bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unraveling the relationship between cancer and life history traits in vertebrates
Authors: Bulls Stephanie E. , Platner Laura , Ayub Wania , Moreno Nickolas , Arditi Jean-Pierre , Dreyer Saskia , McCain Stephanie , Wagner Philipp , Burgstaller Silvia , Davis Leyla R. , Bruins - van Sonsbeek Linda GR. , Fischer Dominik , Lynch Vincent J. , Claude Julien , Glaberman Scott , Chiari Ylenia 
Published: July 2022

ABSTRACTIdentifying species with unusually low cancer prevalence can provide new insights into cancer resistance. Most studies have focused on mammals, but the genetic, physiological, and ecological diversity among vertebrates can influence cancer susceptibility. We used necropsies from over a thousand species of amphibians, birds, crocodilians, mammals, squamates, and turtles to investigate relationships between cancer prevalence, intrinsic cancer risk, body mass, and lifespan. Previous studies often relied on species averages, leading to inaccurate interpretations. Our innovative statistical approach uses raw cancer data and resampling to improve accuracy. We found remarkably low cancer prevalence in turtles, high prevalence in squamates and mammals, and lower-than-expected prevalence based on lifespan and body mass in multiple groups. Our results show lifespan influences neoplasia and malignancy transformation rates in mammals, while body mass affects neoplasia prevalence in amphibians and squamates. These data reveal a complex relationship between life history traits and cancer risk, identifying vertebrates with potential novel cancer resistance mechanisms.STATEMENT OF SIGNIFICANCEBiodiversity is an untapped natural resource for understanding cancer. Our study reveals a wide divergence in cancer prevalence among vertebrate groups, with notably low rates in turtles and high rates in squamates and mammals. These findings can lead to new breakthroughs in understanding the biology of cancer.
Including Phylogenetic Conservatism of Shortgrass Prairie Restoration Species Does Not Improve Species Germinability Prediction
Authors: Chen Yanni , Schwilk Dylan W. , Cox Robert D. , Johnson Matthew G. 
Published: July 2022

ABSTRACTPREMISEWe investigated whether phylogenetic conservatism can improve the performance of seed germinability prediction models. Previous studies in tallgrass prairie and alpine meadow revealed that seed morphological traits demonstrate phylogenetic conservatism. We hypothesized that phylogenetic conservatism in seed traits could help predict the seed germinability, under the assumption that seed traits contain phylogenetic signals.METHODSWe measured seed germination percentage and seed morphological traits (seed mass, seed height, and seed surface area) on 34 native species from shortgrass prairie in North America. We supplemented these data with similar data from the literature on 11 more species. We calculated the robustness of the phylogenetic signal of each trait to the number of species sampled. We also compressed the phylogenetic distance matrix to a two-dimensional space, and applied the Akaike information criterion to evaluate the effects of phylogeny on seed germinability prediction models.KEY RESULTSWe found weak but significant phylogenetic signals in seed mass and seed height in the full data set. These phylogenetic signals were not able to improve seed germinability prediction model performance among shortgrass prairie species. Our robustness tests of phylogenetic signals using random sub-sampling showed that the detection rate of phylogenetic signals in seed mass was increased along with the expansion of species pool, and nearly 100% at 40 species. However, the detection rate of phylogenetic signals in seed height was constantly low, around 20%.CONCLUSIONSWhen the phylogenetic signals are weak, the phylogenetic position does not improve germinability prediction model performance. Therefore, phylogenetic signals detected during a single species pool calculation may not accurately reflect the phylogenetic conservatism of the trait in a plant community. We suggest testing for robustness of phylogenetic signals using random sub-sampling tests.
Visualization of Retroplacental Clear Space Disruption in a Mouse Model of Placental Accreta
Authors: Badachhape Andrew A. , Bhandari Prajwal , Devkota Laxman , Srivastava Mayank , Tanifum Eric A. , George Verghese , Fox Karin A. , Yallampalli Chandrasekhar , Annapragada Ananth V. , Ghaghada Ketan B. 
Published: July 2022

AbstractIntroductionPrior preclinical studies established the utility of liposomal nanoparticle blood-pool contrast agents in visualizing the retroplacental clear space (RPCS), a marker of normal placentation, while sparing fetuses from exposure because the agent does not cross the placental barrier. In this work we characterized RPCS disruption in a mouse model of placenta accreta spectrum (PAS) using these agents.MethodsContrast-enhanced MRI (CE-MRI) and computed tomography (CE-CT) using liposomal nanoparticles bearing gadolinium (liposomal-Gd) and iodine were performed in pregnant Gab3-/- and wild type (WT) mice at day 16 of gestation. CE-MRI was performed on a 1T scanner using a 2D T1-weighted sequence (100×100×600 µm3 voxels) and CE-CT was performed at a higher resolution (70×70×70 µm3 voxels). Animals were euthanized post-imaging and feto-placental units (FPUs) were harvested for histological examination. RPCS conspicuity was scored through blinded assessment of images.ResultsPregnant Gab3-/- mice show elevated rates of complicated pregnancy. Contrast-enhanced imaging demonstrated frank infiltration of the RPCS of Gab3-/- FPUs. RPCS in Gab3-/- FPUs was smaller in volume, demonstrated a heterogeneous signal profile, and received lower conspicuity scores than WT FPUs. Histology confirmed in vivo findings and demonstrated staining consistent with a thinner RPCS in Gab3-/- FPUs.DiscussionImaging of the Gab3-/- mouse model at late gestation with liposomal contrast agents enabled in vivo characterization of morphological differences in the RPCS that could cause the observed pregnancy complications. An MRI-based method for visualizing the RPCS would be valuable for early detection of invasive placentation.
ModelArray: a memory-efficient R package for statistical analysis of fixel data
Authors: Zhao Chenying , Tapera Tinashe M. , Bagautdinova Joëlle , Bourque Josiane , Covitz Sydney , Gur Raquel E. , Gur Ruben C. , Larsen Bart , Mehta Kahini , Meisler Steven L. , Murtha Kristin , Muschelli John , Roalf David R. , Sydnor Valerie J. , Valcarcel Alessandra M. , Shinohara Russell T. , Cieslak Matthew , Satterthwaite Theodore D. 
Published: July 2022

ABSTRACTDiffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel data exist, currently available tools are memory intensive, difficult to scale to large datasets, and support only a limited number of statistical models. Here we introduce ModelArray, a memory-efficient R package for mass-univariate statistical analysis of fixel data. With only several lines of code, even large fixel datasets can be analyzed using a standard personal computer. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n=938). ModelArray required far less memory than existing tools and revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides an efficient and flexible platform for statistical analysis of fixel data.HIGHLIGHTSModelArray is an R package for mass-univariate statistical analysis of fixel dataModelArray is memory-efficient even for large-scale datasetsModelArray supports linear and nonlinear modeling and is extensible to more modelsModelArray facilitates easy statistical analysis of large-scale fixel dataGraphical abstract
Dual-stream cortical feedbacks mediate sensory prediction
Authors: Chu Qian , Ma Ou , Hang Yuqi , Tian Xing 
Published: July 2022

AbstractPredictions are constantly generated from diverse sources to optimize cognitive functions in the ever-changing environment. However, the neural origin and generation process of top-down induced prediction remain elusive. We hypothesized that motor-based and memory-based predictions are mediated by distinct feedback networks from motor and memory systems to the sensory cortices. Using fMRI and a dual imagery paradigm, we showed that motor and memory upstream systems excited the auditory cortex in a content-specific manner. Moreover, the inferior and posterior parts of the parietal lobe differentially relayed predictive signals in motor-to-sensory and memory-to-sensory networks. Our results reveal the functionally distinct neural networks that mediate top-down sensory prediction and ground the neurocomputational basis of predictive processing.
Genome assembly of the polyclad flatworm Prostheceraeus crozieri
Authors: Leite Daniel J. , Piovani Laura , Telford Maximilian J. 
Published: July 2022

AbstractPolyclad flatworms are widely thought to be one of the least derived of the flatworm classes and, as such, are well placed to investigate evolutionary and developmental features such as spiral cleavage and larval diversification lost in other platyhelminths. Prostheceraeus crozieri, formerly Maritigrella crozieri, is an emerging model polyclad flatworm that already has some useful transcriptome data but, to date, no sequenced genome. We have used high molecular weight DNA extraction and long read PacBio sequencing to assemble the highly repetitive (67.9%) P. crozieri genome (2.07 Gb). We have annotated 43,325 genes, with 89.7% BUSCO completeness. Perhaps reflecting its large genome, introns were considerably larger than other free-living flatworms, but evidence of abundant transposable elements suggests genome expansion has been principally via transposable elements activity. This genome resource will be of great use for future developmental and phylogenomic research.
Qualitative and quantitative analysis of the constitutive bark of Q. ilex x Q. suber hybrids
Authors: de Burgos Gonzalo , Díez-Morales Eduardo , López de Heredia Unai , Soto Álvaro 
Published: July 2022

AbstractHybridization and introgression between cork oak (Quercus suber) and holm oak (Q. ilex) have traditionally been reckoned as undesirable processes, since hybrid individuals lack the profitable bark characteristics of cork oak. Nevertheless, a systematic and quantitative description of the bark of these hybrids at the microscopic level based on a significant number of individuals was not available to date.In this work we provide such a qualitative and quantitative description, identifying the most relevant variables for their classification. Hybrids show certain features intermediate between those of the parent species, as well as other unique features, as the general suberization of inactive phloem, reported here for the first time. These results suggest a relevant hybridization-induced modification of the genetic expression patterns. Therefore, hybrid individuals provide a valuable material to disentangle the molecular mechanisms underpinning bark development in angiosperms.
Noise correlations in the owl optic tectum are differentially impacted by unimodal vs crossmodal competitors
Authors: Totten Douglas J , DeBello William M 
Published: July 2022

ABSTRACTMultisensory integration in mammalian superior colliculus and its avian homolog, the optic tectum, is essential for rapid orienting towards salient stimuli. The benefits of combining across modalities is expected to depend in part on interneuronal noise correlations (NCs) and trial-to-trial response variance (Fano factor; FF), yet there is scant data on these topics. Here we used multielectrode arrays (MEAs) to record simultaneously from cohorts of single units in the deep layers of the owl optic tectum (OTd). Stimuli were presented individually or simultaneously as spatially aligned or non-aligned competitors. NC values varied from -0.35 to 0.94, an unexpectedly large range, and decreased only modestly as receptive fields diverged. Spatially aligned bimodal stimuli summed in a largely additive fashion over a wide range of response magnitudes. Most of the observed variance in bimodal NCs and FFs could be accounted for by an additive rule without accounting for internal noise. For non-aligned stimuli, crossmodal competitors decreased FFs, whereas unimodal competitors did not. Most striking, visual competitors decreased NCs for auditory drivers but increased NCs for visual drivers, indicating that the OTd network is differentially wired to process bimodal competitors. In total, these data provide novel descriptive information regarding the correlational structure of OTd neurons underlying multisensory processing.SIGNIFICANCE STATEMENTIn the owl auditory midbrain and its mammalian homologs, little is known regarding the ensemble response properties during multisensory integration. Using microelectrode arrays we found that noise correlations between pairs of neurons evoked by auditory, visual and bimodal stimuli were large and variable. When two non-aligned (competitor) stimuli were presented simultaneously, response reliability and noise correlations were driven down for crossmodal but not unimodal competitors, which suggests distinct processing strategies. This novel finding predicts that visual competitors drive an increase in localization accuracy for auditory targets.
Rewiring Dynamics of Functional Connectome in Motor Cortex during Motor Skill Learning
Authors: Meamardoost Saber , Hwang EunJung , Bhattacharya Mahasweta , Ren Chi , Wang Linbing , Mewes Claudia , Zhang Ying , Komiyama Takaki , Gunawan Rudiyanto 
Published: July 2022

AbstractThe brain’s connectome continually rewires throughout the life of an organism. In this study, we sought to elucidate the operational principles of such rewiring by analyzing the functional connectomes in mouse primary motor cortex (M1) during a 14-session (day) lever-press task learning in response to an auditory cue. Specifically, we employed Calcium imaging recordings of L2/3 and L5 of M1 in awake mice to reconstruct and analyze functional connectomes across learning sessions. Our results show that functional connectomes in L2/3 and L5 follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with higher motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward / performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning.Significance StatementConnectomes in the motor cortex rewire during motor skill learning, but the operational principle behind this rewiring is yet to be determined. Here, we characterized the rewiring dynamics of functional connectomes in L2/3 and L5 of M1 in mice engaging in a lever-press learning, using two-photon fluorescence microscopy data. We identified a universal biphasic rewiring trajectory across animals and layers in the motor cortex that reflects two objectives: an exploratory phase that increases functional connectivity and optimizes motor performance, and a pruning phase that brings connectivity back to a homeostatic level while maintaining motor performance. We found further that connectome rewiring during motor skill learning concentrates around a core set of highly interconnected neurons in L2/3 that reliably encode movements.
Innexin-mediated adhesion between glia is required for axon ensheathment in the peripheral nervous system
Authors: Das Mriga , Cheng Duo , Matzat Till , Auld Vanessa J. 
Published: July 2022

ABSTRACTGlia are essential to protecting and enabling nervous system function and a key glial function is the formation of the glial sheath around peripheral axons. Each peripheral nerve in the Drosophila larva is ensheathed by three glial layers, which structurally support and insulate the peripheral axons. How peripheral glia communicate with each other and between layers is not well established and we investigated the role of Innexins in mediating glial function in the Drosophila periphery. Of the eight Drosophila Innexins, we found two (Inx1 and Inx2) are important for peripheral glia development. In particular loss of Inx1 and Inx2 resulted in defects in the wrapping glia leading to disruption of the glia wrap. Of interest loss of Inx2 in the subperineurial glia also resulted in defects in the neighbouring wrapping glia. Inx plaques were observed between the subperineurial glia and the wrapping glia suggesting that gap junctions link these two glial cell types. We found Inx2 is key to Ca+2 pulses in the peripheral subperineurial glia but not in the wrapping glia, and we found no evidence of gap junction communication between subperineurial and wrapping glia. Rather we have clear evidence that Inx2 plays an adhesive and channel-independent role between the subperineurial and wrapping glia to ensure the integrity of the glial wrap.
Involuntary breathing movement pattern recognition and classification via force based sensors
Authors: Singh Rajat Emanuel , Fleury Jordan M. , Gupta Sonu , Bachman Nate P. , Alumbaugh Brent , White Gannon 
Published: July 2022

AbstractThe study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in a supine position on force plates. We segmented their data into the no-movement (NM) phase aka easy phase and IBM phase (comprising several events or sub-phases of IBM). The acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. The K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five-six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggle and were labeled as classes for classification using support vector machine (SVM), naive bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had a low computational cost and high robustness of 96–99% accuracy.
GBZ File Format for Pangenome Graphs
Authors: Sirén Jouni , Paten Benedict 
Published: July 2022

AbstractMotivationPangenome graphs representing aligned genome assemblies are being shared in the text-based Graphical Fragment Assembly format. As the number of assemblies grows, there is a need for a file format that can store the highly repetitive data space-efficiently.ResultsWe propose the GBZ file format based on data structures used in the Giraffe short read aligner. The format provides good compression, and the files can be efficiently loaded into in-memory data structures. We provide compression and decompression tools and libraries for using GBZ graphs, and we show that they can be efficiently used on a variety of systems.AvailabilityC++ and Rust implementations are available athttps://github.com/jltsiren/gbwtgraphandhttps://github.com/jltsiren/gbwt-rs, respectively.Contactjouni.siren@iki.fiSupplementary informationSupplementary data are available online.
Graph-Based Pharmacokinetic-Pharmadynamic Modeling for Large Scale Systems: Nanoparticles Case
Authors: Lazebnik Teddy , Weitman Hanna , Kaminka Gal A. 
Published: July 2022

AbstractPharmaceutical nanoparticles (NPs) carrying molecular payloads are used for medical purposes such as diagnosis and medical treatment. They are designed to modify the pharmacokinetics-pharmacodynamics (PKPD) of their associated payloads, to obtain better clinical results. Currently, the research process of discovering the PKPD properties of new candidates for efficient clinical treatment is complicated and time-consuming.In silicoexperiments are known to be powerful tools for studying biological and clinical processes and therefore can significantly improve the process of developing new and optimizing current NPs-based drugs. However, the current PKPD models are limited by the number of parameters they can take into consideration and the ability to solve large-scalein vivosettings, thus providing relatively large errors in predicting treatment outcomes. In this study, we present a novel mathematical graph-based model for PKPD of NPs-based drugs. The proposed model is based on a population of NPs performing a directed walk on a graph describing the blood vessels and organs, taking into consideration the interactions between the NPs and their environment. In addition, we define a mechanism to perform different prediction queries on the proposed model to analyze twoin vivoexperiments with eight different NPs, done on mice, obtaining a fitting of 0.84 ± 0.01 and 0.66 ± 0.01 (mean ± standard deviation), respectively, comparing thein vivovalues and thein silicoresults.
In search of a brain microbiome: A machine learning search pipeline for electron microscopy images of brain tissue
Authors: Matelsky Jordan K. , Shih Celina , Hijazi Khalil , Johnson Erik C. 
Published: July 2022

AbstractThe healthy human brain has long been considered a sterile environment, with the blood brain barrier preventing the formation of a bacterial brain microbiome. Recent electron microscopy (EM) imaging of brain tissue has, however, provided the first preliminary evidence of bacteria in otherwise healthy brain slices. Whether due to contamination, disease, or a previously unknown relationship of bacteria to healthy brain tissue, novel tools are needed to detect and search for bacteria in nanoscale, volumetric EM images. While computer vision tools are widely used in cell segmentation and object detection problems in EM imaging, no bacteria detection tool or dataset exists. Overcoming the rarity of training data, this work presents the first pipeline for training a bacteria detection network for EM images, leveraging existing deep networks for object detection. A deployment and proofreading pipeline is presented, along with characterization of deployment to public EM image datasets. While bacteria in healthy brain tissue were not discovered in this work, this tool presents an opportunity for large scale bacteria search in EM imaging for both scientific discovery and experimental quality control, and serves more generally as a framework for sparse object detection in large imagery datasets.