1,864 research outputs found

    Cortisol levels are positively associated with pup-feeding rates in male meerkats

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    In societies of cooperative vertebrates, individual differences in contributions to offspring care are commonly substantial. Recent attempts to explain the causes of this variation have focused on correlations between contributions to care and the protein hormone prolactin, or the steroid hormone testosterone. However, such studies have seldom considered the importance of other hormones or controlled for non-hormonal factors that are correlative with both individual hormone levels and contributions to care. Using multivariate statistics, we show that hormone levels explain significant variation in contributions to pup-feeding by male meerkats, even after controlling for non-hormonal effects. However, long-term contributions to pup provisioning were significantly and positively correlated with plasma levels of cortisol rather than prolactin, while plasma levels of testosterone were not related to individual patterns of pup-feeding. Furthermore, a playback experiment that used pup begging calls to increase the feeding rates of male helpers gave rise to parallel increases in plasma cortisol levels, whilst prolactin and testosterone levels remained unchanged. Our findings confirm that hormones can explain significant amounts of variation in contributions to offspring feeding, and that cortisol, not prolactin, is the hormone most strongly associated with pup-feeding in cooperative male meerkats

    Characterizing soil stiffness using thermal remote sensing and machine learning

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    Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms

    Utilizing hyperspectral remote sensing for soil gradation

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    Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil

    The emergent integrated network structure of scientific research

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    The practice of scientific research is often thought of as individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than would be possible by individuals acting in isolation. Currently, the structure of this integrated and innovative landscape of scientific ideas is not well understood. Here we use tools from network science to map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000. We construct networks in which nodes represent topics of study and edges give the degree to which topics occur in the same papers. The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters. Notably, clusters tend not to align with assigned article classifications, but instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define a novel measure of interdisciplinarity, which is positively associated with PNAS's impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight

    The Gut Microbiome and Substance Use Disorder.

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    Substance use disorders (SUDs) remain a significant public health challenge, affecting tens of millions of individuals worldwide each year. Often comorbid with other psychiatric disorders, SUD can be poly-drug and involve several different substances including cocaine, opiates, nicotine, and alcohol. SUD has a strong genetic component. Much of SUD research has focused on the neurologic and genetic facets of consumption behavior. There is now interest in the role of the gut microbiome in the pathogenesis of SUD. In this review, we summarize current animal and clinical evidence that the gut microbiome is involved in SUD, then address the underlying mechanisms by which the gut microbiome interacts with SUD through metabolomic, immune, neurological, and epigenetic mechanisms. Lastly, we discuss methods using various inbred and outbred mice models to gain an integrative understanding of the microbiome and host genetic controls in SUD

    Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence Alignment

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    Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique in bioinformatics used to infer related residues among biological sequences. Thus alignment accuracy is crucial to a vast range of analyses, often in ways difficult to assess in those analyses. To compare the performance of different aligners and help detect systematic errors in alignments, a number of benchmarking strategies have been pursued. Here we present an overview of the main strategies--based on simulation, consistency, protein structure, and phylogeny--and discuss their different advantages and associated risks. We outline a set of desirable characteristics for effective benchmarking, and evaluate each strategy in light of them. We conclude that there is currently no universally applicable means of benchmarking MSA, and that developers and users of alignment tools should base their choice of benchmark depending on the context of application--with a keen awareness of the assumptions underlying each benchmarking strategy.Comment: Revie

    First Measurement of Monoenergetic Muon Neutrino Charged Current Interactions

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    We report the first measurement of monoenergetic muon neutrino charged current interactions. MiniBooNE has isolated 236 MeV muon neutrino events originating from charged kaon decay at rest (K+μ+νμK^+ \rightarrow \mu^+ \nu_\mu) at the NuMI beamline absorber. These signal νμ\nu_\mu-carbon events are distinguished from primarily pion decay in flight νμ\nu_\mu and νμ\overline{\nu}_\mu backgrounds produced at the target station and decay pipe using their arrival time and reconstructed muon energy. The significance of the signal observation is at the 3.9σ\sigma level. The muon kinetic energy, neutrino-nucleus energy transfer (ω=EνEμ\omega=E_\nu-E_\mu), and total cross section for these events is extracted. This result is the first known-energy, weak-interaction-only probe of the nucleus to yield a measurement of ω\omega using neutrinos, a quantity thus far only accessible through electron scattering.Comment: 6 pages, 4 figure
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