Biology subjects

Baker, C. L.

Publications and source records attributed to Baker, C. L..

3 recordsLinked to original sources

bioRxiv2018

Modeling second-order boundary perception: A machine learning approach

BackgroundVisual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture.\n\nMethodology / Principal FindingsWe introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries.\n\nConclusions / SignificanceThis study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

neuroscience↗

bioRxiv2018

EWSR1 affects PRDM9-dependent histone 3 methylation and provides a link between recombination hotspots and the chromosome axis

Meiotic recombination in most mammals requires recombination hotspot activation through the action of the histone 3 lysine-4 and lysine-36 methyltransferase PRDM9 to ensure successful double-strand break initiation and repair. Here we show that EWSR1, a protein whose role in meiosis was not previously clarified in detail, binds to both PRDM9 and pREC8, a phosphorylated meiosis-specific cohesin, in male meiotic cells. We created a Ewsr1 conditional knockout mouse models to deplete EWSR1 before the onset of meiosis, and found that absence of EWSR1 causes meiotic arrest with decreased histone trimethylation at meiotic hotspots, impaired DNA double-strand break repair, and reduced crossover number. Our results demonstrate that EWSR1 is essential for promoting PRDM9-dependent histone methylation and normal meiotic progress, possibly by facilitating the linking between PRDM9-bound hotspots and the nascent chromosome axis. Author SummaryIn most mammals, including humans and mice, genetic recombination initiates when the meiosis-specific protein PRDM9 binds specific DNA sequences, known as hotspots, at the beginning of the extended prophase I of meiosis, and activates them by trimethylating histone 3 at lysine-4 and lysine-36 on nearby nucleosomes. Although this activation of hotspots is believed to occur on the chromatin loops, the subsequent double-strand break formation and repair occur on a proteinaceous structure known as the chromosome axis. We now show that Ewing sarcoma RNA binding protein 1 (EWSR1) is a key player in early recombination events, binding to PRDM9, promoting PRDM9-dependent histone methylation, and facilitating the linking between PRDM9-bound hotspots and the nascent chromosome axis through the meiosis-specific cohesion REC8. As a result of these activities, EWSR1 assures sufficient numbers of properly positioned crossovers in each meiosis.

developmental biology↗

bioRxiv2018

Tissue-specific trans regulation of the mouse epigenome

Although a variety of writers, readers, and erasers of epigenetic modifications are known, we have little information about the underlying regulatory systems controlling the establishment and maintenance of the epigenetic landscape, which varies greatly among cell types. Here, we have explored how natural genetic variation impacts the epigenome in mice. Studying levels of H3K4me3, a histone modification at sites such as promoters, enhancers, and recombination hotspots, we found tissue-specific trans-regulation of H3K4me3 levels in four highly diverse cell types: male germ cells, embryonic stem (ES) cells, hepatocytes and cardiomyocytes. To identify the genetic loci involved, we measured H3K4me3 levels in male germ cells in a mapping population of 60 BXD recombinant inbred lines, identifying extensive trans-regulation primarily controlled by six major histone quantitative trait loci (hQTL). These chromatin regulatory loci act dominantly to suppress H3K4me3, which at hotspots reduces the likelihood of subsequent DNA double-strand breaks. QTL locations do not correspond with enzyme known to metabolize chromatin features. Instead their locations match clusters of zinc finger genes, making these possible candidates that explain the dominant suppression of H3K4me3. Collectively, these data describe an extensive, tissue-specific set of chromatin regulatory loci that control functionally related chromatin sites.

genomics↗