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  1. Discher, Dennis (Ed.)
    Hydrodynamic flow produced by multiciliated cells is critical for fluid circulation and cell motility. Hundreds of cilia beat with metachronal synchrony for fluid flow. Cilia-driven fluid flow produces extracellular hydrodynamic forces that cause neighboring cilia to beat in a synchronized manner. However, hydrodynamic coupling between neighboring cilia is not the sole mechanism that drives cilia synchrony. Cilia are nucleated by basal bodies (BBs) that link to each other and to the cell’s cortex via BB-associated appendages. The intracellular BB and cortical network is hypothesized to synchronize ciliary beating by transmitting cilia coordination cues. The extent of intracellular ciliary connections and the nature of these stimuli remain unclear. Moreover, how BB connections influence the dynamics of individual cilia has not been established. We show by focused ion beam scanning electron microscopy imaging that cilia are coupled both longitudinally and laterally in the ciliate Tetrahymena thermophila by the underlying BB and cortical cytoskeletal network. To visualize the behavior of individual cilia in live, immobilized Tetrahymena cells, we developed Delivered Iron Particle Ubiety Live Light (DIPULL) microscopy. Quantitative and computer analyses of ciliary dynamics reveal that BB connections control ciliary waveform and coordinate ciliary beating. Loss of BB connections reduces cilia-dependent fluid flowmore »forces.« less
    Free, publicly-accessible full text available September 15, 2023
  2. Abstract Background Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding. Results Here we illustrate a new method, which we call HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies. We illustrate the performance of HapSolo on genome data from three species: the Chardonnay grape ( Vitis vinifera ), with a genome of 490 Mb, a mosquito ( Anopheles funestus ; 200 Mb) and the Thorny Skate ( Amblyraja radiata ; 2650 Mb). Conclusions HapSolo rapidly identified candidate assemblies that yield improvements in assembly metrics, including decreased genome size andmore »improved N50 scores. Contig N50 scores improved by 35%, 9% and 9% for Chardonnay, mosquito and the thorny skate, respectively, relative to unreduced primary assemblies. The benefits of HapSolo were amplified by down-stream analyses, which we illustrated by scaffolding with Hi-C data. We found, for example, that prior to the application of HapSolo, only 52% of the Chardonnay genome was captured in the largest 19 scaffolds, corresponding to the number of chromosomes. After the application of HapSolo, this value increased to ~ 84%. The improvements for the mosquito’s largest three scaffolds, representing the number of chromosomes, were from 61 to 86%, and the improvement was even more pronounced for thorny skate. We compared the scaffolding results to assemblies that were based on PurgeDups for identifying secondary contigs, with generally superior results for HapSolo.« less
    Free, publicly-accessible full text available December 1, 2022
  3. During metamorphosis, the wings of a butterfly sprout hundreds of thousands of scales with intricate microstructures and nano-structures that determine the wings’ optical appearance, wetting characteristics, thermodynamic properties, and aerodynamic behavior. Although the functional characteristics of scales are well known and prove desirable in various applications, the dynamic processes and temporal coordination required to sculpt the scales’ many structural features remain poorly understood. Current knowledge of scale growth is primarily gained from ex vivo studies of fixed scale cells at discrete time points; to fully understand scale formation, it is critical to characterize the time-dependent morphological changes throughout their development. Here, we report the continuous, in vivo, label-free imaging of growing scale cells of Vanessa cardui using speckle-correlation reflection phase microscopy. By capturing time-resolved volumetric tissue data together with nanoscale surface height information, we establish a morphological timeline of wing scale formation and gain quantitative insights into the underlying processes involved in scale cell patterning and growth. We identify early differences in the patterning of cover and ground scales on the young wing and quantify geometrical parameters of growing scale features, which suggest that surface growth is critical to structure formation. Our quantitative, time-resolved in vivo imaging of butterfly scalemore »development provides the foundation for decoding the processes and biomechanical principles involved in the formation of functional structures in biological materials.« less
    Free, publicly-accessible full text available December 7, 2022
  4. Free, publicly-accessible full text available July 1, 2023
  5. Free, publicly-accessible full text available April 1, 2023
  6. Dataframes have become universally popular as a means to represent data in various stages of structure, and manipulate it using a rich set of operators---thereby becoming an essential tool in the data scientists' toolbox. However, dataframe systems, such as pandas, scale poorly---and are non-interactive on moderate to large datasets. We discuss our experiences developing Modin, our first cut at a parallel dataframe system, which already has users across several industries and over 1M downloads. Modin translates pandas functions into a core set of operators that are individually parallelized via columnar, row-wise, or cell-wise decomposition rules that we formalize in this paper. We also introduce metadata independence to allow metadata---such as order and type---to be decoupled from the physical representation and maintained lazily. Using rule-based decomposition and metadata independence, along with careful engineering, Modin is able to support pandas operations across both rows and columns on very large dataframes---unlike Koalas and Dask DataFrames that either break down or are unable to support such operations, while also being much faster than pandas.
    Free, publicly-accessible full text available November 1, 2022
  7. Abstract

    sPHENIX is a high energy nuclear physics experiment under construction at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory (BNL). The primary physics goals of sPHENIX are to study the quark-gluon-plasma, as well as the partonic structure of protons and nuclei, by measuring jets, their substructure, and heavy flavor hadrons in$$p$$p$$+$$+$$p$$p,p+ Au, and Au + Au collisions. sPHENIX will collect approximately 300 PB of data over three run periods, to be analyzed using available computing resources at BNL; thus, performing track reconstruction in a timely manner is a challenge due to the high occupancy of heavy ion collision events. The sPHENIX experiment has recently implemented the A Common Tracking Software (ACTS) track reconstruction toolkit with the goal of reconstructing tracks with high efficiency and within a computational budget of 5 s per minimum bias event. This paper reports the performance status of ACTS as the default track fitting tool within sPHENIX, including discussion of the first implementation of a time projection chamber geometry within ACTS.

  8. Free, publicly-accessible full text available November 1, 2022
  9. Biofuel crops, including annuals such as maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and canola (Brassica napus L.), as well as high-biomass perennial grasses such as miscanthus (Miscanthus giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large-scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009?2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots beingmore »notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2?34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011.« less