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1,049 results for “robustness”
Supplementary Data "Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization"
<p>Pe-computed data for re-producing the results of "Robustness and Generalization in Quantum<br>Reinforcement Learning via Lipschitz Regularization".</p> <p>See also the GitHub repository https://github.com/nicomeyer96/regularized-qpg </p>
Morphology and mini-barcodes: The inclusion of larval sampling and NGS-based barcoding improves robustness of ecological analyses of mosquito communities
<p class="Normal0">A significant proportion of vector-borne diseases are transmitted by blood-sucking dipterans, including mosquitoes. Understanding transmission risks requires accurate identification of species across heterogenous habitats, but many cryptic and polymorphic species are overlooked when using morphological identification. Estimates of mosquito diversity are typically based on adult female trapping methods which tend to target host-seeking species and may represent a biased snapshot of community structure. Unfortunately, diversity estimates based on larval data are rarely included in mosquito ecological analyses. We carried out adult and larval sampling over six months in Singapore using an integrative approach of morphological identification and molecular delineation with mini-barcodes (313 bp) generated on a Next Generation Sequencing platform to obtain species estimates. We collected 3201 mosquitoes across 58 species (14 genera). Notably, 16 species were collected only through larval sampling and 22 species were only resolved using mini-barcodes. Of the latter we identified three morphologically similar species groups and documented several intraspecific polymorphisms. We compared adult-only data against a full dataset (adult + larval + mini-barcode). The species accumulation curves reached an asymptote for all but one site when using the latter; non-metric multidimensional scaling (NMDS) revealed that mosquito communities were only well separated when using the full dataset. Overall, the latter reflects a more defined and accurate community structure across all sites. We find that several mosquito species were generally influenced by tree cover, rainfall and presence of large water bodies, further supporting the idea that many species are niche-specific. <i>Synthesis and applications</i>. We report the first successful use of mini-barcodes on mosquitoes and demonstrate its utility in delineating multiple challenging species groups. We recommend the use of both morphological and molecular identification methods for ecological studies and vector surveillance. Misidentification in species estimation, especially for medically relevant insect groups can lead to conflicting reports and slows down vector control efforts. We provide evidence that varying sampling techniques, particularly of the larval stages for holometabolous insects, is important in generating a robust dataset for downstream analyses. Together with DNA barcoding, this integrative approach helps to minimize error cascades when designing management strategies.</p>
Phylogenetic diversity rankings in the face of extinctions: The robustness of the fair proportion index
<p>Planning for the protection of species often involves difficult choices about which species to prioritize, given constrained resources.<br> One way of prioritizing species is to consider their "evolutionary distinctiveness'', i.e. their relative evolutionary isolation on a phylogenetic tree. Several evolutionary isolation metrics or phylogenetic diversity indices have been introduced in the literature, among them the so-called Fair Proportion index (also known as the "evolutionary distinctiveness" score). This index apportions the total diversity of a tree among all leaves, thereby providing a simple prioritization criterion for conservation. </p> <p>Here, we focus on the prioritization order obtained from the Fair Proportion index and analyze the effects of species extinction on this ranking. More precisely, we analyze the extent to which the ranking order may change when some species go extinct and the Fair Proportion index is re-computed for the remaining taxa. We show that for each phylogenetic tree, there are edge lengths such that the extinction of one leaf per cherry completely reverses the ranking. Moreover, we show that even if only the lowest ranked species goes extinct, the ranking order may drastically change. <br> We end by analyzing the effects of these two extinction scenarios (extinction of the lowest ranked species and extinction of one leaf per cherry) for a collection of empirical and simulated trees. In both cases, we can observe significant changes in the prioritization orders, highlighting the empirical relevance of our theoretical findings.</p>
FIGURE 2. Bromus husainii. A. Inflorescence. B. Robust spikelet. C. Lower glume. D. Upper glume. E–K. Lemma. L. Palea. M. Anthers. N in Bromus husainii (Poaceae:Bromeae), a new species from Valley of Flowers National Park, Uttarakhand, India
FIGURE 2. Bromus husainii. A. Inflorescence. B. Robust spikelet. C. Lower glume. D. Upper glume. E–K. Lemma. L. Palea. M. Anthers. N. Gynoecium.
Robust full-spectral color tuning of photonic colloids
<p><strong>Research Data supporting “</strong><strong>Robust full-spectral color tuning of photonic colloids</strong><strong>”</strong></p> <p>Andrea Dodero, Kenza Djeghdi, Viola Bauernfeind, Martino Airoldi, Bodo D. Wilts, Christoph Weder, Ullrich Steiner, Ilja Gunkel</p> <p><strong><em>Small</em></strong>, DOI: <a href="https://doi.org/10.1002/smll.202205438">10.1002/smll.202205438</a></p> <p>The data are arranged into different folders, containing the following files (.txt, .tif, .xlxs, etc). These data should be read in conjunction with the manuscript and “Supporting Info”, both of which may be found at the following DOI: <a href="https://doi.org/10.1002/smll.202205438">10.1002/smll.202205438</a></p>
A robust estimate of continental-scale terrestrial carbon sinks using GOSAT XCO2 retrievals
<p>The dataset contains monthly terrestrial ecosystem carbon fluxes (NEE) for 51 terrestrial regions from 2011-2014. We used simulations from 12 terrestrial biosphere models (TBMs) as the prior carbon fluxes, therefore the posterior carbon fluxes correspond to the 12 TBMs.</p>
Robust Almost-Sure Reachability in Multi-Environment MDPs: Supplemental Material
<p>Supplemental material for the paper: Robust Almost-Sure Reachability in Multi-Environment MDPs</p> <p>Submitted to TACAS 2023</p>
Transmission of a bumblebee parasite is robust despite exposure to extreme temperatures
<p>Data for Transmission of a bumblebee parasite is robust despite exposure to extreme temperatures</p>
Data files for Peizhi Mai et al., "Robust charge-density wave correlations in the electron-doped single-band Hubbard model" (2023)
<p>Data files for "Robust charge-density wave correlations in the electron-doped single-band Hubbard model" by P. Mai, N. S. Nichols, S. Karakuzu, F. Bao, A Del Maestro, T. A. Maier, and Steven Johnston</p> <p>Preprint: https://arxiv.org/abs/2210.14930</p>
Experiments for 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'
<p>This artifact accompanies the CAV 2023 paper with the title 'Efficient Sensitivity Analysis for Parametric Robust Markov Chains'. The artifact contains a docker file, which can be unzipped and then loaded with:</p> <pre><code>docker load -i prmc_sensitivity_cav23_docker.tar</code></pre> <p>Depending on your permissions, you may need to run this command with sudo. Please refer to the ReadMe for more information.</p> <p>The source code of the docker container is available on GitHub: <a href="https://github.com/LAVA-LAB/prmc-sensitivity">https://github.com/LAVA-LAB/prmc-sensitivity</a>.</p>
50 shades of greenbeard: Robust evolution of altruism based on similarity of complex phenotypes
<p>We study the evolution of altruistic behavior under a model where individuals choose to cooperate by comparing a set of continuous phenotype tags. Individuals play a donation game and only donate to other individuals that are sufficiently similar to themselves in a multidimensional phenotype space. We find the generic maintenance of robust altruism when phenotypes are multidimensional. Selection for altruism is driven by the coevolution of individual strategy and phenotype; altruism levels shape the distribution of individuals in phenotype space. Low donation rates induce a phenotype distribution that renders the population vulnerable to the invasion of altruists, whereas high donation rates prime a population for cheater invasion, resulting in cyclic dynamics that maintain substantial levels of altruism. Altruism is therefore robust to invasion by cheaters in the long term in this model. Furthermore, the shape of the phenotype distribution in high phenotype dimension allows altruists to better resist the invasion by cheaters, and as a result, the amount of donation increases with increasing phenotype dimension. We also generalize previous results in the regime of weak selection to two competing strategies in continuous phenotype space, and show that success under weak selection is crucial to success under strong selection in our model. Our results support the viability of a simple similarity-based mechanism for altruism in a well-mixed population.</p>
Underlying data for: nf-core/clipseq - a robust Nextflow pipeline for comprehensive CLIP data analysis
<p>Underlying data for: nf-core/clipseq - a robust Nextflow pipeline for comprehensive CLIP data analysis</p>
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering (data)
<p>MIBI-TOF data for lymph node dataset reported in Liu et al., Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering</p> <p>1. mibi_single_channel_tifs.zip: Single-channel MIBI-TOF images</p> <p>Folders are labeled according to the field-of-view (FOV) number. Each folder contains single-channel TIFFs for each marker in the panel. Images are 1024x1024 pixels, 500 um. See paper for details.</p> <p>2. segmentation.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021). Output of Mesmer that delineates the single cells in each of the images is included.</p> <p>3. source_data.zip: Source data files for figures</p> <ul> <li>pixel_ccs_allpreprocessing.csv: Cluster consistency score (CCS) for all pixels using all preprocessing steps, related to Fig. 2d-f, Supp. Fig. 4,5,9,10</li> <li>pixel_ccs_nopixelnorm.csv: CCS for all pixels where pixel normalization was left out, related to Fig. 2f, Supp. Fig. 6</li> <li>pixel_ccs_nochannelnorm.csv: CCS for all pixels where channel normalization was left out, related to Fig. 2f, Supp. Fig. 8</li> <li>pixel_ccs_passes1.csv: CCS for all pixels where 1 pass was used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_passes100.csv: CCS for all pixels where 100 passes were used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_sigma0.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma1.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma3.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma0_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma1_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma2_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 2 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma3_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_nodes15.csv: CCS for all pixels where 15 nodes were used for SOM training, related to Supp. Fig. 9e</li> <li>pixel_ccs_threshold80.csv: CCS for all pixels where a threshold of 80% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_ccs_threshold98.csv: CCS for all pixels where a threshold of 98% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_info_comparison_table.csv: Number of pixels that were assigned to a cluster outside of cell segmentation masks, related to Fig. 3d</li> <li>single_cell_pixel_composition_table.csv: Pixel composition information for each single cell, related to Fig. 5, Supp. Fig 16</li> <li>single_cell_integrated_expression_table.csv: Integrated expression per cell, output by Mesmer, related to Fig. 5, Supp. Fig. 16</li> <li>cell_silhouette_scores.csv: Silhouette scores for comparing integrated expression and pixel composition, related to Fig. 5d</li> <li>cell_silhouette_scores_undefinedremoved.csv: Silhouette scores for comparing integrated expression and pixel composition where undefined cells were removed, related to Fig. 16g</li> <li>cell_silhouette_scores_preprocessed.csv: Silhouette scores for comparing integrated expression and pixel composition where pixels were preprocessed before integrating expression, related to Fig. 17d</li> <li>cell_silhouette_scores_ilastik_cellprofiler.csv: Silhouette scores for comparing integrated expression and pixel composition where segmentation masks were obtained using Ilastik/CellProfiler, related to Fig. 18b</li> <li>cell_ccs_pixel_composition.csv: CCS for all cells using pixel composition for clustering, related to Supp. Fig. 16e, 17c</li> <li>cell_ccs_integrated_expression.csv: CCS for all cells using integrated expression for clustering, related to Supp. Fig 16e-f</li> <li>cell_ccs_integrated_expression_preprocessed.csv: CCS for all cells using integrated expression for clustering where data was preprocessed before integrating, related to Supp. Fig 17c</li> <li>cytof_ccs.csv: CCS of the CyTOF dataset used as a benchmark, related to Supp. Fig. 4c,d</li> <li>scrnaseq_ccs.csv: CCS of the scRNA-seq dataset used as a benchmark, related to Supp. Fig. 4c,e</li> <li>pixel_phenotype_maps: TIFFs where pixel value corresponds to pixel cluster number as reported in the paper</li> <li>cell_phenotype_maps: TIFFs where pixel value corresponds to cell cluster number as reported in the paper</li> <li>runtime_analysis_pixel.csv: Runtime analysis of pixel clustering in Pixie, related to Supp. Fig. 22a</li> <li>runtime_analysis_cell.csv: Runtime analysis of cell clustering in Pixie, related to Supp. Fig. 22b</li> <li>runtime_clustering_algorithm.csv: Runtime analysis of different clustering algorithms, related to Supp. Fig. 22c</li> </ul>
Fig. 7. Superimposed 3D in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 7. Superimposed 3D structure of HRP-C (in brown) on A) 1AP2 and B) POXalf. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6. A in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 6. A) RMSD and B) RMSF plots obtained from the analysis of MD simulations (120 ns) of POXalf model.
Fig. 5. A in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 5. A) Approved 3D model of POXalf (residues 61–75 are 80% transparent). B) Topology of helices in 3D structures of POXalf and HRP (PDB:1HCH). C) Stereo environments of distal and proximal Ca ions in POXalf structure.
Fig. 3. A in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 3. A) The 2-D gel IEF of POXalf. B) Optimal pH of activity, C) optimal temperature of activity, and D) the thermal stability of POXalf in the presence of phenol (●) and guaiacol (▴). The average of triple assays of POXsolution (stored at 4 ◦ C) activity in the presence of phenol on E) day 1 and F) day 720. All the phenol (8.6 mM) alf and guaiacol (5 mM) reactions were carried out in PBS (10 mM) in the presence of constant amounts of POXalf (65.35 nM) and H2O2 (6.76 mM). Constant pH of 7 and 6 (for phenol and guaiacol, respectively) and constant temperature (20 ± 1 ◦ C) were applied when needed. POXwas maintained 10 min at the desired pH and alf temperature prior to the assays for the stability examinations.
Fig. 2. A in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 2. A) SDS-PAGE of purified POXalf [lane1: Ladder, lane2:HRP, lanes3,4&5: POXalf] stained by Coomassie blue (left) and silver nitrate (right). Chromatograms of POXalf purification on B) size exclusion column [Sephadex G50, pH 6] and C) ion-exchange column [S-Sepharose, pH 6, NaCl 0.2 M]. D) UV–Visible spectrum of purified POXalf. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1. A in Structure and activity of a novel robust peroxidase from Alkanna frigida cell culture
Fig. 1. A) A. frigida callus at the end of a 31- day subculture and B) its corresponding growth profile. C) POX production in the calluses of A. frigida [A.f], Arnebia euchroma [A.e], Lithospermum officinale [L.o], Onosma dasytrichum [O.d], and Nonea caspica [N.c]. D) POX (filled columns) and CAT (dashed columns) activities in the extract of A. frigida callus during a 31-day subculture. The subcultures were carried out on solid MS medium containing kinetin (10 μM), 2,4-D (1 μM), and sucrose (5% w/v) in darkness at 25 ◦ C. See the experimental section for the enzymatic assays conditions.
Fig. 5 in A robust method for simultaneous quantification of eugenol, eugenyl acetate, and β-caryophyllene in clove essential oil by vibrational spectroscopy
Fig. 5. The score plots of the wavenumbers obtained from the best PLS models. a) Total eugenol, b) Eugenyl acetate, c) β-caryophyllene. PLS: Partial least square.
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.