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  1. Charles, Cyril (Ed.)
    Manually collecting landmarks for quantifying complex morphological phenotypes can be laborious and subject to intra and interobserver errors. However, most automated landmarking methods for efficiency and consistency fall short of landmarking highly variable samples due to the bias introduced by the use of a single template. We introduce a fast and open source automated landmarking pipeline (MALPACA) that utilizes multiple templates for accommodating large-scale variations. We also introduce a K-means method of choosing the templates that can be used in conjunction with MALPACA, when no prior information for selecting templates is available. Our results confirm that MALPACA significantly outperforms single-template methods in landmarking both single and multi-species samples. K-means based template selection can also avoid choosing the worst set of templates when compared to random template selection. We further offer an example of post-hoc quality check for each individual template for further refinement. In summary, MALPACA is an efficient and reproducible method that can accommodate large morphological variability, such as those commonly found in evolutionary studies. To support the research community, we have developed open-source and user-friendly software tools for performing K-means multi-templates selection and MALPACA.
    Free, publicly-accessible full text available December 1, 2023
  2. Free, publicly-accessible full text available October 1, 2023
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  6. Free, publicly-accessible full text available May 17, 2023
  7. Abstract Non-perennial rivers and streams make up over half the global river network and are becoming more widespread. Transitions from perennial to non-perennial flow are a threshold-type change that can lead to alternative stable states in aquatic ecosystems, but it is unknown whether streamflow itself is stable in either wet (flowing) or dry (no-flow) conditions. Here, we investigated drivers and feedbacks associated with regime shifts between wet and dry conditions in an intermittent reach of the Arkansas River (USA) over the past 23 years. Multiple lines of evidence suggested that these regimes represent alternative stable states, including (a) significant jumps in discharge time series that were not accompanied by jumps in flow drivers such as precipitation and groundwater pumping; (b) a multi-modal state distribution with 92% of months experiencing no-flow conditions for <10% or >90% of days, despite unimodal distributions of precipitation and pumping; and (c) a hysteretic relationship between climate and flow state. Groundwater levels appear to be the primary control over the hydrological regime, as groundwater levels in the alluvial aquifer were higher than the stream stage during wet regimes and lower than the streambed during dry regimes. Groundwater level variation, in turn, was driven by processes occurringmore »at both the regional scale (surface water inflows from upstream, groundwater pumping) and the reach scale (stream–aquifer exchange, diffuse recharge through the soil column). Historical regime shifts were associated with diverse pressures including network disconnection caused by upstream water use, increased flow stability potentially associated with reservoir operations, and anomalous wet and dry climate conditions. In sum, stabilizing feedbacks among upstream inflows, stream–aquifer interactions, climate, vegetation, and pumping appear to create alternative wet and dry stable states at this site. These stabilizing feedbacks suggest that widespread observed shifts from perennial to non-perennial flow will be difficult to reverse.« less
    Free, publicly-accessible full text available June 16, 2023
  8. Free, publicly-accessible full text available April 1, 2023
  9. AlN thin films are enabling significant progress in modern optoelectronics, power electronics, and microelectromechanical systems. The various AlN growth methods and conditions lead to different film microstructures. In this report, phonon scattering mechanisms that impact the cross-plane (κ z ; along the c-axis) and in-plane (κ r ; parallel to the c-plane) thermal conductivities of AlN thin films prepared by various synthesis techniques are investigated. In contrast to bulk single crystal AlN with an isotropic thermal conductivity of ∼330 W/m K, a strong anisotropy in the thermal conductivity is observed in the thin films. The κ z shows a strong film thickness dependence due to phonon-boundary scattering. Electron microscopy reveals the presence of grain boundaries and dislocations that limit the κ r . For instance, oriented films prepared by reactive sputtering possess lateral crystalline grain sizes ranging from 20 to 40 nm that significantly lower the κ r to ∼30 W/m K. Simulation results suggest that the self-heating in AlN film bulk acoustic resonators can significantly impact the power handling capability of RF filters. A device employing an oriented film as the active piezoelectric layer shows an ∼2.5× higher device peak temperature as compared to a device based on an epitaxial film.
    Free, publicly-accessible full text available November 7, 2023
  10. Liu, Jie (Ed.)
    Metastatic cancer accounts for over 90% of all cancer deaths, and evaluations of metastasis potential are vital for minimizing the metastasis-associated mortality and achieving optimal clinical decision-making. Computational assessment of metastasis potential based on large-scale transcriptomic cancer data is challenging because metastasis events are not always clinically detectable. The under-diagnosis of metastasis events results in biased classification labels, and classification tools using biased labels may lead to inaccurate estimations of metastasis potential. This issue is further complicated by the unknown metastasis prevalence at the population level, the small number of confirmed metastasis cases, and the high dimensionality of the candidate molecular features. Our proposed algorithm, called P ositive and unlabeled L earning from U nbalanced cases and S parse structures ( PLUS ), is the first to use a positive and unlabeled learning framework to account for the under-detection of metastasis events in building a classifier. PLUS is specifically tailored for studying metastasis that deals with the unbalanced instance allocation as well as unknown metastasis prevalence, which are not considered by other methods. PLUS achieves superior performance on synthetic datasets compared with other state-of-the-art methods. Application of PLUS to The Cancer Genome Atlas Pan-Cancer gene expression data generated metastasis potentialmore »predictions that show good agreement with the clinical follow-up data, in addition to predictive genes that have been validated by independent single-cell RNA-sequencing datasets.« less
    Free, publicly-accessible full text available March 29, 2023