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Au, D.

Publications and source records attributed to Au, D..

2 recordsLinked to original sources

Parallel construction of object motion from retina to cortex

Detecting a moving object against a moving background is fundamental to survival. The retina is known to perform this computation, extracting object motion from global motion before signals reach the brain. Under the prevailing hierarchical view of vision, downstream areas should inherit and elaborate this retinal computation rather than rebuild it. Using multielectrode recordings of matched stimuli in mouse retina and cortex along with computational modeling, we show instead that post-retinal processing recomputes rather than inherits object motion sensitivity. Retinal and cortical object-motion sensitivities have distinct tuning: the retina prefers fine jitter, whereas cortex prefers coarse drift, revealing a division of labor in which the retina detects moving objects, whereas the cortex conveys information about object pattern. Vision synthesizes object motion through parallel specialization, not hierarchical refinement.

neuroscience↗

Performance comparison: exome-sequencing as a single test replacing Sanger-sequencing

Systematic performance comparing the results of exome-sequencing as a single test replacing Sanger-sequencing of targeted gene(s) is still lacking. In this study we compared Sanger-sequencing results of 258 genes to those obtained from next generation sequencing (NGS) using two exome-sequencing enrichment kits: Agilent-SureSelectQXT and Illumina-Nextera. Sequencing was performed on leukocytes and buccal-derived DNA from a single individual, and all 258 genes were sequenced a total of 11 times (using different sequencing methods and DNA sources). Sanger-sequencing was completed for all exons, including flanking {+/-}8bp regions. For the 258 genes, NGS mean coverage was >20x for >98% and >91% of the regions targeted by SureSelect and Nextera, respectively. Overall, 449 variants were identified in at least one experiment, and 407/449 (90.6%) were detected by all. Of the 42 discordant variants, 23 were determined as true calls, summing-up to a truth set of 430 variants. Sensitivity of true-variant detection was 99% for Sanger-sequencing and 97%-100% for the NGS experiments. Mean false-positive rates were 3.7E-6 for Sanger-sequencing, 2.5E-6 for SureSelect-NGS and 5.2E-6 for Nextera-NGS. Our findings suggest a high overall concordance between Sanger-sequencing and NGS. Both methods demonstrated false positive and false negative calls and similar performances. Consequently, high clinical suspicion for a specific diagnosis should override negative results of either Sanger-sequencing or NGS.

genetics↗