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Landing maneuvers of flies are complex behaviors which can be conceptually decomposed into sequences of modular actions, including body-deceleration, leg-extension, and body rotations. These behavioral ‘modules’ must be coordinated to ensure well-controlled landing. The composite nature of these behaviors induces kinematic variability, making it difficult to identify the central rules that govern landing. Many previous studies have relied on tethered preparations to study landing behaviors, but tethering induces experimental artefacts by forcing some behaviors to operate in open-feedback control loop while others remain closed-loop. On the other hand, freely-flying insects are harder to precisely control, and hence inherently prone to behavioral variability. One approach towards understanding general mechanisms of landing is to determine the common elements of their kinematics on surfaces of different orientations. We conducted a series of experiments in which the houseflies, Musca domestica, were lured to land on vertical (vertical landings) or inverted (inverted landings) substrates, while their flight was recorded with multiple high-speed cameras. We observed that, in both cases, well-controlled landings occurred when the distance at which flies initiated deceleration was proportional to flight velocity component in the direction of substrate. The ratio of substrate distance and velocity at onset of deceleration (tau) was conserved, despite substantial differences in mechanics of vertical vs. inverted landings. When these conditions were not satisfied, their landing performance was compromised, causing their heads to collide into the substrate. Unlike body-deceleration, leg-extension in flies was independent of substrate distance or approach velocity. Thus, the robust reflexive visual initiation of deceleration is independent of substrate orientation, and combines with a more variable initiation of leg-extension which depends on surface orientation. Together, these combinations of behaviors enable flies to land in a versatile manner on substrates of various orientations.
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Background: The low bacterial load in samples acquired from the lungs, have made studies on the airway microbiome vulnerable to contamination from bacterial DNA introduced during sampling and laboratory processing. We have examined the impact of laboratory contamination on samples collected from the lower airways by protected (through a sterile catheter) bronchoscopy and explored various in silico approaches to dealing with the contamination post-sequencing. Our analyses included quantitative PCR and targeted amplicon sequencing of the bacterial 16S rRNA gene. Results: The mean bacterial load varied by sample type for the 23 study subjects (oral wash>1st fraction of protected bronchoalveolar lavage>protected specimen brush>2nd fraction of protected bronchoalveolar lavage; p < 0.001). By comparison to a dilution series of know bacterial composition and load, an estimated 10-50% of the bacterial community profiles for lower airway samples could be traced back to contaminating bacterial DNA introduced from the laboratory. We determined the main source of laboratory contaminants to be the DNA extraction kit (FastDNA Spin Kit). The removal of contaminants identified using tools within the Decontam R package appeared to provide a balance between keeping and removing taxa found in both negative controls and study samples. Conclusions: The influence of laboratory contamination will vary across airway microbiome studies. By reporting estimates of contaminant levels and taking use of contaminant identification tools (e.g. the Decontam R package) based on statistical models that limit the subjectivity of the researcher, the accuracy of inter-study comparisons can be improved.
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Arbuscular mycorrhizal fungi (AMF) are important plant symbionts, but we know little about the effects of plant taxonomic identity or functional group on the AMF community composition. To examine effects of the surrounding plant community, of host, and of the AMF pool on the AMF community in plant roots, we manipulated plant community composition in a long-term field experiment. Within four types of manipulated grassland plots, seedlings of eight grassland plant species were planted for 12 weeks, and AMF in their roots were quantified. Additionally, we characterised the AMF community of individual plots (as their AMF pool) and quantified plot abiotic conditions. The largest determinant of AMF community composition was the pool of available AMF, varying at metre scale due to changing soil conditions. The second strongest predictor was the host functional group. The differences between grasses and dicotyledonous forbs in AMF community variation and diversity were much larger than the differences among species within those groups. High cover of forbs in the surrounding plant community had a strong positive effect on AMF colonisation intensity in grass hosts. Using a manipulative field experiment enabled us to demonstrate direct causal effects of plant host and surrounding vegetation.
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Essential tremor (ET) is one of the most common movement disorders. The etiology of ET remains largely unexplained. Whole genome sequencing (WGS) is likely to be of value in understanding a large proportion of ET with Mendelian and complex disease inheritance patterns. In ET families with Mendelian inheritance patterns, WGS may lead to gene identification where WES analysis failed to identify the causative single nucleotide variant (SNV) or indel due to incomplete coverage of the entire coding region of the genome, in addition to accurate detection of larger structural variants (SVs) and copy number variants (CNVs). Alternatively, in ET families with complex disease inheritance patterns with gene x gene and gene x environment interactions enrichment of functional rare coding and non-coding variants may explain the heritability of ET. We performed WGS in eight ET families (n=40 individuals) enrolled in the Family Study of Essential Tremor. The analysis included filtering WGS data based on allele frequency in population databases, rare SNV and indel classification and association testing using the Mixed-Model Kernel Based Adaptive Cluster (MM-KBAC) test. A separate analysis of rare SV and CNVs segregating within ET families was also performed. Prioritization of candidate genes identified within families was performed using phenolyzer. WGS analysis identified candidate genes for ET in 5/8 (62.5%) of the families analyzed. WES analysis in a subset of these families in our previously published study failed to identify candidate genes. In one family, we identified a deleterious and damaging variant (c.1367G>A, p.(Arg456Gln)) in the candidate gene, CACNA1G, which encodes the pore forming subunit of T-type Ca(2+) channels, CaV3.1, and is expressed in various motor pathways and has been previously implicated in neuronal autorhythmicity and ET. Other candidate genes identified include SLIT3 which encodes an axon guidance molecule and in three families, phenolyzer prioritized genes that are associated with hereditary neuropathies (family A, KARS, family B, KIF5A and family F, NTRK1). Functional studies of CACNA1G and SLIT3 suggest a role for these genes in ET disease pathogenesis.
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Cichlid fishes provide textbook examples of explosive phenotypic diversification and sympatric speciation, thereby making them ideal systems for studying the molecular mechanisms underlying rapid lineage divergence. Despite the fact that gene regulation provides a critical link between diversification in gene function and speciation, many genomic regulatory mechanisms such as miRNAs have received little attention in these rapidly diversifying groups. Therefore, we investigated the post-transcriptional regulatory role of miRNAs in the repeated sympatric divergence of Midas cichlids (Amphilophus spp.) from Nicaraguan crater lakes. Using miRNA and mRNA sequencing of embryos from five Midas species, we first identified miRNA binding sites in mRNAs and highlighted the presences of a surprising number of novel miRNAs in these adaptively radiating species. Then, through analyses of expression levels, we identified putative miRNA/gene target pairs with negatively correlated expression level that were consistent with the role of miRNA in downregulating mRNA. Furthermore, we determined that several miRNA/gene pairs show convergent expression patterns associated with the repeated benthic/limnetic sympatric species divergence implicating these miRNAs as potential molecular mechanisms underlying replicated sympatric divergence. Finally, as these candidate miRNA/gene pairs may play a central role in phenotypic diversification in these cichlids, we characterized the expression domains of selected miRNAs and their target genes via in situ hybridization, providing further evidence that miRNA regulation likely plays a role in the Midas cichlid adaptive radiation. These results provide support for the hypothesis that extremely quickly evolving miRNA regulation can contribute to rapid evolutionary divergence even in the presence of gene flow.
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The centralization of locomotor control from weak and local coupling to strong and global is hard to assess outside of particular modeling frameworks. We developed an empirical, model-free measure of centralization that compares information between control signals and both global and local states. A second measure, co-information, quantifies the net redundancy in global and local control. We first validate that our measures predict centralization in simulations of phase-coupled oscillators. We then test how centralization changes with speed in a freely running cockroach. Surprisingly, across all speeds centralization is constant and muscle activity is more informative of the global kinematic state (the averages of all legs) than the local state of that muscle’s leg. Finally we use a legged robot to show that mechanical coupling alone can change the centralization of legged locomotion. The results of these systems span a design space of centralization and co-information for biological and robotic systems.
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Sustained, quantitative observations of nearshore waves and sand levels are essential for testing beach evolution models, but comprehensive datasets are relatively rare. We document beach profiles and concurrent waves monitored at three southern California beaches during 2001-2016. The beaches include offshore reefs, lagoon mouths, hard substrates, and cobble and sandy (medium-grained) sediments. The data span two energetic El Niño winters and four beach nourishments. Quarterly surveys of 165 total cross-shore transects (all sites) at 100m alongshore spacing were made from the backbeach to 8m depth. Monthly surveys of the subaerial beach were obtained at alongshore-oriented transects. The resulting dataset consists of (1) raw sand elevation data, (2) gridded elevations, (3) interpolated elevation maps with error estimates, (4) beach widths, subaerial and total sand volumes, (5) locations of hard substrate and beach nourishments, (6) water levels from a NOAA tide gauge (7) wave conditions from a buoy-driven regional wave model, and (8) time periods and reaches with alongshore uniform bathymetry, suitable for testing 1-dimensional beach profile change models.
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Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.
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Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.
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In deterministic models of epidemics, there is a host abundance threshold, above which the introduction of a few infected individuals leads to a severe epidemic. Studies of weather-driven animal pathogens often assume that abundance thresholds will be overwhelmed by weather-driven stochasticity, but tests of this assumption are lacking. We collected observational and experimental data for a fungal pathogen, {\it Entomophaga maimaiga}, that infects the gypsy moth, {\it Lymantria dispar}. We used an advanced statistical-computing algorithm to fit mechanistic models to our data, such that different models made different assumptions about the effects of host density and weather on {\it E. maimaiga} epizootics (epidemics in animals). We then used AIC analysis to choose the best model. In the best model, epizootics are driven by a combination of weather and host density, and the model does an excellent job of explaining the data, whereas models that allow only for weather effects, or only density-dependence effects, do a poor job of explaining the data. Density-dependent transmission in our best model produces a host-density threshold, but this threshold is strongly blurred by the stochastic effects of weather. Our work shows that host-abundance thresholds may be important even if weather strongly affects transmission, suggesting that epidemiological models that allow for weather have an important role to play in understanding animal pathogens. The success of our model means that it could be useful for managing the gypsy moth, an important pest of hardwood forests in North America.
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