Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,235 bioRxiv papers from 306,680 authors.
Most tweeted bioRxiv papers, last 24 hours
280 results found. For more information, click each entry to expand.
6 tweets animal behavior and cognition
Bees efficiently learn asocial and social cues to optimize foraging from fluctuating floral resources. However, it remains unclear how bees respond to divergent sources of social information, and whether such social cues might modify bees’ natural preferences for asocial cues (e.g. flower colour), hence affecting foraging decisions. Here, we investigated honeybees’ ( Apis mellifera ) inspection and choices of unfamiliar flowers based on both natural colour preferences and simultaneous foraging information from conspecifics and heterospecifics. Individual honeybees’ preferences for flowers were recorded when the reward levels of a learned flower type have declined and novel-coloured flowers were available where they would find either no social information or one conspecific and one heterospecific ( Bombus terrestris ), each foraging from a different coloured flower (either magenta or yellow). Honeybees were found to have a natural preference for magenta flowers. Social information affected honeybees’ inspection time of both types of flowers, i.e., honeybees modified their approaching flights to yellow and magenta flowers in response to conspecific and heterospecific social information. The presence of either demonstrator on the less-preferred yellow flower increased honeybees’ inspection time of yellow flowers. Conspecific social information influenced observers’ foraging choices of yellow flowers, thus outweighing their original preference for magenta flowers, such an influence was not elicited by heterospecific social information. Our results indicate that flower colour preferences of honeybees are rapidly adjusted in response to conspecific social information, which in turn is preferred over heterospecific information, thus favouring the transmission of adaptive foraging information within species.
6 tweets ecology
Cynipid gall wasps play an important role in structuring oak invertebrate communities. Wasps in the Cynipini tribe typically lay their eggs in oaks (Quercus L.), and induce the formation of a gall, which is a tumor-like growth of plant material that surrounds the developing wasp. As the wasp develops, the cynipid and its gall are attacked by a diverse community of natural enemies, including parasitoids, hyperparasitoids, and inquilines. Determining what structures these species-rich natural enemy communities across cynipid gall wasp species is a major question in gall wasp biology. Additionally, gall wasps are ecosystem engineers, as the abandoned gall is used by other invertebrates. The gall-associated insect communities residing on live oaks (Quercus geminata Small and Q. virginiana Mill.) are emerging as a model system for answering ecological and evolutionary questions ranging from community ecology to the evolution of new species. Documenting the invertebrates associated with cynipids in this system will expand our understanding of the mechanisms influencing eco-evolutionary processes, record underexplored axes of biodiversity, and facilitate future work. Here, we present the community of natural enemies and other associates of the asexual generation of the crypt gall wasp, Bassettia pallida Ashmead. We compare the composition of this community to communities recently documented from two other cynipid gall wasps specializing on live oaks along the U.S. Gulf coast, Disholcaspis quercusvirens Ashmead and Belonocnema treatae Mayr. B. pallida and their crypts support a diverse arthropod community, including over 25 parasitoids, inquilines, and other associated invertebrates spanning 5 orders and 16 families.
6 tweets biophysics
Single-particle cryo-EM continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices heralds a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep learning and image analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.
5 tweets evolutionary biology
Aim: To evaluate the potential role of the orogeny of the Eastern Cordillera (EC) of the Colombian Andes and the Merida Andes (MA) of Venezuela as drivers of vicariance between populations of 37 tetrapod lineages codistributed on both flanks, through geological reconstruction and comparative phylogeographic analyses. Location: Northwestern South America. Methods: We first reviewed and synthesized published geological data on the timing of uplift for the EC MA. We then combined newly generated mitochondrial DNA sequence data with published datasets to create a comparative phylogeographic dataset for 37 independent tetrapod lineages. We reconstructed time calibrated molecular phylogenies for each lineage under Bayesian inference to estimate divergence times between lineages located East and West of the Andes. We performed a comparative phylogeographic analysis of all lineages within each class of tetrapod using hierarchical approximate Bayesian computation (hABC) to test for synchronous vicariance across the EC MA. To evaluate the potential role of life history in explaining variation in divergence times among lineages, we evaluated 13 general linear models (GLM) containing up to six variables each (maximum elevation, range size, body length, thermoregulation, type of dispersal, and taxonomic class). Results: Our synthesis of geological evidence suggested that the EC MA reached significant heights by 38 to 33 million years ago (Ma) along most of its length, and we reject the oft cited date of 2 to 5 Ma. Based on mtDNA divergence from 37 lineages, however, the median estimated divergence time across the EC MA was 3.26 Ma (SE = 2.84) in amphibians, 2.58 Ma (SE = 1.81) in birds, 2.99 Ma (SE = 4.68) in reptiles and 1.43 Ma (SE = 1.23) in mammals. Using Bayes Factors, the hypothesis for a single temporal divergence interval containing synchronous divergence events was supported for mammals and but not supported for amphibians, non avian reptiles, or birds. Among the six life history variables tested, only thermoregulation successfully explained variation in divergence times (minimum AICc, R2 = 0.10), with homeotherms showing more recent divergence relative to poikilotherms. Main conclusions: Our results reject the hypothesis of the rise Andean Cordillera as driver of vicariance of lowland population because divergence dates are too recent and too asynchronous. We discuss alternative explanations, including dispersal through mountain passes, and suggest that changes in the climatic conditions during the Pliocene and Pleistocene interacted with tetrapod physiology, promoting older divergences in amphibians and reptiles relative to mammals and birds on an already established orogeny.
5 tweets cell biology
Maeva Dupont, Shanti Souriant, Luciana Balboa, Thien-Phong Vu Manh, Karine Pingris, Stella Rousset, Céline Cougoule, Yoann Rombouts, Renaud Poincloux, Myriam Ben Neji, Carolina Allers, Deepak Kaushal, Marcelo J. Kuroda, Susana Benet, Javier Martinez-Picado, Nuria Izquierdo-Useros, Maria del Carmen Sasiain, Isabelle Maridonneau-Parini, Olivier Neyrolles, Christel Verollet, Geanncarlo Lugo-Villarino
While tuberculosis (TB) is a risk factor in HIV-1-infected individuals, the mechanisms by which Mycobacterium tuberculosis worsens HIV-1 pathogenesis remain poorly understood. Recently, we showed that HIV-1 infection is exacerbated in macrophages exposed to TB-associated microenvironments due to tunneling nanotube (TNT) formation. To identify factors associated with TNT, we performed a transcriptome analysis in these macrophages, and revealed the up-regulation of Siglec-1. We demonstrate Siglec-1 expression depends on TB-mediated production of type-I interferon. In co-infected non-human primates, Siglec-1 is highly expressed by alveolar macrophages, whose abundance correlates with pathology and activation of the type-I interferon/STAT1 pathway. Intriguingly, Siglec-1 localizes exclusively on microtubule-containing TNT that are long and carry HIV-1. Siglec-1 depletion in macrophages decreases TNT length, HIV-1 capture and cell-to-cell transfer, and abrogates TB-driven exacerbation of HIV-1 infection. Altogether, we uncover a deleterious role for Siglec-1 in TB-HIV-1 co-infection, and its localization on TNT opens avenues to understand viral spread.
5 tweets neuroscience
To identify the neural correlates of perceptual awareness, researchers often compare the differences in neural activation between conditions in which an observer is or is not aware of a target stimulus. While intuitive, this approach often contains a critical limitation: In order to link brain activity with perceptual awareness, observers traditionally report the contents of their perceptual experience. However, relying on observers reports is problematic because it makes it difficult to know if the neural responses being measured are associated with conscious perception per se or with post-perceptual processes involved in the reporting task (i.e., working memory, decision-making, etc.). To address this issue, we combined a standard visual masking paradigm with a recently developed no-report paradigm. In the visual masking paradigm, observers saw images of animals and objects that were visible or invisible depending on their proximity to masks. Meanwhile, on half of the trials, observers reported the contents of their perceptual experience (i.e., report condition), while on the other half of trials they refrained from reporting about their experiences (i.e., no-report condition). We used electroencephalography (EEG) to examine how visibility interacts with reporting by measuring the P3b event related potential (ERP), one of the proposed canonical signatures of conscious processing. Overall, we found a robust P3b in the report condition, but no P3b whatsoever in the no-report condition. This finding suggests that the P3b itself is not a neural signature of conscious processing and highlights the importance of carefully distinguishing the neural correlates of perceptual awareness from post-perceptual processing.
5 tweets cell biology
Dynamic co-regulation of the actin and microtubule subsystems enables the highly precise and adaptive remodeling of the cytoskeleton necessary for critical cellular processes, like axonal pathfinding. The modes and mediators of this interpolymer crosstalk, however, are inadequately understood. We identify Fmn2, a non-diaphanous related formin associated with cognitive disabilities, as a novel regulator of cooperative actin-microtubule remodeling in growth cones. We show that Fmn2 stabilizes microtubules in the growth cones of cultured spinal neurons and also in vivo. Superresolution imaging revealed that Fmn2 facilitates guidance of exploratory microtubules along actin bundles into the chemosensory filopodia. Using live imaging, biochemistry and single-molecule assays we show that a C-terminal domain in Fmn2 is necessary for the dynamic association between microtubules and actin filaments. In the absence of the cross-bridging function of Fmn2, filopodial capture of microtubules is compromised resulting in de-stabilized filopodial protrusions and deficits in growth cone chemotaxis. Our results uncover a critical function for Fmn2 in actin-microtubule crosstalk in neurons and demonstrate that modulating microtubule dynamics via associations with F-actin is central to directional motility.
5 tweets evolutionary biology
Genomic imprinting is the expression bias of one allele in a diploid organism, expression being dependent upon which parent the allele was inherited from. Haig's kinship theory predicts that genomic imprinting occurs due to an evolutionary conflict-of-interest between the maternal alleles and paternal alleles of an individual. In social insects, it has been suggested that genomic imprinting should be widespread. One recent study identified parent-of-origin gene expression in honeybees and found evidence supporting one prediction of Haig's kinship theory. However, very little is known about genomic imprinting in insects and multiple theoretical predictions must be tested to avoid single-study confirmation bias. We therefore decided to test if parent-of-origin gene expression also occurs in another social bee using reciprocal crosses. We found equal numbers of maternally and paternally expressed alleles in both reproductive and sterile workers with the majority of genes showing the same expression bias in both castes. The most highly biased genes were maternally expressed. There was very little overlap of differentially expressed genes and genes showing parent-of-origin expression. We also found low evolutionary conservation of potentially imprinted genes with the honeybee. Our results offer some support for the kinship theory of genomic imprinting. However, this theory does not fully explain the patterns we have obtained showing the need to consider alternative models. Additionally, the patterns we see differ from previous work in honeybees. This highlights the importance of using multiple species to test theory. Finally, the lack of conservation between species suggests rapid evolution of imprinted genes in Hymenoptera.
5 tweets genomics
Single cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero-inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform current practice in a downstream clustering assessment using ground-truth datasets.
5 tweets neuroscience
Human auditory cortex contains neural populations that respond strongly to a wide variety of music sounds, but much less strongly to sounds with similar acoustic properties or to other real-world sounds. However, it is unknown whether this selectivity for music is driven by explicit training. To answer this question, we measured fMRI responses to 192 natural sounds in 10 people with extensive musical training and 10 with almost none. Using voxel decomposition (Norman-Haignere et al., 2015) to explain voxel responses across all 20 participants in terms of a small number of components, we replicated the existence of a music-selective response component similar in tuning and anatomical distribution to our earlier report. Critically, we also estimated components separately for musicians and non-musicians and found that a music-selective component was clearly present even in individuals with almost no musical training, which was very similar to the music component found in musicians. We also found that musical genres that were less familiar to our participants (e.g., Mongolian throat singing) produced strong responses within the music component, as did drum clips with rhythm but little melody. These data replicate the finding of music selectivity, broaden its scope to include unfamiliar musical genres and rhythms, and show that it is robustly present in people with almost no musical training. Our findings demonstrate that musical training is not necessary for music selectivity to emerge in non-primary auditory cortex, raising the possibility that music-selective brain responses could be a universal property of human auditory cortex.
5 tweets neuroscience
Parvalbumin-positive (PV+) interneurons are major regulators of cortical experience-dependent plasticity. Using an adaptive auditory discrimination task, we found that perceptual learning is associated with a transient downregulation of PV expression in primary auditory cortex (A1), as previously shown in motor and hippocampal cortex. Chronic chemogenetic manipulation of A1 PV+ interneurons during training changed the rate of acquisition of new skills; such that up-regulation of PV+ cell activity accelerated perceptual learning, but reducing their activity resulted in slower learning. However, both interventions resulted in impaired perceptual acuity by the end of training, relative to controls. These findings suggest that, whereas reduced PV+ cell function may facilitate training-induced plasticity early in training, a subsequent increase in PV+ cell activity might be needed to prevent further plastic changes and consolidate learning.
5 tweets cancer biology
Nevena B Ognjenovic, Meisam Bagheri, Gadisti Aisha Mohamed, Ke Xu, Meredith S. Brown, Youdinghuan Chen, Mohamed Ashick Mohamed Saleem, Shivashankar H Nagaraj, Kristen E Muller, Brock C. Christensen, Diwakar R Pattabiraman
Differentiation therapy is an approach that utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift towards terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have proven challenging. In this study we show that activation of PKA drives aberrant mammary differentiation by diminishing the self-renewing potential of the basal compartment. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. PKA activation represents a promising approach as an adjuvant to chemotherapy for certain breast cancers, reviving the paradigm of differentiation therapy for solid tumors.
4 tweets genomics
Celestia Fang, Zhenjia Wang, Cuijuan Han, Stephanie L Safgren, Kathryn A. Helmin, Emmalee R Adelman, Kyle P. Eagen, Alexandre Gaspar-Maia, Maria E Figueroa, Benjamin D. Singer, Aakrosh Ratan, Panagiotis Ntziachristos, Chongzhi Zang
Background: The three-dimensional genome organization is critical for gene regulation and can malfunction in diseases like cancer. As a key regulator of genome organization, CCCTC-binding factor (CTCF) has been characterized as a DNA-binding protein with important functions in maintaining the topological structure of chromatin and inducing DNA looping. Among the prolific binding sites in the genome, several events with altered CTCF occupancy have been reported as associated with effects in physiology or disease. However, there is no hitherto a comprehensive survey of genome-wide CTCF binding patterns across different human cancers. Results: To dissect functions of CTCF binding, we systematically analyze over 700 CTCF ChIP-seq profiles across human tissues and cancers and identify cancer-specific CTCF binding patterns in six cancer types. We show that cancer-specific lost and gained CTCF binding events are associated with altered chromatin interactions in patient samples, but not always with DNA methylation changes or sequence mutations. While lost bindings primarily occur near gene promoters, most gained CTCF binding events are induced by oncogenic transcription factors and exhibit enhancer activities. We validate these findings in T-cell acute lymphoblastic leukemia and show that oncogenic NOTCH1 induces specific CTCF binding and they cooperatively activate expression of target genes, indicating transcriptional condensation phenomena. Conclusions: Cancer-specific CTCF binding events are not always associated with DNA methylation changes or mutations, but can be induced by other transcription factors to regulate oncogenic gene expression. Our results substantiate CTCF binding alteration as a functional epigenomic signature of cancer.
4 tweets neuroscience
The human adult structural connectome has a rich topology composed of nodal hierarchies containing highly diverse connectivity patterns, aligned to the diverse range of functional specialisations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of neurocognitive impairment and atypical brain development, on hierarchical complexity. We report that the neonatal and adult structural connectomes are both composed of distinct hierarchical tiers. Consistency of ROIs is found at both ends of this hierarchy during early life and in adulthood, but significant differences are evident in intermediate tiers. The neonatal connectome is hierarchically complex in term born neonates, but hierarchically complexity is altered in association with preterm birth. This is mainly due to diversity of connectivity patterns in Tier 3, which is comprised of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (comprising hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid hierarchical structure in hub regions allowing for the development of greater and more diverse functional specialisation in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.
4 tweets neuroscience
The neuroactive metabolites of the steroid hormones progesterone (P4) and testosterone (T) are GABAergic modulators that influence cognitive control, yet the specific effect of P4 and T on brain network activity remains poorly understood. Here, we investigated if a fundamental oscillatory network activity pattern related to cognitive control, frontal midline theta (FMT) oscillations, are modulated by steroids hormones, P4 and T. We measured the concentration P4 and T using salivary enzyme immunoassay and FMT oscillations using high-density electroencephalography (EEG) during the eyes-open resting state in fifty-five healthy female and male participants. Electrical brain activity was analyzed using Morlet wavelet convolution, beamformer source localization, background noise spectral fitting, and phase amplitude coupling analysis. Steroid hormone concentrations and biological sex were used as predictors for scalp and source-estimated theta oscillations and for top-down theta-gamma phase amplitude coupling. Elevated concentrations of P4 predicted increased FMT oscillatory amplitude across both sexes, and no relationship was found with T. The positive correlation with P4 was specific to the frontal-midline electrodes and survived correction for the background noise of the brain. Using source localization, FMT oscillations were localized to the frontal-parietal network. Additionally, theta amplitude within the frontal-parietal network, but not the default mode network, positively correlated with P4 concentration. Finally, P4 concentration correlated with increased coupling between FMT phase and posterior gamma amplitude. Our results suggest that P4 concentration modulates brain activity via upregulation of theta oscillations in the frontal-parietal network and increased top-down control over posterior cortical sites.
4 tweets bioinformatics
Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously used integrative nonnegative matrix factorization (iNMF) to jointly learn interpretable low-dimensional representations from multiple single-cell datasets using dataset-specific and shared metagene factors. These factors provide a principled, quantitative definition of cellular identity and how it varies across biological contexts. However, datasets exceeding 1 million cells are now widely available, creating computational barriers to scientific discovery. For instance, it is no longer feasible to use the entire available datasets as inputs to implement standard pipelines on a personal computer with limited memory capacity. Moreover, there is a need for an algorithm capable of iteratively refining the definition of cellular identity as efforts to create a comprehensive human cell atlas continually sequence new cells. To address these challenges, we developed an online learning algorithm for integrating massive and continually arriving single-cell datasets. We extended previous online learning approaches for NMF to minimize the expected cost of a surrogate function for the iNMF objective. We also derived a novel hierarchical alternating least squares algorithm for iNMF and incorporated it into an efficient online algorithm. Our online approach accesses the training data as mini-batches, decoupling memory usage from dataset size and allowing on-the-fly incorporation of new data as it is generated. The online implementation of iNMF converges much more quickly using a fraction of the memory required for the batch implementation, without sacrificing solution quality. Our new approach enables factorization of 939489 single cells from 9 regions of the mouse brain on a standard laptop in ~30 minutes. Furthermore, we construct a multi-modal cell atlas of the mouse motor cortex by iteratively incorporating seven single-cell datasets from three different modalities generated by the BRAIN Initiative Cell Census Network over a period of two years. Our approach obviates the need to recompute results each time additional cells are sequenced, dramatically increases convergence speed, and allows processing of datasets too large to fit in memory. Most importantly, it facilitates continual refinement of cell identity as new single-cell datasets from different biological contexts and data modalities are generated.
4 tweets bioinformatics
Whole genome comparisons based on Average Nucleotide Identities (ANI), and the Genome-to-genome distance calculator have risen to prominence in rapidly classifying taxa using whole genome sequences. Some implementations have even been proposed as a new standard in species classification and have become a common technique for papers describing newly sequenced genomes. However, attempts to apply whole genome divergence data to delineation of higher taxonomic units, and to phylogenetic inference have had difficulty matching those produced by more complex phylogenetics methods. We present a novel method for generating reliable and statistically supported phylogenies using established ANI techniques. For the test cases to which we applied the developed approach we obtained accurate results up to at least the family level. The developed method uses non-parametric bootstrapping to gauge reliability of inferred groups. This method offers the opportunity make use of whole-genome comparison data that is already being generated to quickly produce accurate phylogenies. Additionally, the developed ANI methodology can assist classification of higher order taxonomic groups.
4 tweets neuroscience
Object recognition relies on different transformations of the retinal input, ranging from local contrast to object shape and category. While some of those representations are thought to occur at specific stages of the visual hierarchy, many of them are correlated (e.g., object shape and identity) and can be retrieved from the activity of several brain regions. This overlap may be explained either by collinearity across representations, or may instead reflect the coding of multiple dimensions by the same cortical population. Moreover, orthogonal and shared components may differently impact on distinctive stages of the visual hierarchy. We recorded functional MRI (fMRI) activity while participants passively attended to objects, and employed a statistical approach that partition orthogonal and shared object representations to reveal their relative impact on brain processing. Orthogonal shape representations (i.e., silhouette, curvature and medial-axis) independently explain distinct and overlapping clusters of selectivity in occitotemporal (OTC) and parietal cortex. Moreover, we showed that the relevance of shared representations linearly increases moving from posterior to anterior regions. These results indicate that the visual cortex encodes shared relations between different features in a topographic fashion and that object shape is encoded along different dimensions, each representing orthogonal features.
4 tweets neuroscience
Behavioral evolution relies on genetic changes, yet few social behaviors can be traced to specific genetic sequences in vertebrates. Here, we show experimental evidence that differentiation of a single gene has contributed to divergent behavioral phenotypes in the white-throated sparrow, a common North American songbird. In this species, one of two alleles of ESR1 , encoding estrogen receptor α (ERα), has been captured inside a differentiating supergene that segregates with an aggressive phenotype, such that ESR1 expression predicts aggression. Here, we show that the aggressive phenotype associated with the supergene is prevented by ESR1 knockdown in a single brain region. Next, we show that in a free-living population, aggression is predicted by allelic imbalance favoring the supergene allele. Cis -regulatory variation between the two alleles affects transcription factor binding sites, DNA methylation, and rates of transcription. This work provides a rare illustration of how genotypic divergence has led to behavioral phenotypic divergence in a vertebrate.
4 tweets neuroscience
Neurons and synapses in the cerebral cortex behave stochastically. The advantages of such stochastic properties have been proposed in several works, but the relationship and synergy between the stochasticities of neurons and synapses remain largely unexplored. Here, we show that these stochastic features can be inseparably integrated into a simple framework that provides a practical and biologically plausible learning algorithm that consistently accounts for various experimental results, including the most efficient power-law coding of the cortex. The derived algorithm overcomes many of the limitations of conventional learning algorithms of neural networks. As an experimentally testable prediction, we derived the slow retrograde modulation of the excitability of neurons from this algorithm. Because of the simplicity and flexibility of this algorithm, we anticipate that it will be useful in the development of neuromorphic devices and scalable AI chips, and that it will help bridge the gap between neuroscience and machine learning.
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