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Diffraction minima resolve point scatterers at few hundredths of the wavelength
Resolving two or more constantly scattering identical point sources using freely propagating waves is limited by diffraction. Here we show that, by illuminating with a diffraction minimum, a given number of point scatterers can be resolved at distances of small fractions of the wavelength. Specifically, we identify an 8 nm distance, which corresponds to 1/80 of the employed 640 nm wavelength, between two constantly emitting fluorescent molecules in the focal plane of an optical microscope. We also measure 22 nm side length for a quadratic array of four molecules. Moreover, we show that the measurement precision improves with decreasing distance and with increased scatterer density. This work opens up the prospect of resolving individual scatterers in clusters that are far smaller than the wavelength.
Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data
Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.
Self-reports map the landscape of task states derived from brain imaging
Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.
The genetic origins of species boundaries at subtropical and temperate ecoregions in the North American racers (Coluber constrictor)
Phylogeographically structured lineages are a common outcome of range-wide population genetic studies. In the southeastern United States, disconnection between populations found at the intersection of the southeastern coastal plains of peninsular Florida and the southeastern plains of the adjacent continent is readily apparent among many plants and animals. However, the timing and maintenance of species boundaries between these distinctly different subtropical and temperate regions remains unknown for all organisms studied there. Using genome-scale data, we examine the timing of origins, gene flow, and the movement of genes under selection in unique ecoregions within the North American racers (Coluber constrictor). Isolation-migration models along with tests of genome-wide selection, locus-environment associations, and spatial and genomic clines demonstrate that two unrecognized species are present and are in contact at the boundary of these two ecoregions. We show that selection at several loci associated with unique environments have maintained species boundaries despite constant levels of gene flow between these lineages over thousands of generations. This research provides a new avenue of research to examine speciation processes in poorly studied biodiversity hotspots.
Ocean-bottom seismometers reveal surge dynamics in Earth’s longest-runout sediment flows
Turbidity currents carve Earth’s deepest canyons, form Earth’s largest sediment deposits, and break seabed telecommunications cables. Directly measuring turbidity currents is notoriously challenging due to their destructive impact on instruments within their path. This is especially the case for canyon-flushing flows that can travel >1000 km at >5 m/s, whose dynamics are poorly understood. We deployed ocean-bottom seismometers safely outside turbidity currents, and used emitted seismic signals to remotely monitor canyon-flushing events. By analyzing seismic power variations with distance and signal polarization, we distinguish signals generated by turbulence and sediment transport and document the evolving internal speed and structure of flows. Flow-fronts have dense near-bed layers comprising multiple surges with 5-to-30-minute durations, continuing for many hours. Fastest surges occur 30–60 minutes behind the flow-front, providing momentum that sustains flow-fronts for >1000 km. Our results highlight surging within dense near-bed layers as a key driver of turbidity currents’ long-distance runout.
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