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2,904 results for “Solute”
Data from: D3.1. Parametric library of Nature Based Solutions (NBS)
<p><span>For updated versions of the files (e.g., parametric design tool), please, contact <a href="mailto:jorge.rodriguez@unican.es">jorge.rodriguez@unican.es</a></span></p>
THE ROLE OF UZBEK DIALECTS ON PRONUNCIATION OF ENGLISH LANGUAGE, PROBLEMS AND SOLUTIONS
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Dataset for Unlocking the Mitigation Potential of Landfills as A Cost-Effective Solution for Climate Change
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SPARCS_WP3_Espoo_City_SPARCS-WP3 other stakeholders contributed in co-created solutions in Espoo
<p>Number of other stakeholders contributing in co-created solutions during SPARCS Work Package 3 (WP3) actions</p>
Data Sets for "Efficient Solution of the Number Partitioning Problem on a Quantum Annealer: A Hybrid Quantum-Classical Decomposition Approach"
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Figures for electrospinning of PVA-CS-CH3COOH-C2H5OH-H2O solution
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Data from: Character Analysis in Morphological Phylogenetics: Problems and Solutions
Many aspects of morphological phylogenetics are controversial in the theoretical systematics literature and yet are often poorly explained and justified in empirical studies. In this paper, I argue that most morphological characters describe variation that is fundamentally quantitative, regardless of whether it is coded qualitatively or quantitatively by systematists. Given this view, three fundamental problems in morphological character analysis (character state definition, delimitation, and ordering) may have a common solution: coding morphological characters as continuous quantitative traits. A new parsimony method (step-matrix gap-weighting, a modification of Thiele's approach) is proposed that allows quantitative traits to be analyzed as continuous variables. The problem of scaling or weighting quantitative characters relative to qualitative characters (and to each other) is reviewed, and three possible solutions are described. The new coding method is applied to data from hoplocercid lizards, and the results show the sensitivity of phylogenetic conclusions to different scaling methods. Although some authors reject the use of continuous, overlapping, quantitative characters in phylogenetic analysis, quantitative data from hoplocercid lizards that are coded using the new approach do contain significant phylogenetic structure, and exhibit levels of homoplasy that are similar to those seen in data that are coded qualitatively.
Data from: Solution of the Generalized Noah's Ark Problem
The phylogenetic diversity (PD) of a set of species is a measure of the evolutionary distance among the species in the collection, based on a phylogenetic tree. Such a tree is composed of a root, of internal nodes and of leaves that correspond to the set of taxa under study. With each edge of the tree is associated a non-negative branch length (evolutionary distance). If a particular survival probability is associated with each taxon, the PD measure becomes the expected PD measure. In the Noah's Ark Problem (NAP) introduced by Weitzman (1998), these survival probabilities can be increased at some cost. The problem is to determine how best to allocate a limited amount of resources to maximize the expected PD of the considered species. It is easy to formulate the NAP as a (difficult) nonlinear 0-1 programming problem. The aim of this article is to show that a general version of the NAP (GNAP) can be solved simply and efficiently with any set of edge weights and any set of survival probabilities by using standard mixed-integer linear programming software. The crucial point to move from a nonlinear program in binary variables to a mixed-integer linear program, is to approximate the logarithmic function by the lower envelope of a set of tangents to the curve. Solving the obtained mixed-integer linear program provides not only a near-optimal solution but also an upper bound on the value of the optimal solution. We also applied this approach to a generalization of the Nature Reserve Problem (GNRP) that consists of selecting a set of regions to be conserved so that the expected phylogenetic diversity of the set of species present in these regions is maximized. In this case, the survival probabilities of different taxa are not independent of each other. Computational results are presented to illustrate potentialities of the approach. Near-optimal solutions with hypothetical phylogenetic trees comprising about 4,000 taxa are obtained in a few seconds or minutes of computing time for the GNAP, and in about 30 minutes for the GNRP. In all the cases the average guarantee varies from 0% to 1.20%.
Data from: Challenges and solutions for analyzing dual RNA-seq data for non-model host/pathogen systems
1. Dual RNA-seq simultaneously profiles the transcriptomes of a host and pathogen during infection and may reveal the mechanisms underlying host-pathogen interactions. Dual RNA-seq is inherently a mixture of transcripts from at least two species (host and pathogen), so this mixture must be computationally sorted into host and pathogen components. Sorting relies on aligning reads to respective reference genomes, which may be unavailable for both species in non-model host-pathogen pairs. This lack of genomic resources may present challenges to applying dual RNA-seq to non-model systems. 2. We assessed the accuracy of alignments of dual RNA-seq when using the genomic resources of a closely-related species to the species of interest by simulating datasets of mixed transcripts from a host and pathogen. Specifically, we compared how different aligners performed across different proportions of pathogen to host transcripts and across variation in the genetic distance between the pathogen genome and reference genome. We performed extensive analyses for a host plant with fungal pathogen, and then we extended the plant-fungus results by repeating key analyses in vertebrate (human)-fungus and vertebrate-bacterium systems. 3. Aligners that were able to map pathogen transcripts to the reference genome of a species closely related to the pathogen (a "related reference genome") also mismapped transcripts originating from the host to the pathogen's related reference genome, which results in regions where this occurred being quantified as overexpressed. If a host reference genome was available, we show that to minimize host transcript mismapping while retaining the ability to map pathogen transcripts, one could concatenate it with the pathogen's related reference genome, then map transcripts to the concatenated genomes. If a host genome was unavailable, assembling reads de novo prior to aligning substantially decreased host read mismapping, while retaining the ability to map pathogen transcripts to a related reference genome. 4. The application of dual RNA-seq to organisms without reference genomes is currently limited. We propose an analytical workflow that leverages the genomic resources of species closely related to species of interest to facilitate application of dual RNA-seq to reveal the mechanisms of host-pathogen interactions across a wider array of systems.
Phase Field model of ice crystallization in sugar solutions
<p>Phase Field model of ice crystallization in sugar solutions</p>
FIGURE 15 in Solution NMR Structure of CalU16 from Micromonospora echinospora, Northeast Structural Genomics Consortium (NESG) Target MiR12
FIGURE 15. Original plate in the description of Nototrema fissipes by Boulenger (1888).
FIGURE 1. Robertsicus elaphensis n. comb., horizontal scale bar 1.0 in Two new genera of hard ticks, Robertsicus n. gen. and Archaeocroton n. gen., and the solution to the mystery of Hoogstraal's and Kaufman's "primitive" tick from the Carpathian Mountains
FIGURE 1. Robertsicus elaphensis n. comb., horizontal scale bar 1.0 mm; vertical scale bar 2.5 mm..
Zircon solubility in solute-rich supercritical fluids and Zr transfer from slab to wedge in the deep subduction process
<p>Geochemical data of primitive arc basalts and MORBs</p>
Weakly hydrated anions bind to polymers but not monomers in aqueous solutions - Source data for Figure 3
<p>Source data that was used to create Figure 3. </p>
Figure 1 from: Correa C (2021) A solution to the enigma of the type locality of Telmatobius halli Noble, 1938 (Anura, Telmatobiidae), a frog lost for 86 years. ZooKeys 1060: 183-192. https://doi.org/10.3897/zookeys.1060.67904
Figure 1 Upper area of the Loa River and surroundings, showing the location of populations recently assigned to Telmatobius halli, its type locality according to this study and other Telmatobius populations near Ollagüe. The railway line that connected Ollagüe with the Collahuasi mine (to the Montt station) and the dirt road that connected the Collahuasi mine and Chiclla with Miño (white arrows) are indicated. Note that the road continues from Miño to the Carcote salt flat. The background map was constructed by joining two maps published by the Comisión Chilena de Límites in 1912.
The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical datasets
<p class="CxSpFirst">The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the Δ<i>K</i> method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of <i>K</i> and is sensitive to uneven sampling. If this finding holds for empirical datasets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of <i>K</i> to re-estimate genetic structure in 41 empirical microsatellite datasets; 15 from a broad range of taxa and 26 focused on a diverse phylogenetic group, coral. We compared alternative estimates of <i>K</i> (Puechmaille statistics) with traditional (Δ<i>K</i> and posterior probability) estimates and found widespread disagreement of estimators across datasets. Thus, one estimator alone is insufficient for determining the optimal number of clusters regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) between clustering solutions did not necessarily clarify which solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to Δ<i>K</i>. Differences between estimators could reveal patterns with important biological implications, such as the potential for more population structure than previously estimated, as was the case for many studies reanalyzed here.</p>
Figure 7 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 7 - Focus stacking in Zerene Stacker. The small image in the upper corner provides a detailed close-up of 518 × 345 pix of the image at 100%. A Stack of 77 pictures, aligned and combined with PMax B Stack of 44 pictures, aligned and combined with PMax The individual pictures of both stacks are made with the Leica Z6 APO with DFC290 and Manfrotto led light system.
Figure 9 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 9 - A 3D model of a Dicranorrhina sp. beetle. The left pictures (A, C) represent the 3D model with its texture, while the right pictures (B, D) are from the model with the mesh only and show the level of detail of the 3D model made in Agisoft Photoscan.
Figure 8 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 8 - Focus stacking in Zerene Stacker. The small image in the upper corner provides a detailed close-up of 518 × 345 pix of the image at 100%. A Stack of 74 pictures, aligned and combined with PMax B Stack of 41 pictures, aligned and combined with PMax. The individual pictures of both stacks are made with the Canon-Cognisys set-up and double flash lights.
Figure 5 from: Brecko J, Mathys A, Dekoninck W, Leponce M, VandenSpiegel D, Semal P (2014) Focus stacking: Comparing commercial top-end set-ups with a semi-automatic low budget approach. A possible solution for mass digitization of type specimens. ZooKeys 464: 1-23. https://doi.org/10.3897/zookeys.464.8615
Figure 5 - The results of the different stacking methods in Zerene Stacker, each with a detail of a hairy leg and a part of the head with hairs. A, B details of the stack pictures by the PMax method C, D details of the stacked picture with the DMap method.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.