Biology subjects

Fu, X.

Publications and source records attributed to Fu, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

A global resource constrained model to predict metabolic flux dynamics in fluctuating environments

Environmental changes often induce global variations on bacterial gene expression, frequently accompanied by alterations in growth rates. Integrating non-metabolic cellular processes, such as gene expression and macromolecule synthesis, with metabolic modeling remains a significant challenge in systems biology due to the lack of mechanistic representation of gene regulation. Here, we introduce a novel constraint-based modeling framework called dynamic Constrained Allocation Flux Balance Analysis (dCAFBA), which comprehensively integrates metabolism, cellular resource allocation, and gene regulation. We employ a quasi-steady-state assumption, positing that reaction fluxes achieve balance at each time step, adapting more rapidly than protein synthesis and growth dilution. This approach enables the prediction of reaction flux dynamics and protein allocation necessary to achieve cellular objectives, such as optimizing growth during various growth shifts (including carbon, amino acid, and transcriptional signal), without detailing molecular gene regulations. Our model offers a new method for interpreting proteome allocation and cellular metabolism in complex and transient environments, providing mechanistic insights valuable for metabolic engineering.

systems biology↗

bioRxiv2024

Single Distal Mutation Enhances Activity of known PETases via Stabilisation of PET-binding

As a major source of plastic pollution, PET has attracted significant interest for biodegradation due to its potential in the circular economy. Finding effective enzymes still remains a challenge as screening methods are limited by either the low throughput or dependence on alternative non-PET substrates due to PETs insolubility. Here, we report a highly active, stable and robust enzyme, Fast_2.9, identified while directly screening for PET-degrading activity in mesophilic conditions using droplet-based encapsulation of PET nanoparticles with the throughput above 1 kHz. We identified a distal S269T mutation that improves activity in the majority of all known PETases with up to 400 times over wildtype, and more than twice of known engineered PETases, as tested on untreated post-consumer plastics. Microsecond time scale molecular dynamics analyses indicate that this distant mutation possibly influences residues near the substrate-binding cleft via a common mechanism across PETases. Compared to the state-of-the-art FastPETase and LCCICCG enzymes, the engineered Fast_2.9 enzyme requires up to 8 and 42 times lower enzyme concentrations to reach the same enzymatic activity, ultimately requiring significantly less enzyme. As such our engineered enzyme degrades multiple post-consumer PET substrates, including polyester textiles, within as least as just 2 days with up to nearly 100% terephthalic acid conversion using as little as 0.72 mgenzyme/gPET at 50. Our study presents a universal methodology for direct screening of insoluble substrates at ultra-high-throughput and highlights the techno-economic potential of Fast_2.9 for PET depolymerisation.

synthetic biology↗

bioRxiv2024

Pix2Path: Integrating Spatial Transcriptomics and Digital Pathology with Deep Learning to Score Pathological Risk and Link Gene Expression to Disease Mechanisms

Spatial transcriptomics (ST) provides high-resolution mapping of gene expression within tissues, and integrating ST with digital pathology can offer unprecedented insights into the molecular mechanisms underlying various diseases. However, existing methods primarily focus on aligning these two distinct datasets, often neglecting the causal connections between spatial gene activity and pathological phenotype. We introduce Pix2Path, a deep learning-based approach utilizing conditional generative adversarial networks (cGANs), to bridge the gap between spatial transcriptomics and digital pathology. Pix2Path can process data from various spatial transcriptomics (ST) technologies, assess pathological risk scores across different conditions, and supports a leave-one-out spatial in silico gene perturbation strategy. As demonstrated in AD A{beta} plaques pathology, this approach allows to link gene expression changes to tissue morphology and pathology without relying on predefined conditions, providing a new perspective on understanding disease mechanisms.

pathology↗

bioRxiv2024

Dynamics and Evolutionary Conservation of B Complex Protein Recruitment During Spliceosome Activation

Spliceosome assembly and catalytic site formation (called activation) involve dozens of protein and snRNA binding and unbinding events. The B-complex specific proteins Prp38, Snu23, and Spp381 have critical roles in stabilizing the spliceosome during conformational changes essential for activation. While these proteins are conserved, different mechanisms have been proposed for their recruitment to spliceosomes. To visualize recruitment directly, we used Colocalization Single Molecule Spectroscopy (CoSMoS) to study the dynamics of Prp38, Snu23, and Spp381 during splicing in real time. These proteins bind to and release from spliceosomes simultaneously and are likely associated with one another. We designate the complex of Prp38, Snu23, and Spp381 as the B Complex Protein (BCP) subcomplex. Under splicing conditions, the BCP associates with pre-mRNA after tri-snRNP binding. BCP release predominantly occurs after U4 snRNP dissociation and after NineTeen Complex (NTC) association. Under low concentrations of ATP, the BCP pre-associates with the tri-snRNP resulting in their simultaneous binding to pre-mRNA. Together, our results reveal that the BCP recruitment pathway to the spliceosome is conserved between S. cerevisiae and humans. Binding of the BCP to the tri-snRNP when ATP is limiting may result in formation of unproductive complexes that could be used to regulate splicing. KEY POINTSO_LIPrp38, Snu23, and Spp381 associate together to form the B Complex Proteins (BCP) Complex C_LIO_LIDuring yeast spliceosome assembly, the BCP binds after the tri-snRNP and leaves after NTC arrival C_LIO_LIAt low ATP, the BCP pre-associates with the tri-snRNP in complexes that are likely unproductive C_LI

biochemistry↗

bioRxiv2024

Confidence-Enhanced Semi-supervised Learning for Mediastinal Neoplasm Segmentation

Automated segmentation of mediastinal neoplasms in preoperative computed tomography (CT) scans is critical for accurate diagnosis. Though convolutional neural networks (CNNs) have proven effective in medical imaging analysis, the segmentation of mediastinal neoplasms, which vary greatly in shape, size, and texture, presents a unique challenge due to the inherent local focus of convolution operations. To address this limitation, we propose a confidence-enhanced semi-supervised learning framework for mediastinal neoplasm segmentation. Specifically, we introduce a confidence-enhanced module that improves segmentation accuracy over indistinct tumor boundaries by assessing and excluding unreliable predictions simultaneously, which can greatly enhance the efficiency of exploiting unlabeled data. Additionally, we implement an iterative learning strategy designed to continuously refine the estimates of prediction reliability throughout the training process, ensuring more precise confidence assessments. Quantitative analysis on a real-world dataset demonstrates that our model significantly improves the performance by leveraging unlabeled data, surpassing existing semi-supervised segmentation benchmarks. Finally, to promote more efficient academic communication, the analysis code is publicly available at https://github.com/fxiaotong432/CEDS. Author summaryIn clinical practice, computed tomography (CT) scans can aid in the detection and evaluation of mediastinal tumors. The early detection of mediastinal tumors plays a crucial role in formulating appropriate treatment plans and improving patient survival rates. To reduce the high cost of manual annotation, researchers have attempted to employ convolutional neural networks (CNNs) for efficient automatic segmentation. However, the significant challenges arise due to the considerable variation in shape, size, and texture of mediastinal tumors, posing difficulties for the segmentation task. In this study, we introduce a confidence-enhanced module with a semi-supervised learning framework. By evaluating the models prediction confidence and selecting high-confidence predictions, we improve the efficiency and quality of data utilization. This approach demonstrates the achievement of accurate mediastinal tumor segmentation with only a minimal amount of labeled data. Our research not only provides an effective technical approach for automatic segmentation of mediastinal tumors but also opens up new possibilities for optimizing strategies in semi-supervised learning methods.

bioengineering↗

bioRxiv2024

Transposable Element Expression and Sub-cellular Dynamics During hPSC Differentiation to Endoderm, Mesoderm, and Ectoderm Lineages

Transposable elements (TEs) are genomic elements that are found in multiple copies in mammalian genomes. TEs were previously thought to have little functional relevance but recent studies have reported TE roles in multiple biological processes, particularly in embryonic development. To investigate the expression dynamics of TEs during human early development, we used long-read sequence data generated from in vitro differentiation of human pluripotent stem cells (hPSCs) to endoderm, mesoderm, and ectoderm lineages to construct lineage-specific transcriptome assemblies and accurately place TE sequences in their transcript context. Our analysis revealed that specific TE types, such as LINEs and LTRs, exhibit distinct expression patterns across different lineages. Notably, an expression outburst was observed in the ectoderm lineage, with multiple TE types showing dynamic expression trajectories. Additionally, certain LTRs, including HERVH and LTR7Y, were highly expressed in hPSCs and endodermal cells, but these HERVH and LTR7Y sequences originated from completely different transcripts. Interestingly, TE-containing transcripts exhibit distinct levels of transcript stability and subcellular localization across different lineages. Moreover, we showed a consistent trend of increased chromatin association of TE-containing transcripts in germ lineage cells compared to hPSCs. This study suggests that TEs contribute to human embryonic development through dynamic chromatin interactions. Key findingsO_LIDifferent loci of the same TEs are independently regulated in different cell states C_LIO_LIEctoderm has the highest frequency of TE-containing transcripts C_LIO_LIThe presence of TEs dynamically drives transcripts to different sub-cellular compartments in different cell states C_LIO_LIhPSCs have the least stable TE transcripts with the weakest TE chromatin association, highlighting loose hPSC chromatin and potential roles in cell differentiation C_LI

genomics↗

bioRxiv2024

Spatial gene expression at single-cell resolution from histology using deep learning with GHIST

The increased use of spatially resolved transcriptomics provides new biological insights into disease mechanisms. However, the high cost and complexity of these methods are barriers to broad clinical adoption. Consequently, methods have been created to predict spot-based gene expression from routinely-collected histology images. Recent benchmarking showed that current methodologies have limited accuracy and spatial resolution, constraining translational capacity. Here, we introduce GHIST, a deep learning-based framework that predicts spatial gene expression at single-cell resolution by leveraging subcellular spatial transcriptomics and synergistic relationships between multiple layers of biological information. We validated GHIST using public datasets and The Cancer Genome Atlas data, demonstrating its flexibility across different spatial resolutions and superior performance. Our results underscore the utility of in silico generation of single-cell spatial gene expression measurements and the capacity to enrich existing datasets with a spatially resolved omics modality, paving the way for scalable multi-omics analysis and new biomarker discoveries.

bioinformatics↗

bioRxiv2024

A Message Passing Framework for Precise Cell State Identification with scClassify2

In single-cell analysis, the ability to accurately annotate cells is crucial for downstream exploration. To date, a wide range of approaches have been developed for cell annotation, spanning from classic statistical models to the latest large language models. However, most of the current methods focus on annotating distinct cell types and overlook the identification of sequential cell populations such as transitioning cells. Here, we propose a message-passing-neural-network-based cell annotation method, scClassify2, to specifically focus on adjacent cell state identification. By incorporating prior biological knowledge through a novel dual-layer architecture and employing ordinal regression and conditional training to differentiate adjacent cell states, scClassify2 achieves superior performance compared to other state-of-the-art methods. In addition to single-cell RNA-sequencing data, scClassify2 is generalizable to annotation from different platforms including subcellular spatial transcriptomics data. To facilitate ease of use, we provide a web server hosting over 30 human tissues.

bioinformatics↗

bioRxiv2024

Colony pattern development of a synthetic bistable switch

Microbial colony development hinges upon a myriad of factors, including mechanical, biochemical, and environmental niches, which collectively shape spatial patterns governed by intricate gene regulatory networks. The inherent complexity of this phenomenon necessitates innovative approaches to comprehend and compare the mechanisms driving pattern formation. Here, we unveil the multistability of bacterial colony patterns orchestrated by a simple synthetic bistable switch. Utilizing quantitative imaging and spatially resolved transcriptome approaches, we explore the deterministic process of a ring-like colony pattern formation from a single cell. This process is primarily driven by bifurcation events programmed by the gene regulatory network and microenvironmental cues. Additionally, we observe a noise-induced process amplified by the founder effect, leading to patterns of symmetry-break during range expansion. The degrees of asymmetry are profoundly influenced by the initial conditions of single progenitor cells during the nascent stages of colony development. These findings underscore how the process of range expansion enables individual cells, exposed to a uniform growth-promoting environment, to exhibit inherent capabilities in generating emergent, self-organized behaviour.

systems biology↗

bioRxiv2024

Valine and Inflammation Drive Epilepsy in a Mouse Model of ECHS1 Deficiency

ECHS1 Deficiency (ECHS1D) is a rare and devastating pediatric disease that currently has no defined treatments. This disorder results from missense loss-of-function mutations in the ECHS1 gene that result in severe developmental delays, encephalopathy, hypotonia, and early death. ECHS1 enzymatic activity is necessary for the beta-oxidation of fatty acids and the oxidation of branched-chain amino acids within the inner mitochondrial matrix. The pathogenesis of disease remains unknown, however it is hypothesized that disease is driven by an accumulation of toxic metabolites from impaired valine oxidation. To expand our knowledge on disease mechanisms, a novel mouse model of ECHS1D was generated that possesses a disease-associated knock-in (KI) allele and a knock-out (KO) allele. To investigate the behavioral phenotype, a battery of testing was performed at multiple time points, which included assessments of learning, motor function, endurance, sensory responses, and anxiety. Neurological abnormalities were assessed using wireless telemetry EEG recordings, pentylenetetrazol (PTZ) seizure induction, and immunohistochemistry. Metabolic perturbations were measured within the liver, serum, and brain using mass spectrometry and magnetic resonance spectroscopy. To test disease mechanisms, mice were subjected to disease pathway stressors and then survival, body weight gain, and epilepsy were assessed. Mice containing KI/KI or KI/KO alleles were viable with normal development and survival, and the presence of KI and KO alleles resulted in a significant reduction in ECHS1 protein. ECHS1D mice displayed reduced exercise capacity and pain sensation. EEG analysis revealed increased slow wave power that was associated with perturbations in sleep. ECHS1D mice had significantly increased epileptiform EEG discharges, and were sensitive to seizure induction, which resulted in death of 60% of ECHS1D mice. Under basal conditions, brain structure was grossly normal, although histological analysis revealed increased microglial activation in aged ECHS1D mice. Increased dietary valine only affected ECHS1D mice, which significantly exacerbated seizure susceptibility and resulted in death. Lastly, acute inflammatory challenge drove regression and early lethality in ECHS1D mice. In conclusion, we developed a novel model of ECHS1D that may be used to further knowledge on disease mechanisms and to develop therapeutics. Our data suggests altered metabolic signaling and inflammation may contribute to epilepsy in ECHS1D, and these alterations may be attributed to impaired valine metabolism.

neuroscience↗

bioRxiv2024

Label-Free Electrochemical Imaging of Single Extracellular Vesicles

Extracellular vesicles (EVs) are pivotal in various biological processes and diseases, yet their small size and heterogeneity pose challenges for single EV quantification. We introduce interferometric electrochemical microscopy (iECM), a sensitive label-free technique that combines interferometric scattering and electrochemical impedance imaging. This method enables the quantification of the impedance of single EVs, providing unique insights into their electrochemical properties, and allows for the simultaneous measurement of size and real-time monitoring of antibody binding. Notably, we discovered that the impedance spectra of EVs can serve as unique fingerprints to differentiate EVs from cancerous and healthy cells, offering potential advancements in clinical diagnostics and the profiling of extracellular biomarkers.

biophysics↗

bioRxiv2024

Estropausal gut microbiota transplant improves ovarian function in adult mice

Decline in ovarian function with age not only affects fertility but is also linked to a higher risk of age-related diseases in women (e.g. osteoporosis, dementia). Intriguingly, earlier menopause is linked to shorter lifespan; however, the underlying molecular mechanisms of ovarian aging are not well understood. Recent evidence suggests the gut microbiota may influence ovarian health. In this study, we characterized ovarian aging associated microbial profiles in mice and investigated the effect of the gut microbiome from young and estropausal female mice on ovarian health through fecal microbiota transplantation. We demonstrate that the ovarian transcriptome can be broadly remodeled after heterochronic microbiota transplantation, with a reduction in inflammation-related gene expression and trends consistent with transcriptional rejuvenation. Consistently, these mice exhibited enhanced ovarian health and increased fertility. Using metagenomics-based causal mediation analyses and serum untargeted metabolomics, we identified candidate microbial species and metabolites that may contribute to the observed effects of fecal microbiota transplantation. Our findings reveal a direct link between the gut microbiota and ovarian health.

physiology↗

bioRxiv2024

Human myogenic progenitor cells display tenogenic differentiation potential and facilitate tendon regeneration

Tendon injury occurs at high frequency and is difficult to repair. Identification of human stem cells being able to regenerate tendon will greatly facilitate the development of regenerative medicine for tendon injury. We identified human CD29+/CD56+ myogenic progenitors having tendon differentiation potential both in vitro and in vivo. Transplantation of human CD29+/CD56+ myogenic progenitors contributes to injured tendon repair and thus improves locomotor function. Interestingly, the tendon differentiation potential in mouse muscle stem cells is minimal and the higher TGF{beta} signaling level may be the key for the distinct feature of human CD29+/CD56+ myogenic progenitors. These findings reveal that human CD29+/CD56+ myogenic progenitors are bi-potential adult stem cells and can serve as a new source for tendon regeneration.

cell biology↗

bioRxiv2024

Development and Application of Decontamination Methods for the Re-Use of Laboratory Grade Plastic Pipette Tips

During the SARS-CoV-2 pandemic, a need for methods to decontaminate and reuse personal protective equipment (PPE) and medical plastics became a priority. In this investigation we aimed to develop a contamination evaluation protocol for laboratory pipette tips, after decontamination. Decontamination methods tested in this study included cleaning with a common laboratory detergent (2.5% Alconox(R) solution followed with steam decontamination), exposure of ozone vapor at 250 and 14400 PPM * minute, and exposure to cold atmospheric plasma (CAP). All tips (control and experimental groups) were introduced to the methods described, while tips exposed to DNA extracts of Aeromonas hydrophila (ATCC-23211) were assessed for experimental groups. Decontamination was determined by turnover ratio and log reduction in detectable genomic material on the contaminated products using real-time quantitative PCR (qPCR) assay. Our results showed, cleaning tips with lab detergents along with steam decontamination removed genetic material, resulting in the highest log reduction, compared with ozone or CAP treatments. Detergent/washing methods showed the highest turnover ratio (95.9 %) and log reduction (5.943). However, the excessive residue (post-cleaning) on the plastic, within inner filters, and tip boxes suggested that washing with lab detergents was not favorable for reuse. Ozone vapor at 14400 PPM * minutes showed the second highest turnover ratio (98.4 %) and log reduction (4.511). CAP exposure with tips inverted (the tip end exposed closer to the plasma flame) for 1 minute showed a turnover ratio of (68.3 %) and log reduction (4.002). Relatively, lower turnover ratio and log reduction of CAP could be attributed to development/optimization of treatment conditions, including increases in exposure time and relative to tip positioning.

molecular biology↗

bioRxiv2024

Marker selection strategies for circulating tumor DNA guided by phylogenetic inference

MotivationBlood-based profiling of tumor DNA ("liquid biopsy") has offered great prospects for non-invasive early cancer diagnosis, treatment monitoring, and clinical guidance, but require further advances in computational methods to become a robust quantitative assay of tumor clonal evolution. We propose new methods to better characterize tumor clonal dynamics from circulating tumor DNA (ctDNA), through application to two specific questions: 1) How to apply longitudinal ctDNA data to refine phylogeny models of clonal evolution, and 2) how to quantify changes in clonal frequencies that may be indicative of treatment response or tumor progression. We pose these questions through a probabilistic framework for optimally identifying maximum likelihood markers and applying them to characterizing clonal evolution. ResultsWe first estimate a distribution over plausible clonal lineage models, using bootstrap samples over pre-treatment tissue-based sequence data. We then refine these lineage models and the clonal frequencies they imply over successive longitudinal samples. We use the resulting framework for modeling and refining tree distributions to pose a set of optimization problems to select ctDNA markers to maximize measures of utility capturing ability to solve the two questions of reducing uncertain in phylogeny models or quantifying clonal frequencies given the models. We tested our methods on synthetic data and showed them to be effective at refining distributions of tree models and clonal frequencies so as to minimize measures of tree distance relative to the ground truth. Application of the tree refinement methods to real tumor data further demonstrated their effectiveness in refining a clonal lineage model and assessing its clonal frequencies. The work shows the power of computational methods to improve marker selection, clonal lineage reconstruction, and clonal dynamics profiling for more precise and quantitative assays of tumor progression. Availabilityhttps://github.com/CMUSchwartzLab/Mase-phi.git. Contactrussells@andrew.cmu.edu

bioinformatics↗

bioRxiv2024

BRD8 guards the pluripotent state by sensing and maintaining histone acetylation

Epigenetic control of cell fates is a critical determinant to maintain cell type stability and permissive differentiation. However, the epigenetic control mechanisms are not well understood. Here, we show that the histone acetyltransferase reader protein BRD8 impairs the conversion of primed mouse EpiSCs (epiblast stem cells) to naive mouse ESCs (embryonic stem cells). BRD8 works by maintaining histone acetylation on promoters and transcribed gene bodies. BRD8 is responsible for maintaining open chromatin at somatic genes, and histone acetylation at naive-specific genes. When Brd8 expression was reduced, chromatin accessibility was unchanged, but histone acetylation at primed-specific genes was reduced. Conversely, naive-specific genes had reduced repressive chromatin marks, and acquired accessible chromatin more rapidly during the cell type conversion. We show that this process requires active histone deacetylation to promote the conversion of primed to naive. Our data supports a model for BRD8 reading histone acetylation to accurately localize the genome-wide binding of the histone acetyltransferase KAT5. Overall, this study shows how the reading of the histone acetylation state by BRD8 maintains cell type stability. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/585857v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@199139forg.highwire.dtl.DTLVardef@d735c6org.highwire.dtl.DTLVardef@10b6e15org.highwire.dtl.DTLVardef@16863cf_HPS_FORMAT_FIGEXP M_FIG C_FIG Key findingsO_LIBRD8 blocks the primed-to-naive transition. C_LIO_LIReduced Brd8 promoted the suppression of somatic and primed genes through reduced chromatin repression. C_LIO_LIReduced Brd8 enabled the accelerated activation of naive-specific genes by opening chromatin. C_LIO_LIBRD8 anchors the histone acetyltransferase KAT5 to acetylated genomic loci. C_LI

cell biology↗

bioRxiv2024

Navigated range expansion promotes migratory culling

Motile organisms can expand into new territories and increase their fitness, while nonmotile viruses usually depend on host migration to spread across long distances. In general, faster host motility facilitates virus transmission. However, recent ecological studies have also shown that animal host migration can reduce viral prevalence by removing infected individuals from the migratory group. Here, we use a bacteria-bacteriophage co-propagation system to investigate how host motility affects viral spread during range expansion. We find that phage spread during chemotaxis-driven navigated range expansion decreases as bacterial migration speed increases. Theoretical and experimental analyses show that the navigated migration leads to a spatial sorting of infected and uninfected hosts in the co-propagating front of bacteria-bacteriophage, with implications for the number of cells left behind. The preferential loss of infected cells in the co-propagating front inhibits viral spread. Further increase in host migration speed leads to a phase transition that eliminates the phage completely. These results illustrate that navigated range expansion of the host can promote the migratory culling of infectious diseases in the migration group. Significance StatementHost migration is commonly believed to accelerate the spread of infectious diseases. However, recent ecological studies suggest that migration may impede this spread. In our study, we developed a synthetic host-virus co-propagation model to explore the impact of host range expansion on the interplay between host mobility and virus spatial distribution. Our experimental and theoretical analysis revealed the spatial sorting of uninfected and infected hosts in the navigated propagating front leads to faster back diffusion of infected hosts. This self-organized structure allowed the migrating host population to eradicate the infectious disease, independent of intricate host-virus dynamics.

systems biology↗

bioRxiv2024

Communication with Surprise - Computational and Neural Mechanisms for Non-Verbal Human Interactions

Communication, often grounded in shared expectations, faces challenges without common linguistic backgrounds. Our study explores how people instinctively turn to the fundamental principles of the physical world to overcome communication barriers. Specifically, through the Tacit Communication Game, we investigate how participants develop novel strategies for conveying messages without relying on common linguistic signals. We developed a new computational model built from the principle of expectancy violations of a set of common universal priors derived from movement kinetics. The model closely resembles the Senders messages, with its core variable - the information-theoretic surprise - explaining the Receivers physiological and neural responses. This is evidenced by a significant correlation with the pupil diameter, indicating cognitive effort, and neural activity in brain areas related to expectancy violations. This work highlights the adaptability of human communication, showing how surprise can be a powerful tool in forming new communicative strategies without relying on common language.

neuroscience↗