Peter J. , Janssen Bart , Caldenhoven Eric , Rausch Christian , de Jong Mark , Stubbs Andrew P. , Meijer Gerrit A. , Fijneman Remond J.A. , Jenster Guido 
Published: May 2021

AbstractSpliced fusion-transcripts are typically identified by RNA-seq without elucidating the causal genomic breakpoints. However, non poly(A)-enriched RNA-seq contains large proportions of intronic reads spanning also genomic breakpoints. Using 1.274 RNA-seq samples, we investigated what additional information is embedded in non poly(A)-enriched RNA-seq data. Here, we present our novel, graph-based, Dr. Disco algorithm that makes use of both intronic and exonic RNA-seq reads to identify not only fusion transcripts but also genomic breakpoints in gene but also in intergenic regions. Dr. Disco identified TMPRSS2-ERG fusions with genomic breakpoints and other transcribed rearrangements from multiple RNA-sequencing cohorts. In breast cancer and glioma samples Dr. Disco identified rearrangement hotspots near CCND1 and MDM2 and could directly associate this with increased expression. A comparison with matched DNA-sequencing revealed that most genomic breakpoints are not, or minimally, transcribed while also revealing highly expressed translocations missed by DNA-seq. By using the full potential of non poly(A)-enriched RNA-seq data, Dr. Disco can reliably identify expressed genomic breakpoints and their transcriptional effects.
A Meta-Analysis of fMRI Studies of Semantic Cognition in Children
Authors: Enge Alexander , Rahman Rasha Abdel , Skeide Michael A. 
Published: May 2021

AbstractOur capacity to derive meaning from things that we see and words that we hear is unparalleled in other animal species and current AI systems. Despite a wealth of functional magnetic resonance imaging (fMRI) studies on where different semantic features are processed in the adult brain, the development of these systems in children is poorly understood. Here we conducted an extensive database search and identified 50 fMRI experiments investigating semantic world knowledge, semantic relatedness judgments, and the differentiation of visual semantic object categories in children (total N = 1,018, mean age = 10.1 years, range 4–15 years). Synthesizing the results of these experiments, we found consistent activation in the bilateral inferior frontal gyri (IFG), fusiform gyri (FG), and supplementary motor areas (SMA), as well as in the left middle and superior temporal gyri (MTG/STG). Within this system, we found little evidence for age-related changes across childhood and high overlap with the adult semantic system. In sum, the identification of these cortical areas provides the starting point for further research on the mechanisms by which the developing brain learns to make sense of its environment.
Rhizome trait scaling relationships are modulated by growth conditions and are linked to plant fitness
Authors: Thakur Dinesh , Münzbergová Zuzana 
Published: May 2021

ABSTRACTBackground and AimRhizomes are important organs allowing many clonal plants to persist and reproduce under stressful climates with longer rhizomes indicating enhanced ability of the plants to spread vegetatively. We do not however, know either how rhizome construction costs change with increasing length or vary with environmental conditions.MethodsWe analysed the rhizome length vs mass scaling relationship, the plasticity in the scaling relationships, their genetic basis, and how scaling relationships are linked to plant fitness. We used data from 275 genotypes of a clonal grass Festuca rubra originating from 11 localities and cultivated under four contrasting climates. Data were analysed using standard major axis regression, mixed-effect regression models and a structural equation model.Key ResultsRhizome construction costs increased (i.e., lower specific rhizome length) with increasing length. The trait scaling relationships were modulated by cultivation climate and its effects also interacted with the climate of origin of the experimental plants. With increasing length, increasing moisture led to a greater increase in rhizome construction costs. Plants with lower rhizome construction costs showed significantly higher fitness.ConclusionsThis study suggests that rhizome scaling relationships are plastic, but also show genetic differentiation and are linked to plant fitness. Therefore, to persist under variable environments modulation in scaling relationships could be an important plants’ strategy.
Schema representations in distinct brain networks support narrative memory during encoding and retrieval
Authors: Masís-Obando Rolando , Norman Kenneth A. , Baldassano Christopher 
Published: May 2021

AbstractSchematic prior knowledge can scaffold the construction of event memories during perception and also provide structured cues to guide memory search during retrieval. We measured the activation of story-specific and schematic representations using fMRI while participants were presented with 16 stories and then recalled each of the narratives, and related these activations to memory for specific story details. We predicted that schema representations in mPFC would be correlated with successful recall of story details. In keeping with this prediction, an anterior mPFC region showed a significant correlation between activation of schema representations at encoding and subsequent behavioral recall performance; however, this mPFC region was not implicated in schema representation during retrieval. More generally, our analyses revealed largely distinct brain networks at encoding and retrieval in which schema activation was related to successful recall. These results provide new insight into when and where event knowledge can support narrative memory.
Compensatory functional connectome changes in a rat model of traumatic brain injury
Authors: Yang Zhihui , Zhu Tian , Pompilus Marjory , Fu Yueqiang , Zhu Jiepei , Arjona Kefren , Arja Rawad Daniel , Grudny Matteo M. , Plant H. Daniel , Bose Prodip , Wang Kevin K. , Febo Marcelo 
Published: May 2021

AbstractPenetrating cortical impact injuries alter neuronal communication beyond the injury epicenter, across regions involved in affective, sensorimotor, and cognitive processing. Understanding how traumatic brain injury (TBI) reorganizes local and brain wide nodal functional interactions may provide valuable quantitative parameters for monitoring pathological progression and functional recovery. To this end, we investigated spontaneous fluctuations in the functional magnetic resonance imaging (fMRI) signal obtained at 11.1 Tesla in rats sustaining controlled cortical impact (CCI) and imaged at 2- and 30-days post-injury. Graph theory-based calculations were applied to weighted undirected matrices constructed from 12,879 pairwise correlations between fMRI signals from 162 regions. Our data indicate that on days 2 and 30 post-CCI there is a significant increase in connectivity strength in nodes located in contralesional cortical, thalamic, and basal forebrain areas. Rats imaged on day 2 post-injury had significantly greater network modularity than controls, with influential nodes (with high eigenvector centrality) contained within the contralesional module and participating less in cross-modular interactions. By day 30, modularity and cross-modular interactions recover, although a cluster of nodes with low strength and low eigenvector centrality remain in the ipsilateral cortex. Our results suggest that changes in node strength, modularity, eigenvector centrality, and participation coefficient track early and late TBI effects on brain functional connectivity. We propose that the observed compensatory functional connectivity reorganization in response to CCI may be unfavorable to brain wide communication in the early post-injury period.
Deep Learning Assisted Mechanotyping of Individual Cells Through Repeated Deformations and Relaxations in Undulating Channels
Authors: Combs Cody T. , Seith Daniel D. , Bovyn Matthew J. , Gross Steven P. , Xie Xiaohui , Siwy Zuzanna S. 
Published: May 2021

AbstractMechanical properties of cells are important features that are tightly regulated, and are dictated by various pathologies. Deformability cytometry allows for the characterization of mechanical properties of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that would assure classification accuracy approaching 100%. In order to maximize the accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations. We also track the shape dynamics of individual cells with high time resolution, and apply sequence-based deep learning models for feature extraction. HL60 cells with and without treatment with cytochalasin D (cytoD), a reagent previously shown to perturb the actin network, were used as a model system to understand the classification potential of our approach. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes, and shown to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified the two sub-populations of HL60 cells with an accuracy of 95%. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry.