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3,937 results for “number”
Strategies of grain number determination differentiate barely row-types
<p>Gaining knowledge on intrinsic interactions of various yield components is crucial to improve the yield potential in small grain cereals. It is well known in barley that increasing the grain number (GN) preponderantly improves their yield potential; however, the yield components determining GN and their association in barley row-types are less explored. In this study, we assessed different yield components like potential spikelet number (PSN), spikelet survival (SSL), spikelet number (SN), grain set (GS), and grain survival (GSL), as well as their interactions with GN by using a selected panel of two- and six-rowed barley types. Also, to analyze the stability of these interactions, we performed the study in two growth conditions, greenhouse and field. From this study, we found that in two-rowed, GN determination is strongly influenced by PSN rather than SSL and/or GS in both growth conditions. Conversely, in six-rowed, GN is associated with SSL instead of PSN and/or GS. Thus, our study exemplified that increasing GN might be possible by augmenting PSN in two-rowed genotypes, while for six-rowed genotypes, the ability of SSL needs to be improved. We speculate that this disparity of GN determination in barley row-types might be due to the fertility of lateral spikelets. Collectively, this study revealed that the GN of two-rowed largely depends on the developmental trait, PSN, while in six-rowed, it mainly follows the ability of SSL. </p>
Boosting Activity and Stability of Metal Single-Atom Catalysts via Regulation of Coordination Number and Local Composition
<p>Optimized geometry coordinates in DFT calculations for the publication "Boosting Activity and Stability of Metal Single-Atom Catalysts via Regulation of Coordination Number and Local Composition".</p> <p> </p>
FIGURE. RAxML tree based on a combined dataset of partial LSU and ITS sequence analyses. Bootstrap support values for ML equal to or greater than 60 %, Bayesian posterior probabilities (BYPP) equal to or greater than 0.95 are shown as ML/ BYPP above the nodes. New isolates are in red bold. The tree is rooted to Conioscypha lignicola and Conioschypha minutispora (FMR11245) and Conioscyphascus varius. The scale bar represents the expected number of nucleotide substitutions per site. in Yunnan-Guizhou Plateau: a mycological hotspot
FIGURE. RAxML tree based on a combined dataset of partial LSU and ITS sequence analyses. Bootstrap support values for ML equal to or greater than 60 %, Bayesian posterior probabilities (BYPP) equal to or greater than 0.95 are shown as ML/ BYPP above the nodes. New isolates are in red bold. The tree is rooted to Conioscypha lignicola and Conioschypha minutispora (FMR11245) and Conioscyphascus varius. The scale bar represents the expected number of nucleotide substitutions per site.
FIGURE. Number of species introduced from different provinces in China over the last decade (2010–2020). in Yunnan-Guizhou Plateau: a mycological hotspot
FIGURE. Number of species introduced from different provinces in China over the last decade (2010–2020).
Dataset for CIIE 2018 folio number 2018062802133: A Computational Literature Review of Educational Innovation
<p>Dataset for CIIE 2018 folio number 2018062802133: A Computational Literature Review of Educational Innovation</p>
More Flexible Number Formats for High-Accuracy Simulations - Randomized Inputs
<p>The random input matrices and vectors used in the thesis "More Flexible Number Formats for High-Accuracy Simulations".</p>
Primitive quartic number fields of absolute discriminant at most 10^9
<p>Complete list of all primitive number fields of degree 4 and absolute discriminant at most 10<sup>9</sup>. Computed through a method similar to that of Belabas [1], but starting from Bhargava's bijection [2] instead of Davenport-Heilbronn's.</p> <p>File "raw": the following data are given for each field:</p> <ul> <li>Coefficients of a pair (F<sub>1</sub>, F<sub>2</sub>) of ternary quadratic forms corresponding to the field under Bhargava's bijection: F<sub>1</sub> = v<sub>0</sub> x<sup>2</sup> + v<sub>1</sub> x y + v<sub>2</sub> x z + v<sub>3</sub> y<sup>2</sup> + v<sub>4</sub> y z + v<sub>5</sub> z<sup>2</sup> and F<sub>2</sub> = v<sub>6</sub> x<sup>2</sup> + v<sub>7</sub> x y + v<sub>8</sub> x z + v<sub>9</sub> y<sup>2</sup> + v<sub>10</sub> y z + v<sub>11</sub> z<sup>2</sup>.</li> <li>Cubic covariant of (F<sub>1</sub>, F<sub>2</sub>): if M<sub>1</sub>, M<sub>2</sub> are the matrices representing F<sub>1</sub>, F<sub>2</sub>, then 4 det(x M<sub>1</sub> + y M<sub>2</sub>) = p<sub>0</sub> x<sup>3</sup> + p<sub>1</sub> x<sup>2</sup> y + p<sub>2</sub> x y<sup>2</sup> + p<sub>3</sub> y<sup>3</sup>.</li> <li>Resultant of F<sub>1</sub>(x, y, 1) and F<sub>2</sub>(x, y, 1) with respect to y: r<sub>0</sub> x<sup>4</sup> + r<sub>1</sub> x<sup>3</sup> + r<sub>2</sub> x<sup>2</sup> + r<sub>3</sub> x + r<sub>4</sub>. This is a defining polynomial for the field.</li> </ul> <p>Files "Ti.gp": the index i is the number of pairs of complex embeddings. The following data are given for each field:</p> <ul> <li>Discriminant.</li> <li>Coefficients of the canonical defining polynomial for the field, as given by PARI's function polredabs.</li> <li>Number of elements and cyclic decomposition of the class group, as given by PARI's function bnfinit.</li> </ul> <p>The following sanity checks have been performed:</p> <ul> <li>The number of totally real fields agrees with that computed by Malle [3] using Hunter's method.</li> <li>The list of discriminants (with multiplicities) agrees with that computed by PARI's function nflist using class field theory (conditionally on the generalised Riemann hypothesis).</li> </ul> <p>References:</p> <ol> <li>Karim Belabas. A fast algorithm to compute cubic fields. <em>Math. Comp.</em>, 66(219):1213–1237, 1997.</li> <li>Manjul Bhargava. Higher composition laws. III. The parametrization of quartic rings. <em>Ann. of Math. (2)</em>, 159(3):1329–1360, 2004.</li> <li>Gunter Malle. The totally real primitive number fields of discriminant at most 10<sup>9</sup>. In <em>Algorithmic number theory</em>, volume 4076 of <em>Lecture Notes in Comput. Sci.</em>, pages 114–123. Springer, Berlin, 2006.</li> </ol>
FIGURE 10. A specimen numbered Bijie Exped. 125 in Raising Thalictrum uncatum var. angustialatum (Ranunculaceae) from China to T. angustialatum as an independent species based on evidence from morphology, geographical distribution, cytology and molecular systematics
FIGURE 10. A specimen numbered Bijie Exped. 125 (PE00471121) and agreeing with other sheets of Bijie Exped. 125 in the collection records, but actually belonging to Thalictrum lecoyeri and not cited in the protologue of T. angustialatum, and thus not an isotype of T. angustialatum.
Table S7.Data on 392 potential triplex-forming lncRNAs and target genes, and the number of R/Y/M-type PTS.
<p>The file includes information about 392 triplex lncRNA related to lncRNA.ensembl,lncRNA.symbol,Number of potential triplex target mRNA,percentage of positively correlated mRNA, as well as information about 7,839 purine motif (R),12,678 pyrimidine motif (Y), and 9,214 purine-pyrimidine motif (or mixed/M) PTS Information.</p>
Weakly-Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT
<p>Modern machine learning pipelines, in particular those based on deep learning (DL) models, require large amounts of labeled data. For classification problems, the most common learning paradigm consists of presenting labeled examples during training, thus providing \emph{strong supervision} by directly presenting examples from the different classes, e.g. positive and negative samples. As a result, the adequate training of these models demands the curation of large datasets with high-quality labels. This constitutes a major obstacle for the development of DL models in radiology---in particular for cross-sectional imaging (e.g., computed tomography [CT] scans)---where labels must come from manual annotations by expert radiologists at the image or slice-level (as opposed to the examination level, such as could be obtained using natural language processing of radiology reports). <br> This work studies the question of what kind of labels should be collected for the problem of intracranial hemorrhage detection in brain CT. We investigate whether image-level annotations should be preferred to examination-level ones. By framing this task as a Multiple Instance Learning (MIL) problem, and employing modern attention-based DL architectures, we analyze the degree to which different levels of supervision improve the detection performance. We find that strong supervision (learning with local image-level annotations) and weak supervision (learning with only global examination-level labels) achieve comparable performance in both examination- and image-level hemorrhage detection, as well as in hemorrhage localization at the pixel-level (explainability). Furthermore, we study this behavior as a function of the number of labels available during training. Our results suggest that local labels may not be necessary at all, drastically reducing the time and cost involved in collecting and curating datasets.</p>
FIGURE. The most diverse genera in Iran, along with the number of species, subspecies and varieties. in A checklist of Iranian Grasses
FIGURE. The most diverse genera in Iran, along with the number of species, subspecies and varieties.
Species of Acantholichen occurring only in the Neotropics show a high degree of endemism (Dal Forno et al. 2016). Of the seven species now recognized in this genus (Table 2), 71.4% (5) are known only from South America. As with Dictyonema, Acantholichen seem to be specific to substrate type and appears in the Andean small forest occurring on mosses in tree bark inhabiting mostly exposed habitats. Cyphellostereum is also represented by a high number of species restricted to the Neotropics [6 (66.6%)], while one is known only from North America, one from Southeastern United States and Puerto Rico, and another species is known only from Borneo and Fiji (Table 2). It is probably due to their unusual appearance that these lichens are getting confused with free-living cyanobacteria colonies, and that there are still undescribed species in the Neotropics. in Eight new species of lichenized Basidiomycota in the genera Acantholichen, Cyphellostereum and Dictyonema s.str. (Agaricales, Hygrophoraceae) from northern South America
Species of Acantholichen occurring only in the Neotropics show a high degree of endemism (Dal Forno et al. 2016). Of the seven species now recognized in this genus (Table 2), 71.4% (5) are known only from South America. As with Dictyonema, Acantholichen seem to be specific to substrate type and appears in the Andean small forest occurring on mosses in tree bark inhabiting mostly exposed habitats. Cyphellostereum is also represented by a high number of species restricted to the Neotropics [6 (66.6%)], while one is known only from North America, one from Southeastern United States and Puerto Rico, and another species is known only from Borneo and Fiji (Table 2). It is probably due to their unusual appearance that these lichens are getting confused with free-living cyanobacteria colonies, and that there are still undescribed species in the Neotropics.
Accuracy of COVID-19 relevant knowledge among youth: Number of information sources matters
<p>Dataset for manuscript: <strong>Accuracy of COVID-19 relevant knowledge among youth: Number of information sources matters </strong></p>
Interaction-driven Spontaneous Ferromagnetic Insulating States with Odd Chern Numbers
<p>Data for "Interaction-driven Spontaneous Ferromagnetic Insulating States with Odd Chern Numbers"</p>
Supplementary material 1 from: Bourret A, Nozères C, Parent E, Parent GJ (2023) Maximizing the reliability and the number of species assignments in metabarcoding studies using a curated regional library and a public repository. Metabarcoding and Metagenomics 7: e98539. https://doi.org/10.3897/mbmg.7.98539
Creation of Gulf of St. Lawrence regional library (GSL-rl) and creation of an eDNA metabarcoding dataset
Random Numbers / Zufallszahlen
<p>List with <strong>500 random numbers</strong> (<strong>Zufallszahlen</strong>) from 0 to 9. Each number has the same probability to appear.</p> <p>The list with the random numbers can be used to make a random group selection, especially important in RCT-trials. </p>
FIGURE 4 in Where to set the bar? Recent descriptions inflate species number in South American toad-headed turtles (Mesoclemmys)
FIGURE 4. Comparison of the mean of the head width (HW), the carapace width (CW), and the plastron length (PL) relative to carapace length (CL) in four adults of Mesoclemmys jurutiensis and M. sabiniparaensis. The data was retrieved from table 4 in Cunha et al. (2021) and table 6 in Cunha et al. (2022). The percentile method was used to generate 95% confidence intervals around the mean (black circle) by bootstrapping 1000 times the observations (grey points). The means of the outgroups M. gibba (dotted horizontal line), M. raniceps (dashed horizontal line), and M. perplexa (dot-dashed horizontal line) were retrieved from table 7 in Cunha et al. (2022).
FIGURE 1 in Where to set the bar? Recent descriptions inflate species number in South American toad-headed turtles (Mesoclemmys)
FIGURE 1. (A) Maximum likelihood tree redrawn from Cunha et al. (2022); (B) maximum likelihood tree using a concatenated alignment (1261 bp; 675 bp COI, 586 bp 16S) combining sequences from Cunha et al. (2021, 2022); (C) topology of the corresponding clades redrawn from Thomson et al. (2021) derived from a Bayesian analysis of 15 nuclear loci. Taxa not represented in Cunha et al. (2021, 2022) are shown in grey. Nomenclature for genera follows TTWG (2021).
FIGURE 3. Maximum likelihood tree using a in Where to set the bar? Recent descriptions inflate species number in South American toad-headed turtles (Mesoclemmys)
FIGURE 3. Maximum likelihood tree using a COI alignment (675 bp) expanding the alignment from Cunha et al. (2021, 2022). Sequences used by Cunha et al. (2021, 2022) in bold. Codes preceding taxon names are GenBank accession numbers. Nomenclature for genera follows TTWG (2021).
FIGURE 2 in Where to set the bar? Recent descriptions inflate species number in South American toad-headed turtles (Mesoclemmys)
FIGURE 2. Maximum likelihood trees for the COI (675 bp) and 16S (586 bp) alignments corresponding to the datasets of Cunha et al. (2021, 2022). Codes preceding taxon names are GenBank accession numbers. Nomenclature for genera follows TTWG (2021).
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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.