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704 results for “Interference”
Population genomics and sexual signals identify reproductive interference in Uperoleia
<p>When closely related species come into contact via range expansion, both may experience reduced fitness as a result of the interaction. Selection is expected to favor traits that minimize costly interspecies reproductive interactions (such as mismating) via a phenomenon called reproductive character displacement (RCD). Research on RCD frequently assumes secondary contact between species, but the geographic history of species interactions is often unknown. Landscape genomic data allows tests of geographic hypotheses about species origins and secondary contact through range expansion. We used landscape genomic data from single nucleotide polymorphisms (SNPs), mitochondrial sequence data, advertisement call data, and morphological data to investigate a species complex of toadlets (<em>Uperoleia borealis, U. crassa, U. inundata</em>) from northern Australia. Although the three species of frogs were morphologically indistinguishable in our analysis, we determined that <em>U. crassa</em> and <em>U. inundata</em> form a single species (synonymized here) based on an absence of genomic divergence. SNP data identified the phylogeographic origin of <em>U. crassa </em>as the Top End, with subsequent westward invasion into the range of <em>U. borealis</em> in the Kimberley. We identified six F1 hybrids, all of which had the <em>U. borealis</em> mitochondrial haplotype, suggesting unidirectional hybridization. Consistent with the RCD hypothesis, <em>U. borealis</em> and <em>U. crassa</em> sexual signals differ more in sympatry than in allopatry. Hybrid males have intermediate calls, which likely reduces attractiveness to females. Integrating landscape genomic data, mitochondrial sequencing, morphology, and behavioral approaches supplies us an unusually detailed collection of evidence for reproductive character displacement following range expansion and secondary contact.</p>
Machine Learning Models and New Computational Tool for the Discovery of Insect Repellents that Interfere with Olfaction
<ul> <li><strong>SI1_Supporting Information</strong> file (docx) brings together detailed information on the outstanding models obtained for each dataset analyzed in this study such as statistical and training parameters and outliers. There can be found the responses in spikes/s of the mosquito <em>Culex quinquefasciatus </em>to the 50 IRs. Besides, there is presented a full table of the up-to-date studies related to QSAR and insect repellency.</li> <li><strong>SI2_EXP1_50IRs from Liu et al (2013)</strong> SDF file presents the structures of each of the 50 IRs analyzed.</li> <li><strong>SI3_EXP2_Datasets</strong> gathers the four datasets as SDF files from Oliferenko <em>et al.</em> (2013), Gaudin<em> et al. </em>(2008), Omolo <em>et al.</em> (2004), and Paluch <em>et al.</em> (2009) used for the repellency modeling in <strong>EXP2</strong>.</li> <li><strong>SI4_EXP3_Prospective analysis </strong>provides Malaria Box Library (400 compounds) as an SDF file, which were analyzed in our virtual screening to prospect potential virtual hits.</li> <li><strong>SI5_QuBiLS-MIDAS MDs lists</strong> contain three TXT lists of 3D molecular descriptors used in QuBiLS-MIDAS to describe the molecules used in the present study.</li> <li><strong>SI6_EXP1_Sensillar Modeling</strong> comprises two subfolders: Classification and Regression models for each of the six sensilla. Models built to predict the physiological interaction experimentally obtained from Liu <em>et al.</em> (2013). All of the models are implemented in the software SiLiS-PAPACS. Every single folder compiles a DOCX file with the detailed description of the model, an XLSX file with the output obtained from the training in Weka 3.9.4, an ARFF, and CSV files with the MDs for each molecule, and the SDF of the study dataset.</li> <li><strong>SI7_EXP2_Repellency Modeling </strong>encompasses the four datasets in the study: Oliferenko <em>et al.</em> (2013), Gaudin<em> et al. </em>(2008), Omolo <em>et al.</em> (2004), and Paluch <em>et al.</em> (2009). Inside the subfolders, there are three models per type of MDs (duplex, triple, generic, and mix) selected that best predict each dataset. As well as the SI6 folder, each model includes six files: DOCX, XLSX, ARFF, CSV, and an SDF.</li> <li><strong>SI8_Virtual Hits </strong>includes the cluster analysis results and physico-chemical properties of new IR virtual leads.</li> </ul>
AID: Open-Source Anechoic Interferer Dataset
<p>A dataset of anechoic recordings of various sound sources encountered in domestic environments is provided, which is intended to be a resource of non-stationary, environmental noise signals that, when convolved with acoustic impulse responses, can be used to simulate complex acoustic scenes.</p> <p>The dataset consists of anechoic recordings of 43 different types of sound sources encountered in domestic environments, with the number of individual recordings per sound source varying between two and eleven. The sound sources, which are mostly household devices and utilities, include door keys, plastic bags, clothing, a drilling machine, an electric blender, glass jars and metal boxes but also a few human-made sounds, such as clapping, breathing, snapping or whistling. The recordings cover a wide range of timbres. Multiple sounds from every individual source were recorded by different ways of excitation, such as hitting and shaking, or switching on and off the electric devices. Three different microphones were used to record the various sound sources.</p> <p>In addition, a <em>Python</em> library is provided that can be used to randomly arrange multiple anechoic noise recordings into a single channel interference signal. The number of individual recordings concurrently playing at any point in time in an interference signal can be specified by the user, providing control over the temporal density. The signal generator implementation is hosted on <a href="https://github.com/audiolabs/anechoic-noise">GitHub</a>.</p>
X-Ray Structures of Target-Ligand Complexes Containing Compounds with Assay Interference Potential
<p>A total of 2755 crystallographic complexes with ligands containing PAINS-defining substructures were extracted from the Protein Data Bank (PDB). PDB identifiers of these structures are made available together with the the corresponding PDB_PAINS (component identifier, aromatic nonstereo SMILES, PAINS class). </p>
Figure 7 in Interference and management of herbicide-resistant crop volunteers
Figure 7. Individual rows of weedy rice accessions or cultivated rice cultivars 8 d following a post-flood application of benzobicyclon at 371 g ai ha−1. Healthy rows are cultivated rice or resistant weedy rice accessions, whereas chlorotic rows are benzobicyclon-sensitive weedy rice accessions.
Figure 9 in Interference and management of herbicide-resistant crop volunteers
Figure 9. Field-scale evaluation of imidazolinone-resistant (ClearfieldṜ) wheat compared with non–herbicide resistant wheat (including volunteers the following year) in Saskatchewan, Canada, in the early 2000s (adapted from Beckie et al. 2011).
Figure 4 in Interference and management of herbicide-resistant crop volunteers
Figure 4. Symptoms of (A) glufosinate on glyphosate-resistant volunteer corn in glufosinate-resistant soybean and (B) sethoxydim on glyphosate/glufosinate-resistant volunteer corn in dicamba/glyphosate-resistant soybean.
Figure 2 in Interference and management of herbicide-resistant crop volunteers
Figure 2. Soybean after corn is a typical rotation in the midwestern United States. If not controlled, volunteer corn is a problem weed in soybean fields.
Figure 1 in Interference and management of herbicide-resistant crop volunteers
Figure 1. Glyphosate- and glufosinate-resistant canola volunteers in adjacent fields in Saskatchewan, Canada, due to bidirectional pollen-mediated gene flow the previous year.
Figure 3 in Interference and management of herbicide-resistant crop volunteers
Figure 3. Volunteer corn in a cornfield in Nebraska. Highly productive soils and easy access to irrigation have encouraged growers to adopt a corn-on-corn cropping system in south-central Nebraska that results in corn volunteers.
Figure 3 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 3. Relationships between yield components and Vulpia myuros density at two sowing times and crop densities in the growing seasons of 2017–2018 (A and C) and 2018– 2019 (B and D). Number of crop ears per square meter (A and B) and 1,000-kernel weight (C and D) data are shown with fitted curves. Data from the growing seasons of 2017–2018 and 2018–2019 were fit to the linear regression (Equation 2) and asymptotic nonlinear regression (Equation 3) models, respectively.
Figure 4 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 4. Relationships between the per-plant seed production and Vulpia myuros density at two crop densities solely at normal sowing time in the growing season of 2017–2018 (A) and at two sowing times and crop densities in 2018–2019 (B). Data from the growing seasons of 2017–2018 and 2018–2019 were fit to the linear regression (Equation 2) and asymptotic nonlinear regression (Equation 3) models, respectively.
Figure 2 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 2. Relationships between crop grain yield (kg ha−1) and Vulpia myuros density at two sowing times and crop densities in winter wheat in the growing seasons of 2017–2018 (A) and 2018–2019 (B). Data were fit to the rectangular hyperbola model (Equation 1).
Figure 1 in Rattail fescue (VulpiO myuros) interference and seed production as affected by sowing time and crop density in winter wheat
Figure 1. Cumulative emergence dynamics of Vulpia myuros at normal sowing time and late sowing time in relation to thermal time (C) in 2017–2018 (A) and 2018–2019 (B). Regression equation and parameter estimates described in Table 2.
Data and fitting script for "Direct measurement of a sin(2φ) current phase relation in a graphene superconducting quantum interference device"
<p>This repository contains data and Python analysis scripts used for the publication "Direct measurement of a sin(2\phi) current phase relation in a graphene superconducting quantum interference device (https://doi.org/10.48550/arXiv.2405.13642).</p> <p>The repository is organized as follows: the raw data are encapsulated in a QCoDes database (https://microsoft.github.io/Qcodes/) named 'D-SQUID-06.db'. Post-treated critical current data are included as .csv files and are indexed by measurement ids.</p> <p>The principal analysis is realized in the Jupyter notebook 'Fits_and_Figures.ipynb', which includes all article figures as well as fit functions for fitting both critical currents Ic- and Ic+ simulatenously, first using analytical expression from equation 3 then using numerical expression from equation 5. All fits mentionned in the article are performed there. The repository includes a generic notebook "Extract_Critical_Current.ipynb" used to explore the raw data in the Qcodes database and features an enhanced peak detection script that we used to automatically extract critical current data despite having some artifacts on raw differential conductance versus bias current and magnetic field.</p> <p>We acknowledge the contribution of R. Kerjouan for developing the Python fitting script.</p>
Fig. 6 in RNA interference mediated serine protease gene (Spbtry1) knockdown affects growth and mortality in the soybean pod borer (Lepidoptera: Olethreutidae)
Fig. 6. The mortality of the larvae feed on an artficial diet with added dsRNA. (*Student's t-test, n = 3, P <0.05; **Student's t-test, n = 3, P <0.01).
Fig. 4 in RNA interference mediated serine protease gene (Spbtry1) knockdown affects growth and mortality in the soybean pod borer (Lepidoptera: Olethreutidae)
Fig. 4. Relatve trypsin gene (Spbtry1) expression levels as determined by qPCR at different tme points. Actn was used as an internal reference gene. (*Student's t-test, n = 3, P <0.05; **Student's t-test, n = 3, P <0.01).
Fig. 5 in RNA interference mediated serine protease gene (Spbtry1) knockdown affects growth and mortality in the soybean pod borer (Lepidoptera: Olethreutidae)
Fig. 5. Effect of Spbtry1 RNAi on Leguminivora glycinivorella larval development. (A) The body weight of larvae fed on an artficial diet with added dsRNA at different tme points. (B) Pictures of the larvae showing reduced body size and developmental stage afer 15 days on an artficial diet with added dsRNA. (*Student's t-test, n = 3, P <0.05; **Student's t-test, n = 3, P <0.01).
Fig. 3. A in RNA interference mediated serine protease gene (Spbtry1) knockdown affects growth and mortality in the soybean pod borer (Lepidoptera: Olethreutidae)
Fig. 3. A) Relatve Spbtry1 gene expression levels was determined by qPCR (histograms) and RT-PCR (gel pictures) in the synganglion (SY), cutcle (CU), salivary (SA), midgut (MG), ovary (OV), tests (TE), and fat body (FT) in the 3rd instar soybean pod borer larvae. Actn was used as an internal reference gene. (B) Relatve trypsin gene (Spbtry1) expression levels as determined by qPCR (histograms) and RT-PCR (gel pictures) in soybean pod borer eggs (EG), 1st (N1), 2nd (N2), 3rd (N3), 4th (N4) instar larvae and pupae (PU), and adults (AD). Actn was used as an internal reference gene. Relatve Spbtry1 gene expression was analyzed by MJ Optcon Monitor Sofware Version 3.1.
Fig. 2 in RNA interference mediated serine protease gene (Spbtry1) knockdown affects growth and mortality in the soybean pod borer (Lepidoptera: Olethreutidae)
Fig. 2. Phylogenetc tree analysis of Spbtry1 and 13 homologues of other lepidopteran trypsin- and chymotrypsin-like serine proteases. The phylogenetc tree analysis was performed using the neighbor-joining algorithm to estmate evolutonary distances in MEGA 6.method at a gap penalty of 10, a gap length penalty of 0.2, and a bootstrap value of 1,000 iteratons.
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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.