bluuebunny/crossref_metadata_2025_split huggingface dataset:
Orthogonal axes of microbiome variation associated with functionally distinct transcriptomic signatures in the gut of wildDrosophila melanogaster
Authors: Llanwarne Frances , Dobson Adam J 
Published: April 2023

AbstractGut microbiota are fundamental for healthy animal function, but the evidence that host function can be predicted from microbiota taxonomy remains equivocal, and natural populations remain understudied compared to laboratory animals. Paired analyses of covariation in microbiota and host parameters are powerful approaches to characterise host-microbiome relationships mechanistically, especially in wild populations of animals that are also lab models, enabling insight into the ecological basis of host function at a molecular and cellular level. The fruitflyDrosophila melanogasteris a preeminent model organism, amenable to field investigation by’omic analyses. Here we present an analysis of wild maleD. melanogaster, with paired measurements of (A) bacterial diversity and abundance, measured by 16S amplicon sequencing; and (B) the host gut transcriptome. We found orthogonal axes of microbial genera, which correspond to differential expression of host genes. The differentially-expressed gene sets were enriched in functions including protein translation, mitochondrial respiration, immunity and reproduction. Each gene set had a distinct functional signature, suggesting that wild flies exhibit a range of distinct axes of functional variation, which correspond to orthogonal axes of microbiome variation. These findings strengthen the bridge between the wild ecology and functional genetics of a leading host-microbiome model.
Metabolic Reprogramming by Histone Deacetylase Inhibition Selectively Targets NRF2-activated tumors
Authors: Karagiannis Dimitris , Wu Warren , Li Albert , Hayashi Makiko , Chen Xiao , Yip Michaela , Mangipudy Vaibhav , Xu Xinjing , Sánchez-Rivera Francisco J. , Soto-Feliciano Yadira M. , Ye Jiangbin , Papagiannakopoulos Thales , Lu Chao 
Published: April 2023

AbstractInterplay between metabolism and chromatin signaling have been implicated in cancer initiation and progression. However, whether and how metabolic reprogramming in tumors generates specific epigenetic vulnerabilities remain unclear. Lung adenocarcinoma (LUAD) tumors frequently harbor mutations that cause aberrant activation of the NRF2 antioxidant pathway and drive aggressive and chemo-resistant disease. We performed a chromatin-focused CRISPR screen and report that NRF2 activation sensitized LUAD cells to genetic and chemical inhibition of class I histone deacetylases (HDAC). This association was consistently observed across cultured cells, syngeneic mouse models and patient-derived xenografts. HDAC inhibition causes widespread increases in histone H4 acetylation (H4ac) at intergenic regions, but also drives re-targeting of H4ac reader protein BRD4 away from promoters with high H4ac levels and transcriptional downregulation of corresponding genes. Integrative epigenomic, transcriptomic and metabolomic analysis demonstrates that these chromatin changes are associated with reduced flux into amino acid metabolism andde novonucleotide synthesis pathways that are preferentially required for the survival of NRF2-active cancer cells. Together, our findings suggest that metabolic alterations such as NRF2 activation could serve as biomarkers for effective repurposing of HDAC inhibitors to treat solid tumors.
Protein homeostasis from diffusion-dependent control of protein synthesis and degradation
Authors: Chen Yuping , Huang Jo-Hsi , Phong Connie , Ferrell James E. 
Published: April 2023

SummaryIt has been proposed that the concentration of proteins in the cytoplasm maximizes the speed of important biochemical reactions. Here we have used theXenopusextract system, which can be diluted or concentrated to yield a range of cytoplasmic protein concentrations, to test the effect of cytoplasmic concentration on mRNA translation and protein degradation. We found that protein synthesis rates are maximal in ∼1x cytoplasm, whereas protein degradation continues to rise to an optimal concentration of ∼1.8x. This can be attributed to the greater sensitivity of translation to cytoplasmic viscosity, perhaps because it involves unusually large macromolecular complexes like polyribosomes. The different concentration optima sets up a negative feedback homeostatic system, where increasing the cytoplasmic protein concentration above the 1x physiological level increases the viscosity of the cytoplasm, which selectively inhibits translation and drives the system back toward the 1x set point.
Sizes, conformational fluctuations, and SAXS profiles for Intrinsically Disordered Proteins
Authors: Mugnai Mauro L. , Chakraborty Debayan , Kumar Abhinaw , Nguyen Hung T. , Zeno Wade , Stachowiak Jeanne C. , Straub John E. , Thirumalai D. 
Published: April 2023

AbstractThe preponderance of Intrinsically Disordered Proteins (IDPs) in the eukaryotic proteome, and their ability to interact with each other, folded proteins, RNA, and DNA for functional purposes, have made it important to quantitatively characterize their biophysical properties. Toward this end, we developed the transferable Self-Organized Polymer (SOP-IDP) model to calculate the properties of several IDPs. The values of the radius of gyration (Rg) obtained from SOP-IDP simulations are in excellent agreement (correlation coefficient of 0.96) with those estimated from SAXS experiments. For AP180 and Epsin, the predicted values of the hydrodynamic radii (Rhs) are in quantitative agreement with those from Fluorescence Correlation Spectroscopy (FCS) experiments. Strikingly, the calculated SAXS spectra for thirty-six IDPs are also nearly superimposable on the experimental profiles. The dependence ofRgand the mean end-to-end distance (Ree) on chain length,N, follows Flory’s scaling law,Rα≈aαN0.588(α=g, ande), suggesting that globally IDPs behave as synthetic polymers in a good solvent. The values ofag, andaeare 0.20 nm and 0.48 nm respectively. Surprisingly, finite size corrections to scaling, expected on theoretical grounds, are negligible forRgandRee. In contrast, only by accounting for the finite sizes of the IDPs, the dependence of experimentally measurableRhonNcan be quantitatively explained usingν= 0.588. Although Flory scaling law captures the estimates forRg,Ree, andRhaccurately, the spread of the simulated data around the theoretical curve is suggestive of of sequence-specific features that emerge through a fine-grained analysis of the conformational ensembles using hierarchical clustering. Typically, the ensemble of conformations partitiones into three distinct clusters, having different equilibrium populations and structural properties. Without any further readjustments to the parameters of the SOP-IDP model, we also obtained excellent agreement with paramagnetic relaxation enhancement (PRE) measurements forα-synuclein. The transferable SOP-IDP model sets the stage for several applications, including the study of phase separation in IDPs and interactions with nucleic acids.
Interpretable dimensionality reduction and classification of mass spectrometry imaging data in a visceral pain model via non-negative matrix factorization
Authors: Pathirage Kasun , Virmani Aman , Scott Alison J. , Traub Richard J. , Ernst Robert K. , Ghodssi Reza , Babadi Behtash , Abshire Pamela 
Published: April 2023

AbstractMass spectrometry imaging (MSI) is a powerful scientific tool for understanding the spatial distribution of biochemical compounds in tissue structures. MSI data analysis presents problems due to the large file sizes and computational resource requirements and also due to the complexity of interpreting the raw spectral data. Dimensionality reduction techniques that address the first issue do not necessarily result in readily interpretable features. In this paper, we present non-negative matrix factorization (NMF) as a dimensionality reduction algorithm that reduces the size of MSI datasets by three orders of magnitude with limited loss of information, yielding spatial and spectral components with meaningful correlation to tissue structure. This analysis is demonstrated on an MSI dataset from female Sprague-Dawley rats for an animal model of comorbid visceral pain hypersensitivity (CPH). The significant findings are: 1) High-dimensional MSI data (∼100,000 ions per pixel) was reduced to 20 spectral NMF components with<20% loss in reconstruction accuracy. 2) Spatial NMF components are reproducible and correlate well with H&E-stained tissue images. 3) Spatial NMF components may be used to provide images with enhanced specificity for different tissue types. 4) Small patches of NMF data (i.e., 20 spatial NMF components over 20 x 20 pixels) provide an accuracy of∼87% in classifying CPH vs näıve control subjects. This paper presents novel methodologies for data augmentation to support classification, ranking of features according to their contribution to classification, and image registration to support tissue-specific imaging.
How do host population dynamics impact Lyme disease risk dynamics in theoretical models?
Authors: Savage Joseph D.T. , Moore Christopher M. 
Published: April 2023

AbstractLyme disease is the most common wildlife-to-human transmitted disease reported in North America. The study of this disease requires an understanding of the ecology of the complex communities of ticks and host species involved in harboring and transmitting this disease. Much of the ecology of this system is well understood, such as the life cycle of ticks, and how hosts are able to support tick populations and serve as disease reservoirs, but there is much to be explored about how the population dynamics of different host species and communities impact disease risk to humans. In this study we construct a stage-structured, empirically-informed model with host dynamics to investigate how host population dynamics can affect disease risk to humans. The model describes a tick population and a simplified community of three host species, where primary nymph host populations are made to fluctuate on an annual basis, as commonly observed in host populations. We tested the model under different environmental conditions to examine the effect of environment on the interactions of host dynamics and disease risk. Results show that allowing for host dynamics in the model reduces mean nymphal infection prevalence and increases the maximum annual prevalence of nymphal infection and the density of infected nymphs. Effects of host dynamics on disease measures of nymphal infection prevalence were nonlinear and patterns in the effect of dynamics on amplitude in nymphal infection prevalence varied across environmental conditions. These results highlight the importance of further study of the effect of community dynamics on disease risk. This will involve the construction of further theoretical models and collection of robust field data to inform these models With a more complete understanding of disease dynamics we can begin to better determine how to predict and manage disease risk using these models.
CcBHLA: pan-specific peptide–HLA class I binding prediction via Convolutional and BiLSTM features
Authors: Wu Yejian , Cao Lujing , Wu Zhipeng , Wu Xinyi , Wang Xinqiao , Duan Hongliang 
Published: April 2023

AbstractHuman major histocompatibility complex (MHC) proteins are encoded by the human leukocyte antigen (HLA) gene complex. When exogenous peptide fragments form peptide-HLA (pHLA) complexes with HLA molecules on the outer surface of cells, they can be recognized by T cells and trigger an immune response. Therefore, determining whether an HLA molecule can bind to a given peptide can improve the efficiency of vaccine design and facilitate the development of immunotherapy. This paper regards peptide fragments as natural language, we combine textCNN and BiLSTM to build a deep neural network model to encode the sequence features of HLA and peptides. Results on independent and external test datasets demonstrate that our CcBHLA model outperforms the state-of-the-art known methods in detecting HLA class I binding peptides. And the method is not limited by the HLA class I allele and the length of the peptide fragment. Users can download the model for binding peptide screening or retrain the model with private data on github (https://github.com/hongliangduan/CcBHLA-pan-specific-peptide-HLA-class-I-binding-prediction-via-Convolutional-and-BiLSTM-features.git).
Perfusion estimation using MRI-based measurements and a porous media flow model
Authors: Lorentzen Rolf J. , Nævdal Geir , Sævareid Ove , Hodneland Erlend , Hanson Erik , Munthe-Kaas Antonella Z. 
Published: April 2023

AbstractThe measurement of perfusion and filtration of blood in biological tissue give rise to important clinical parameters used in diagnosis, follow-up, and therapy. In this paper we address techniques for perfusion analysis using processed contrast agent concentration data from dynamic MRI acquisitions. New methodology for analysis is evaluated and verified using synthetic data generated on a tissue geometry.Author summaryAccurate knowledge of tissue perfusion is crucial for proper diagnostics and treatment of several medical disorders. Traditional methods based on medical imaging are fast, but usually lack precision and robustness. In this paper, we address methodology to develop better diagnosis and treatment strategies for malignant tumors and stroke where blood perfusion may be altered. In our work, mathematical models for estimating perfusion are calibrated using magnetic resonance imaging (MRI) data, and more accurate representations of tissue parameters are provided. This methodology is a step towards minimal invasive and individually tailored diagnosis and treatment. We demonstrate the methodology with a twin-experiment using models of different complexity for generating data and estimating the tissue parameters. Both models are based on a mathematical description of how fluids flows in a porous medium, where the data-generating model uses higher resolution and a network representation of blood vessels than the estimating model. The calibration of unknown tissue parameters is done using a statistical framework, and the choice of methodology is motivated by applications from sub-surface reservoir characterization.
Interdependence of primary and secondary somatosensory cortices for plasticity and texture discrimination learning
Authors: Pandey Anurag , Kang Sungmin , Pacchiarini Nicole , Wyszynska Hanna , Grewal Aneesha , Griffiths Alex , Healy-Millett Imogen , Masseri Zena , Hardingham Neil , O’Neill Joseph , Honey Robert C. , Fox Kevin 
Published: April 2023

AbstractFeedforward and feedback pathways are important for transfer and integration of information between sensory cortical areas. Here we find that two closely connected cortical areas, the primary (S1) and secondary somatosensory cortices (S2) are both required for mice to learn a whisker-dependent texture discrimination. Increased inhibition in either area (using excitatory DREADDs expressed in inhibitory interneurones) prevents learning. We find that learning the discrimination produces structural plasticity of dendritic spines on layer 2/3 pyramidal neurones in vibrissae S1 that is restricted to the basal dendrites and leaves dendritic spines on apical dendrites unchanged. As S2 projects to the apical dendrites of S1 neurones, we tested whether S2 affects LTP-induction in S1. We found that feedback projections from S2 to S1 gates LTP on feedforward pathways within S1. These studies therefore demonstrate the interdependence of S1 and S2 for learning and plasticity in S1.HIGHLIGHTSBoth primary (S1) and secondary (S2) somatosensory cortices are necessary for whisker based texture discrimination learningS2 feedback connections to S1 gate LTP at feedforward pathways in S1S1 undergoes structural plasticity of pre-existing spines during learningS1 learning induced plasticity and LTP occurs on basal but not apical dendrites
Development and Mobile Deployment of a Stair Recognition System for Human-Robot Locomotion
Authors: Kurbis Andrew Garrett , Mihailidis Alex , Laschowski Brokoslaw 
Published: April 2023

AbstractEnvironment sensing and recognition can improve the safety and autonomy of human-robot locomotion, especially during transitions between environmental states such as walking to and from stairs. However, accurate and real-time perception on edge devices with limited computational resources is an open problem. Here we present the development and mobile deployment ofStairNet- a vision-based automated stair recognition system powered by deep learning. Building onExoNet- the largest open-source dataset of egocentric images of real-world walking environments - we designed a new dataset specifically for stair recognition with over 515,000 images. We then developed a lightweight and efficient convolutional neural network for image classification, which accurately predicted complex stair environments with 98.4% accuracy. We also studied different model compression and optimization methods and deployed our system on several mobile devices running a custom-designed iOS application with onboard accelerators using CPU, GPU, and/or NPU backend computing. Of the designs that we tested, our highest performing system showed negligible reductions in classification accuracy due to the model conversion for mobile deployment and achieved an inference time of 2.75 ms on an iPhone 11. The high speed and accuracy of theStairNetsystem on edge devices opens new opportunities for autonomous control and planning of robotic prosthetic legs, exoskeletons, and other assistive technologies for human locomotion.
Unlocking the Secrets of the Primate Visual Cortex: A CNN-Based Approach Traces the Origins of Major Organizational Principles to Retinal Sampling
Authors: da Costa Danny , Kornemann Lukas , Goebel Rainer , Senden Mario 
Published: April 2023

AbstractPrimate visual cortex exhibits key organizational principles: Cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells (RGCs), and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks (CNNs) outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.
Robust detection of clinically relevant features in single-cell RNA profiles of patient-matched fresh and formalin-fixed paraffin-embedded (FFPE) lung cancer tissue
Authors: Trinks Alexandra , Milek Miha , Beule Dieter , Kluge Julie , Florian Stefan , Sers Christine , Horst David , Morkel Markus , Bischoff Philip 
Published: April 2023

AbstractSingle-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. However, the established approach to perform such analyses on freshly collected tissue constitutes an important limitation since it requires prospective collection and immediate processing. Therefore, the ability to perform single-cell RNA sequencing from archived tissues would be very beneficial in a clinical setting. Here, we benchmark single-cell gene expression profiles from patient-matched fresh, cryopreserved and FFPE cancer tissue. We find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathways, Hallmark and clinically useful signatures, despite some variability in expression of individual genes due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting. Our analysis suggests that single-cell RNA sequencing from FFPE tissues is comparable to and can replace analyses from fresh tissue. This highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and dramatically increases the applicability in translational projects.
Leaf excision has minimal impact on photosynthetic parameters across crop functional types
Authors: Ferguson John N. , Jithesh Tamanna , Lawson Tracy , Kromdijk Johannes 
Published: April 2023

AbstractPhotosynthesis is increasingly becoming a recognised target for crop improvement. Phenotyping photosynthesis-related traits on field-grown material is a key bottleneck to progress here due to logistical barriers and short measurement days. Many studies attempt to overcome these challenges by phenotyping excised leaf material in the laboratory. To date there are no demonstrated examples of the representative nature of photosynthesis measurements performed on excised leaves relative to intact leaves in crops. Here, we tested whether standardised leaf excision on the day prior to phenotyping impacted a range of common photosynthesis-related traits across crop functional types using tomato (C3-dicot), barley (C3-monocot), and maize (C4-monocot). Potentially constraining aspects of leaf physiology that could be forecasted to impair photosynthesis in excised leaves, namely leaf water potential and abscisic acid accumulation, were not different between intact and excised leaves. We also observed non-significant differences in spectral reflectance and chlorophyll fluorescence traits between the treatments across the three species. However, we did observe some significant differences between gas exchange and photosynthetic capacity associated traits across all three species. This study represents a useful reference for those who perform measurements of this nature and the differences reported should be considered in associated experimental design and statistical analyses.HighlightAcross the main photosynthesis functional types (C3-dicot, C3-monot, and C4-monot) of major crops (tomato, barley, and maize), measurements of photosynthetic parameters demonstrate few, but important, differences when measured on excised relative to intact leaves.