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3,937 results for “number”
Data from: Joint evolution of asexuality and queen number in an ant
Ants exhibit a striking diversity of reproductive systems, varying in traits such as the number of reproductives per colony [1], the mode of daughter production (sexual or asexual) [2], and the mode of caste determination (genetic or environmental) [3]. Species employing mixed reproductive systems present a unique opportunity to explore the causes and consequences of alternative breeding strategies. Mixed reproductive systems in ants include social polymorphism in colony queen number, whereby single-queen (monogyne) and multiple-queen (polygyne) colonies co-occur within species [4, 5, 6, 7], and facultative asexuality, in which female offspring may be produced sexually or asexually within colonies [8, 9, 10, 11, 12, 13]. Here, we document a remarkable confluence of multiple mixed reproductive systems in the tropical fire ant, Solenopsis geminata, in a population with three important features: (1) polygyne colonies produce workers sexually but queens asexually, whereas monogyne colonies produce both castes sexually; (2) polygyne queens mate with monogyne males to produce workers, but monogyne queens do not mate with polygyne males; and (3) different asexual/polygyne lineages evidently were founded separately by genetically distinct founder queens, which appear to have originated from the same neighboring monogyne population. Multiple asexual/polygyne genomes are transmitted undiluted in this system, but sterile workers produced with sperm from a sexually-reproducing/monogyne population are necessary for the persistence of these lineages. The intersection of social polymorphism, facultative asexuality, and genetic caste determination marks this population of S. geminata as an embodiment of the diversity of ant reproductive systems and suggests previously unknown connections between these phenomena.
A visualization of co-authorship network of mathematicians with an Erdős number of at most 2
<p>Co-authorship network of mathematicians with an Erdős number of at most 2</p> <p>Based on the data available at: https://oakland.edu/enp/thedata/erdos1.</p> <p>As can be seen, Erdős has collaborated directly or through an intermediary with different groups of scientists.<br> Researchers in the field of graph and computer science (gold), specialists in number theory (pink), mathematicians in the field of set theory (blue), researchers in the field of computer engineering (green) and the isolated group of Peter Salamon and his colleagues are the main groups of these researchers.<br> The names of researchers with more than 190 colleagues in the network (the first 10% of researchers) are written in the figure.</p> <p>See https://sites.google.com/oakland.edu/grossman/home/the-erdoes-number-project/the-erdoes-number-project-data-files.</p> <p>This artifact is a part of https://math-sci.ui.ac.ir/article_25684.html.</p>
Minimum number of experimental data for the thermal characterization of a hot water storage tank. Videos of temperature evolution
<p>Videos that show the evolution of the instantaneous temperature profile measured and determined with the models and in the cases developed in the paper "Minimum number of experimental data for the thermal characterization of a hot water storage tank" by the same authors. Thermocline thickness evolution is also shown.</p> <p>Video 1. Evolution of the instantaneous temperature profile estimated with the 5 MEL models during the charging period. Evolution of thermocline thickness during the same period is displayed in a separate frame also for all MEL models.<br> Video 2. Evolution of the instantaneous temperature profile estimated with the 5 MDH models during the charging period. Evolution of thermocline thickness during the same period is displayed in a separate frame also for all MDH models.</p>
Numbers of individuals and endemicity for birds in three study sites of the Himalayas-Hengduan mountains of China
<p>1. Describing the patterns and revealing the underlying mechanisms responsible for variations in community structure remains a central focus in ecology. However, important gaps remain, including our understanding of species abundance. Most studies on abundance-based relationships are from either temperate ecosystems or tropical ecosystems, and few have explicitly tested abundance-based relationships across a temperate to tropical ecotone.</p> <p>2. Here, we use a comprehensive dataset of breeding birds across elevation spanning a temperate to subtropical gradient in the Himalayas-Hengduan mountains of China to examine the relationship between species abundance and (1) elevational range size, (2) body size, (3) elevational range center and, (4) endemicity.</p> <p>3. We tested a priori predictions for abundance-elevational range size relationship, abundance-body size relationship, and abundance-elevational range center relationship and explored how these relationships change along this temperate to subtropical mountain ecosystem.</p> <p>4. We found that species abundance was significantly positively correlated with elevational range size across the study sites, demonstrating the key importance of elevational range size towards species abundance. Body size and elevational range center are weakly correlated with abundance. A novel finding of our study is that the abundance-elevational range size relationship gradually weakened from temperate to subtropical ecosystems, adding to a growing body of evidence suggesting that abundance-elevational range size tracks a temperate to tropical ecotone.</p> <p>5. Our study demonstrates that abundance range-size relationship can transition across ecotones where faunas of different evolutionary origins converge. Furthermore, measuring abundance relationships across different environmental variables at the same spatial scale with comparable biogeography is a key strategy that can reveal the underlying mechanisms behind abundance patterns.</p>
The first 500 numbers in the Fibonacci sequence
<p>This dataset consists of a single text file containing the first 500 numbers in the Fibonacci sequence, one per line. This is a sample dataset used to showcase the features of the <a href="https://github.com/rodluger/showyourwork">showyourwork</a> package.</p>
FIG. 8. — All field book pages that mention the collection number 401 in Saint-Hilaire virtual herbarium, a new upgradeable tool to study Brazilian botany
FIG. 8. — All field book pages that mention the collection number 401.
Fig. 3. The contact region. Numbers designate collecting sites. Compare with figure 49 in Hybridization Among Western Whiptail Lizards (Cnemidophorus Tigris) In Southwestern New Mexico: Population Genetics, Morphology, And Ecology In Three Contact Zones
Fig. 3. The contact region. Numbers designate collecting sites. Compare with figure 49.
FIGURE 2 in Egg Number Varies With Population Density; A Study Of Three Oribatid Mite Species In Orchard Habitats In Egypt
FIGURE 2:: Average population densities of the three studied species as a function of sampling time. Means are given with their standard errors. For each vegetation type, means were taken over three different sites (cf. Fig. 1) and three replicate samples within a site.
FIGURE 1 in Egg Number Varies With Population Density; A Study Of Three Oribatid Mite Species In Orchard Habitats In Egypt
FIGURE 1: Map of Al-Gharbia Governate in Egypt, indicating the three sampling areas (Tanta, Al Mahalla Al Kobra and Kafr Al Zayat). The inset shows the location of Al-Gharbia in Egypt. In each area, three orchards were sampled four times in a year. The same three types of orchard were selected in each area. The distance between locations varies from 19 to 38 km; the distance between sites within one location is 1-2 km.
FIGURE 3 in Egg Number Varies With Population Density; A Study Of Three Oribatid Mite Species In Orchard Habitats In Egypt
FIGURE 3: Relationship between population density and fecundity of the three studied species. The data (pooled over three samples per site) are for all sites, vegetation types and seasons together.
Is a number of strongly magnetised OB stars enough to produce all known magnetars?
<p>According to stellar evolution models, massive OB stars form neutron stars after the end of their life. But does the initial magnetic field of a neutron star depend on the magnetic field of a predecessor star? Some pulsar population synthesis (e.g. Gullón et al. 2015) which included thermally emitting neutron stars showed that magnetars (and for example the group of young cooling neutron stars called "the Magnificent Seven") could originate from a peculiar subpopulation of massive stars. Moreover, we can see that massive stars also can be divided into two groups: magnetic stars with average magnetic fields from several hundreds of Gauss to tens of kilogauss (only 5-7% of all) and nonmagnetic or weakly magnetic stars whose magnetic fields can not yet be measured accurately. Therefore, we want to check the hypothesis if magnetic OB stars in the Milky Way could be progenitors of magnetars. We also aim to explain how the magnetic fields depend on the stellar radii, binarity, and other parameters using modern observational data for various values (NARVAL measurements, Magnetar Outburst Online Catalog, Gaia DR2, etc). To do so, we study the distribution of magnetic fields of massive stars using the maximum likelihood technique and run a new pulsar population synthesis code. As a result of our analysis, we can claim that normal pulsars seem to be plausible descendants of the weakly magnetic OB stars and the precursors of magnetars are magnetic OB stars.</p>
Amaranthus palmeri EPSPS copy number and glyphosate resistance variation
<p>Gene copy number variation (CNV) has been increasingly associated with organismal responses to environmental stress, but we know little about the quantitative relation between CNV and phenotypic variation. In this study we quantify the relation between variation in <i>EPSPS</i> (5-enolpyruvylshikimate-3-phosphate synthase) copy number using digital drop PCR and variation in phenotypic glyphosate resistance in 22 populations of <i>Amaranthus palmeri</i> (Palmer Amaranth), a range-expanding agricultural weed. Overall, we detected a significant positive relation between population mean copy number and resistance. The majority of populations exhibited high glyphosate resistance yet maintained low-resistance individuals, resulting in bimodality in many populations. We also investigated threshold models for the relation between copy number and resistance, and found evidence for a threshold of ~15 <i>EPSPS</i> copies: there was a steep increase in resistance below the threshold, followed by a much shallower increase. Across 924 individuals, as copy number increases the range of variation in resistance decreases, yielding an increasing frequency of high phenotypic resistance individuals. Among populations <span>we detected a decline in variation (s.d.) as mean phenotypic resistance increased from moderate to high, consistent with the prediction that as phenotypic resistance increases in populations, stabilizing selection decreases variation in the trait. Our study demonstrates that populations of <i>A. palmeri</i> can harbour wide variation in <i>EPSPS</i> copy number and phenotypic glyphosate resistance, reflecting the history of, and template for future, resistance evolution.</span></p>
Galactic cosmic ray fluxes for a number of nearby Sun-like stellar systems
<p>This data set corresponds to the simulation data presented in the article "Charting nearby stellar systems: The intensity of Galactic cosmic rays for a sample of solar-type stars" (Rodgers-Lee, Vidotto & Mesquita, MNRAS, 2021). Details of the stellar wind and cosmic ray model used for the simulations are available in the article.</p> <p>The data consists of the differential intensity of Galactic cosmic rays as a function of cosmic ray kinetic energy at different orbital distances from the star. The data is given for a number of different stellar systems. The first line gives the column headings.</p> <p>File names are formatted as "gcr_data_XXX.dat" where "XXX" corresponds to the name of the star considered. The simulation details can be found in Tables 1 and 2 of the article.</p>
Data from: On the function of flower number: disentangling fertility from pollinator-mediated selection
<p>FLODIS is an experimental research project which aims at understanding how artificially increased flower number (i.e. enhanced floral display) impacts male and female reproductive/mating success as well as sex-specific pattern of selection on floral traits, in the dioecious insect-pollinated species <em>Silene dioica</em>.</p>
Power spectrum and reduced bispectrum of halo number-count mock catalogs
<p>Power spectrum (left) and reduced bispectrum (right) computed from sets of halo number-count mock catalogs (of 80 realizations each ) obtained from the calibration of BAM using the TkWEB model as described in . First rows show the mean in each case. The second row shows the ratio of to the reference (RTR) and the third row shows the variance in the respective statistics. The shaded area in the second rows denote the $5\%$ deviation with respect to unity.</p>
Data for: Long-term clade-wide shifts in trilobite segment number and allocation during the Palaeozoic
<p>Arthropods are characterized by having an exoskeleton, paired jointed appendages, and segmented body. The number and shape of those segments varies dramatically and unravelling the evolution of segmentation is fundamental to our understanding of arthropod diversification. Because trilobites added segments to the body post-hatching which were expressed and preserved in biomineralized exoskeletal sclerites, their fossil record provides an excellent system for understanding the early evolution of segmentation in arthropods. Over the last 200 years, palaeontologists have hypothesized trends in segment number and allocation in the trilobite body, but they have never been rigorously tested. We tabulated the number of segments in the post-cephalic body for over 1500 species, selected to maximize taxonomic, geographic, and temporal representation. Analysis reveals long-term shifts in segment number and allocation over the 250-million-year evolutionary history of the clade. For most of the Palaeozoic, the median number of segments in the body did not change significantly. Instead, the total range decreased over time and there was long-term increase in the proportion of segments allocated to the fused terminal sclerite relative to the articulated thoracic region. There was also increased conservation of thoracic segment number within families. Neither taxonomic turnover nor trends in functionally-relevant defensive behaviour sufficiently explain these patterns.</p>
Reducing the number of accepted species in Aspergillus series Nigri
<p>The <em>Aspergillus</em> series <em>Nigri</em> contains biotechnologically and medically important species. They can produce hazardous mycotoxins, which is relevant due to the frequent occurrence of these species on foodstuffs and in the indoor environment. The taxonomy of the series has undergone numerous rearrangements, and currently, there are 14 species accepted in the series, most of which are considered cryptic. Species-level identifications are, however, problematic or impossible for many isolates even when using DNA sequencing or MALDI-TOF mass spectrometry, indicating a possible problem in the definition of species limits or the presence of undescribed species diversity. To re-examine the species boundaries, we collected DNA sequences from three phylogenetic markers (<em>benA</em>, <em>CaM</em> and <em>RPB2</em>) for 276 strains from series <em>Nigri</em> and generated 18 new whole-genome sequences. With the three-gene dataset, we employed phylogenetic methods based on the multispecies coalescence model, including four single-locus methods (GMYC, bGMYC, PTP and bPTP) and one multilocus method (STACEY). From a total of 15 methods and their various settings, 11 supported the recognition of only three species corresponding to the three main phylogenetic lineages: <em>A</em>. <em>niger</em>, <em>A</em>. <em>tubingensis</em> and <em>A</em>. <em>brasiliensis</em>. Similarly, recognition of these three species was supported by the GCPSR approach (Genealogical Concordance Phylogenetic Species Recognition) and analysis in DELINEATE software. We also showed that the phylogeny based on <em>benA</em>, <em>CaM</em> and <em>RPB2</em> is suboptimal and displays significant differences from a phylogeny constructed using 5 752 single-copy orthologous proteins; therefore, the results of the delimitation methods may be subject to a higher than usual level of uncertainty. To overcome this, we randomly selected 200 genes from these genomes and performed ten independent STACEY analyses, each with 20 genes. All analyses supported the recognition of only one species in the <em>A</em>. <em>niger</em> and <em>A</em>. <em>brasiliensis</em> lineages, while one to four species were inconsistently delimited in the <em>A</em>. <em>tubingensis</em> lineage. After considering all of these results and their practical implications, we propose that the revised series <em>Nigri</em> includes six species: <em>A</em>. <em>brasiliensis</em>, <em>A</em>. <em>eucalypticola</em>, <em>A</em>. <em>luchuensis</em> (syn. <em>A</em>. <em>piperis</em>), <em>A</em>. <em>niger</em> (syn. <em>A</em>. <em>vinaceus</em> and <em>A</em>. <em>welwitschiae</em>), <em>A</em>. <em>tubingensis</em> (syn. <em>A</em>. <em>chiangmaiensis</em>, <em>A</em>. <em>costaricensis</em>, <em>A</em>. <em>neoniger</em> and <em>A</em>. <em>pseudopiperis</em>) and <em>A</em>. <em>vadensis</em>. We also showed that the intraspecific genetic variability in the redefined <em>A</em>. <em>niger</em> and <em>A</em>. <em>tubingensis</em> does not deviate from that commonly found in other aspergilli. We supplemented the study with a list of accepted species, synonyms and unresolved names, some of which may threaten the stability of the current taxonomy.</p>
Reproductive transcriptome of Nicotiana tabacum male gametophyte unveils decreasing number of new transcription factors during pollen ontogeny
<p>Plants with highly reduced male gametophytes represent a successful adaptation to sexual reproduction, which plays an important role in the colonization and radiation of terrestrial ecosystems. During pollen maturation, microsporocytes to mature pollen grain cells switch from mitosis to meiosis and ultimately form a haploid male gametophyte, a widely used model to study plant development. We performed RNASeq analysis of <em>Nicotiana tabacum</em> at six developmental stages from microspores to mature pollen grain to characterize in detail key transcription factor (TF) genes involved in pollen ontogeny. Our results provide the most complete transcriptomic data during pollen development in the important model plant <em>Nicotiana tabacum. </em>We have identified DEGs associated with six ontogenetic stages of the male gametophyte, providing insights into the molecular regulation of reproductive development by TFs that have a high potential for agronomic research investigating molecular networks associated with pollen sterility and related issues.</p>
Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography</em>”</strong> in Optics Express (doi.org/10.1364/OE.474279<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 10 minutes and 2D flow profile analysis may take up to one hour.</p> <p>For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, <em>Q</em>. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement.</p> <p>For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, <em>Q</em>. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Usability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 15-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 22-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 08-07-2022.zip</p> </td> <td> <p>2D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>All measurements</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>Processing.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines particle size from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Shape.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines axial beam shape from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Doppler Angle.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow Profile.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Number Density.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines particle number density raw OCT spectra.</p> </td> </tr> <tr> <td> <p>2D Flow Profile.py</p> </td> <td> <p>2D measurements</p> </td> <td> <p>This script determines 2D flow profiles from raw OCT spectra.</p> </td> </tr> </tbody> </table> <p> </p>
Data from: Higher evolutionary dynamics of gene copy number for Drosophila glue genes located near short repeat sequences
<p><strong>Background</strong></p> <p>During evolution, genes can experience duplications, losses, inversions and gene conversions. Why certain genes are more dynamic than others is poorly understood. Here we examine how several <em>Sgs</em> genes encoding glue proteins, which make up a bioadhesive that sticks the animal during metamorphosis, have evolved in <em>Drosophila</em> species.</p> <p><strong>Results</strong></p> <p>We examined high-quality genome assemblies of 24 <em>Drosophila</em> species to study the evolutionary dynamics of four glue genes that are present in <em>D. melanogaster</em> and are part of the same gene family <em>–</em> <em>Sgs1, Sgs3, Sgs7 and Sgs8 –</em> across approximately 30 millions of years. We annotated a total of 102 <em>Sgs</em> genes and grouped them into 4 subfamilies. We present here a new nomenclature for these <em>Sgs</em> genes based on protein sequence conservation, genomic location and presence/absence of internal repeats. Two types of glue genes were uncovered. The first category (<em>Sgs1, Sgs3x, Sgs3e</em>) showed a few gene losses but no duplication, no local inversion and no gene conversion. The second group (<em>Sgs3b, Sgs7, Sgs8</em>) exhibited multiple events of gene losses, gene duplications, local inversions and gene conversions. Our data suggest that the presence of short "new glue" genes near the genes of the latter group may have accelerated their dynamics.</p> <p><strong>Conclusions</strong></p> <p>Our comparative analysis suggests that the evolutionary dynamics of glue genes is influenced by genomic context. Our molecular, phylogenetic and comparative analysis of the four glue genes <em>Sgs1, Sgs3, Sgs7</em> and <em>Sgs8 </em>provides the foundation for investigating the role of the various glue genes during <em>Drosophila</em> life.</p>
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.