Biology subjects

Dunn, T.

Publications and source records attributed to Dunn, T..

11 recordsLinked to original sources

bioRxiv2026

Bumetanide Strengthens Residual Brain Bladder Communication After Spinal Cord Injury

Neurogenic bladder is one of the most disabling consequences of spinal cord injury (SCI), yet it remains unclear whether residual brain bladder communication persists after injury and can be therapeutically strengthened. Here, we integrated analysis of a clinical SCI cohort with studies in a mouse model that recapitulates key features of human neurogenic bladder. SCI markedly impaired both descending and ascending limbs of the spinobulbospinal micturition reflex, reducing bladder responses evoked by stimulation of the pontine micturition center (PMC) and bladder filling-induced activation of the periaqueductal gray. Despite this marked functional impairment, pseudorabies virus tracing, optogenetics, and machine learning assisted three dimensional imaging revealed persistent bladder connected neurons within the spared thoracic interlesion region. Neurons within this region remained responsive to descending PMC input, identifying the interlesion network as a candidate substrate for residual brain bladder communication. Early continuous intrathecal bumetanide improved urinary storage and emptying, enhanced descending PMC to bladder and ascending bladder to brain signaling, and increased the functional engagement of bladder-connected thoracic neurons without increasing urine production. Bumetanide also increased transsynaptic labeling between the bladder and PMC, consistent with enhanced polysynaptic brain bladder connectivity. Together, these findings indicate that brain bladder communication is not completely lost after SCI and may be supported by a functionally compromised residual spinal network that remains amenable to therapeutic reinforcement. Targeting spared autonomic circuitry may therefore provide a strategy for improving bladder function after SCI.

neuroscience↗

bioRxiv2025

Hidden in Plain Sight. How Ks histogram dynamics can reveal and obscure ancient whole genome duplications.

Polyploidy is a significant force in angiosperm evolution, of great interest to evolutionary biologists and crop scientists. The mode of origin of a polyploid lineage is a continuum, ranging from whole genome duplication (WGD) from within the same species (autopolyploidy), to a WGD derived from hybridization between two (or more) distinct species (allopolyploidy). The polyploid mode of origin impacts many aspects of genome evolution, including patterns of chromosomal inheritance, homeologous exchange rate, and post-WGD diploidization. Here, we develop a novel polyploid genome simulation engine DemographiKS to demonstrate that the Ks histogram, one of the most broadly utilized methods for inferring WGD events, is highly sensitive to evolutionary parameters relating to the mode of origin, as well as demographic parameters such as migration events, population bottlenecks and expansions. Our results show that that the location of the Ks peak may correspond to the time of parental divergence (for allopolyploids); might be hidden at Ks=0 (for autopolyploids under a population contraction); may correspond to the mean time of coalescence of the diploid ancestor (for autopolyploids under a population expansion), or the time of most recent migration between the parental species (for allopolyploids). We also fit simulated Ks histograms to polyploid genomes from four well-studied plant lineages on the polyploid continuum (Coffea arabica, Zea mays, Populus trichocarpa, and Saccharum spontaneum), demonstrating that WGD simulations may be used to corroborate inferred demographies. In summary, we demonstrate that Ks histograms are information-rich, computationally tractable, and can be utilized to corroborate detailed evolutionary histories inferred by other methods.

genomics↗

bioRxiv2025

ConvNeXt-Driven Detection of Alzheimers Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes

Alzheimers disease (AD) is a leading worldwide cause of cognitive impairment, necessitating accurate, inexpensive diagnostic tools to enable early recognition. In this study, we present a robust deep learning approach for AD classification based on structural MRI scans, ConvNeXt, an emergent convolutional architecture inspired by vision transformers. We further present AlzaSet, a novel, expertly labeled clinical dataset with axial, coronal, and sagittal perspectives from AD and cognitively normal control subjects. Three ConvNeXt sizes (Tiny, Small, Base) were compared and benchmarked against existing state-of-the-art CNN models (VGG16, VGG19, InceptionV3, DenseNet1 21). ConvNeXt-Base consistently outperformed the other models on coronal slices with accuracy of 98.37% and an AUC of 0.992. Coronal views were determined to be most diagnostically informative, with emphasis on visualization of the medial temporal lobe. Moreover, comparison with recent ensemble-based techniques showed that superior performance with comparable computational efficiency. These results indicate that ConvNeXt-capable models applied to clinically curated data sets have strong potential to provide scalable, real-time AD screening in diverse settings, including both high-resource and resource-constrained settings.

neuroscience↗

bioRxiv2024

A rapid, inexpensive, culture-free, universal bacterial identification system using internal transcribed spacer targeting primers: a proof-of-principle study

Techniques for bacterial detection and identification can be characterized as either untargeted and taxonomically broad or targeted and taxonomically narrow. Untargeted techniques (e.g., culture and sequencing) are time-consuming and/or expensive while targeted techniques (polymerase chain reaction; PCR) can be faster and less expensive but require a strong pre-test suspicion of the potential organism to choose the correct test. We have developed a universal bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The platform utilizes a unique universal polymerase chain reaction (PCR) primer set that targets the internal transcribed spacer (ITS) regions between conserved bacterial genes, creating a distinguishable electrophoretic pattern for each bacterial species. Bioinformatic simulation demonstrates that at least 45 commonly isolated pathogenic species can be uniquely identified from a single set of PCR primers using this approach. We experimentally confirmed these predictions on seven representative human pathogens, including gram-negatives and gram-positives, aerobes and anaerobes, and spore formers. Without a priori knowledge of the organism, this system can rapidly identify the unique pattern generated by multiple species in a single reaction. Using quantitative PCR, the system can also determine the corresponding concentration of the organism in question. We also show that the primers are resilient to human DNA contamination at physiologic concentrations, eliminating the need for complex and time intensive extraction methods. Proof-of-principle testing on actual clinical specimens demonstrate that this assay can identify more than twice the species as current multiplex PCR assays (e.g., BioFire) using only one universal primer pair in a single PCR reaction, providing results in <3 hours for <$20 without bioinformatic turnaround time. SIGNIFICANCE STATEMENTThis paper describes a novel bacterial identification system that is as taxonomically broad as culture and amplicon sequencing but as fast, easy, and affordable as PCR. The assay covers more than twice the species as the leading multiplex PCR assays but uses only one universal primer pair in a single PCR reaction. It is resilient to human genomic contamination precluding the need for timely or costly methods to clear human DNA. This approach represents a major advancement in the decades-long struggle to rapidly and accurately identify bacteria.

bioengineering↗

bioRxiv2024

Accurately Inferring Ancient Auto and Allopolyploidization Events using Forward-time Simulations

Multiple rounds of whole-genome duplication (WGD) followed by diploidization have occurred throughout the evolutionary history of angiosperms. To understand how these cycles occur, much work has been done to model the genomic consequences and evolutionary significance of WGD. The machinations of diploidization are strongly influenced by the mode of speciation (allo or autopolyploidy). However, there is no discrete boundary between allo and autopolyploidy, which is best described as a continuum. Here we present a forward-time polyploid genome evolution simulator called SpecKS. SpecKS models polyploid speciation as originating from a 2D continuum, whose dimensions account for both the level of genetic differentiation between the ancestral parental genomes, as well the time lag between ancestral speciation and their subsequent reunion in the derived polyploid. Using extensive simulations, we demonstrate that changes in initial conditions along either dimension of the 2D continuum deterministically affect the shape of the Ks histogram. Our findings indicate that the error in the common method of estimating WGD time from the Ks histogram peak scales with the degree of allopolyploidy, and we present an alternative, accurate estimation method that is independent of the degree of allopolyploidy. Lastly, we use SpecKS to derive tests that infer both the lag time between parental divergence and WGD time, and the diversity of the ancestral species, from an input Ks histogram. We apply the latter test to transcriptomic data from over 200 species across the plant kingdom, the results of which are concordant with the prevailing theory that the majority of angiosperm lineages are derived from diverse parental genomes and may be of allopolyploid origin.

evolutionary biology↗

bioRxiv2024

Jointly benchmarking small and structural variant calls with vcfdist

In this work, we extend vcfdist to be the first variant call benchmarking tool to jointly evaluate phased single-nucleotide polymorphisms (SNPs), small insertions/deletions (INDELs), and structural variants (SVs) for the whole genome. First, we find that a joint evaluation of small and structural variants uniformly reduces measured errors for SNPs (-28.9%), INDELs (-19.3%), and SVs (-52.4%) across three datasets. Next, we correct a common flaw in phasing evaluations, reducing measured flip errors by over 50%. Lastly, we show that vcfdist is more accurate than previously published works and on par with the newest approaches, but with improved result interpretability.

bioinformatics↗

bioRxiv2023

vcfdist: Accurately benchmarking phased small variant calls in human genomes

AbstractAccurately benchmarking small variant calling accuracy is critical for the continued improvement of human whole genome sequencing. In this work, we show that current variant calling evaluations are biased towards certain variant representations and may misrepresent the relative performance of different variant calling pipelines. We propose solutions, first exploring the affine gap parameter design space for complex variant representation and suggesting a standard. Next, we present our tool "vcfdist" and demonstrate the importance of enforcing local phasing for evaluation accuracy. We then introduce the notion of partial credit for mostly-correct calls and present an algorithm for clustering dependent variants. Lastly, we motivate using alignment distance metrics to supplement precision-recall curves for understanding variant calling performance. We evaluate the performance of 64 phased "Truth Challenge V2" submissions and show that vcfdist improves measured (SNP, INDEL) performance consistency across variant representations from R2 = (0.14542, 0.97243) for baseline vcfeval to (0.99999, 0.99996) for vcfdist.

bioinformatics↗

bioRxiv2022

Perfusable Apparatus For Thick-tissue Creation And Growth (patch) Of Cardiac Tissue

Cardiac tissue engineering has been developed as a potential alternative treatment for heart failure. However, current 3D tissues are limited in size and thickness due to the lack of an effective vascularization method. We have developed a novel bioreactor system to create viable vascularized cardiac tissue from multicellular spheroids using a digital light processing (DLP) 3D bioprinting system. Spheroids were created from induced pluripotent stem cells (iPSC) and cardiac fibroblasts (FB) using special dimple plates for mass production. One centimeter cubic tissues were created from spheroids using a DLP 3D printed mold with vascular channels. The tissue was maintained in a perfusion chamber under regulated flow and pressure following differentiation to cardiac tissue and endothelialization. Mass production of large spheroids (35,000 / tissue, diameter of 395.99 um +/- 101.15 um) was achieved from 170 million iPSCs and 50 million FBs for the creation of 1cm3 cardiac tissue in a 3D printed mold with vascular channels. The cardiac tissues (n=5) were perfused for 20 days under stable pressure of 17.5 +/- 3.05 PSI and flow of 5000 uL/min +/- 1116.42 uL/min. On days 10 and 20, Alamar blue assays showed viability for all five tissues (Alamar blue intensity: Day 10 1.57 +/- 0.15. Day 20 2.21 +/- 0.19). Thick and viable cardiac tissues were created and maintained using a 3D printed vascularized mold and perfusion system for maturation and growth in vitro for 30 days. This technology will open new doors for viable in vitro cardiac tissue creation.

bioengineering↗

bioRxiv2022

nPoRe: n-Polymer Realigner for improved pileup variant calling

Despite recent improvements in nanopore basecalling accuracy, germline variant calling of small insertions and deletions (INDELs) remains poor. Although precision and recall for single nucleotide polymorphisms (SNPs) now regularly exceeds 99.5%, INDEL recall at relatively high coverages (85x) remains below 80% for standard R9.4.1 flow cells [22, 23, 31]. Current nanopore variant callers work in two stages: an efficient pileup-based method identifies candidates of interest, and then a more expensive full-alignment model provides the final variant calls. Most false negative INDELs are lost during the first (pileup-based) step, particularly in low-complexity repeated regions. We show that read phasing and realignment can recover a significant portion of INDELs lost during this stage. In particular, we extend Needleman-Wunsch affine gap alignment by introducing new gap penalties for more accurately aligning repeated n-polymer sequences such as homopolymers (n = 1) and tandem repeats (2 [≤] n [≤] 6). On our dataset with 60.6x coverage, haplotype phasing improves INDEL recall in all evaluated high confidence regions from 63.76% to 70.66% and then nPoRe realignment improves it further to 73.04%, with no loss of precision.

bioinformatics↗

bioRxiv2018

Pisces: An Accurate and Versatile Variant Caller for Somatic and Germline Next-Generation Sequencing Data

MotivationNext-Generation Sequencing (NGS) technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants, suitable for the clinic.\n\nResultsWe have developed Pisces, a rapid, versatile and accurate small variant calling suite designed for somatic and germline amplicon sequencing applications. Pisces accuracy is achieved by four distinct modules, the Pisces Read Stitcher, Pisces Variant Caller, the Pisces Variant Quality Recalibrator, and the Pisces Variant Phaser. Each module incorporates a number of novel algorithmic strategies aimed at reducing noise or increasing the likelihood of detecting a true variant.\n\nAvailabilityPisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace SequenceHub as part of the TruSeq Amplicon workflow and the Illumina Ampliseq Workflow. Pisces is distributed on Illumina sequencing platforms such as the MiSeq, and is included in the Praxis Extended RAS Panel test which was recently approved by the FDA for the detection of multiple RAS gene mutations.\n\nContactpisces@illumina.com\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

Inferring Functional Neural Connectivity With Deep Residual Convolutional Networks

Measuring synaptic connectivity in large neuronal populations remains a major goal of modern neuroscience. While this connectivity is traditionally revealed by anatomical methods such as electron microscopy, an efficient alternative is to computationally infer functional connectivity from recordings of neural activity. However, these statistical techniques still require further refinement before they can be reliably applied to real data. Here, we report significant improvements to a deep learning method for functional connectomics, as assayed on synthetic ChaLearn Connectomics data. The method, which integrates recent advances in convolutional neural network architecture and model-free partial correlation coefficients, outperforms published methods on competition data and can achieve over 90% precision at 1% recall on validation datasets. This suggests that future application of the model to in vivo whole-brain imaging data in larval zebrafish could reliably recover on the order of 106 synaptic connections with a 10% false discovery rate. The model also generalizes to networks with different underlying connection probabilities and should scale well when parallelized across multiple GPUs. The method offers real potential as a statistical complement to existing experiments and circuit hypotheses in neuroscience.

neuroscience↗