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3,648 results for “induction”
Data from: How symbiosis and ecological context influence the variable expression of transgenerational wing induction upon fungal infection of aphids
Aphids, like most animals, mount a diverse set of defenses against pathogens. For aphids, two of the best studied defenses are symbiont-conferred protection and transgenerational wing induction. Aphids can harbor bacterial symbionts that provide protection against pathogens, parasitoids and predators, as well as against other environmental stressors. In response to signals of danger, aphids also protect not themselves but their offspring by producing more winged than unwinged offspring as a way to ensure that their progeny may be able to escape deteriorating conditions. Such transgenerational wing induction has been studied most commonly as a response to overcrowding of host plants and presence of predators, but recent evidence suggests that pea aphids (Acyrthosiphon pisum) may also begin to produce a greater proportion of winged offspring when infected with fungal pathogens. Here, we explore this phenomenon further by asking how protective symbionts, pathogen dosage and environmental conditions influence this response. Overall, while we find some evidence that protective symbionts can modulate transgenerational wing induction in response to fungal pathogens, we observe that transgenerational wing induction in response to fungal infection is highly variable. That variability cannot be explained entirely by symbiont association, by pathogen load or by environmental stress, leaving the possibility that a complex interplay of genotypic and environmental factors may together influence this trait.
Figure 3 in Gall-induction by an Australian insect of the family Thripidae (Thysanoptera: Terebrantia)
Figure 3. Cyrilthrips cecidis adult female. (A) Habitus; (B) head and pronotum; (C) antenna; (D) mesonotum and metanota; (E) prosternum; (F) tergites IX–X; (G) hind coxae and abdominal sternites.
Figure 2 in Gall-induction by an Australian insect of the family Thripidae (Thysanoptera: Terebrantia)
Figure 2. Proportion of male Cyrilthrips within double-fold galls from time of induction over 21 weeks during late summer and autumn.
Figure 4 in Gall-induction by an Australian insect of the family Thripidae (Thysanoptera: Terebrantia)
Figure 4. Cyrilthrips cecidis. (A) Forewing; (B) scanning electron micrograph (SEM) of antennal segments II–III; (C) SEM of male tergite IX; (D) SEM of male tergite IX lateral view; (E) SEM of female hind coxae and sternites; (F) SEM of head dorsal view.
Figure 1 in Gall-induction by an Australian insect of the family Thripidae (Thysanoptera: Terebrantia)
Figure 1. (A) Austrosteenisia blackii seedlings showing pinnate leaves; (B) double-folded leaflet with overlapping marginal fold at right.
A peripheral immune signature of labor induction
<p>Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.</p>
Sperm induction in the social spider Anelosimus jabaquara Levi, 1956 (Theridiidae)
<p>Movie file to accompany Sperm induction in the social spider Anelosimus jabaquara Levi, 1956 (Theridiidae), Arachnology 18(9).</p>
Derived data for: "Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury's interior"
<p>Derived data for "Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury’s interior", accepted for publication in the Journal of Geophysical Research: Space Physics.</p>
Do genetic differences in growth thermal reaction norms maintain genetic variation in timing of diapause induction?
<ol> <li>An optimal timing for diapause induction through the sexual production of dormant propagules is expected in organisms with temporary populations. Yet, empirical studies often find high within-population genetic variation in the sexual production of such propagules, suggesting that this is a common feature of such organisms.</li> <li>Here, we hypothesize that genetic variation in the propensity to produce dormant propagules, <i>P<sub>d</sub></i>, is maintained by a genotype-by-environment interaction in clonal reproductive rates, where fast-growing genotypes within an environment should delay diapause relative to slow-growing genotypes. From this, we derive two predictions. First, if reaction norms of clonal reproduction cross between two environments, the genetic correlation of <i>P<sub>d</sub></i> between these environments should be negative. Second, the correlation between plasticity values of clonal reproduction and <i>P<sub>d</sub></i> should be negative.</li> <li>We tested these predictions by quantifying ephippia production in genotypes of a population of the facultative sexual cladoceran <i>Daphnia magna</i> at two temperatures. The population biomass at the onset of ephippia production was used as a measure of <i>P<sub>d</sub></i>, whereas juvenile somatic growth rate was used as a proxy for clonal reproductive rate. Plasticity for both measurements was derived from thermal reaction norms.</li> <li>Our results did not support either prediction, as neither the genetic correlation of <i>P<sub>d</sub></i> between environments, nor the correlation between plasticity values of growth and <i>P<sub>d</sub></i> were found to be significant.</li> <li>Our results suggest that genetic variation in the timing of diapause is not maintained by genetic differences in thermal clonal reproduction reaction norms. We propose as an alternative hypothesis that if there is across year variation in how stochastically the environment deteriorates, fluctuating selection may favor genotypes with different <i>P<sub>d</sub></i> between years.</li> </ol>
Supplementary data Quantitative elemental mapping of chondritic meteorites using laser ablation-inductively coupled plasma-time of flight-mass spectrometry (LA-ICP-TOF-MS)
<p>Supplementary data Quantitative elemental mapping of chondritic meteorites using laser ablation-inductively coupled plasma-time of flight-mass spectrometry (LA-ICP-TOF-MS)English</p>
Reproduction Package to "Resolving Power of Visible--to--Near-Infrared Hybrid beta-Ta/(Nb,Ti)N Kinetic Inductance Detectors"
<p>Reproduction package for paper: Resolving Power of Visible--to--Near-Infrared Hybrid beta-Ta/(Nb,Ti)N Kinetic Inductance Detectors. This package contains the necessary scripts and data to reproduce the data and figures in the paper. In addition, it contains a python based KID response model that can be used to test the coordinate transformations used in the paper for different use cases.</p>
Laser Ablation Inductively Coupled Plasma Mass Spectrometric Quantification of Isotope Trace Elements in Human Carcinoma Tissue - Stochastic Dynamics and Theoretical Analysis (SUPPORTING INFORMATION)
<p>Supporting information file of publication [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4334866]. </p>
Preparation of common bean explants for callus induction
<p>Preparation of common bean explants for callus induction published in the PhD dissertation named "Unraveling the raffinose family oligosaccharides metabolic pathway in common bean: From gene discovery to gene editing for improved nutrition"</p>
Micrographs of strains with MultiduBE before and after induction
<p><span>For the verification of MultiduBE-promoted fluorescent and morphology diversification, the crRNA array YKTmf5C-Sp4 was assembled into the mixture of pWLT-duBE-1a and pWLT-duBE-2b and then transformed into strain BSZRG-CmYKT. The results colonies were scraped from the plate and inoculated in a 50-mL sterile tube containing 10 mL LB media with kanamycin for pre-culture for about 10 h. Subsequently, the pre-culture was inoculated in a 14-mL sterile tube containing 2 mL LB-kanamycin media without aTC and a 14-mL sterile tube containing 2 mL LB-kanamycin media with 0.5 μM aTC, respectively. The two group cultures (without induction and with induction) were analyzed by Laser Scanning Confocal Microscopy (LSCM) and flow cytometry.</span></p>
Fig. 2 in Induction of promising antibacterial prenylated isoflavonoids from different subclasses by sequential elicitation of soybean
Fig. 2. Timeline of (H2O2 + AgNO3)-treatment with or without subsequent microbial elicitation of soybean seedlings. Microbial elicitation was performed with a live preparation of either a phytopathogenic fungus, Rhizopus spp. or a symbiotic bacterium, Bacillus subtilis. "Early" and "Late" refer to the time point of application of the (H2O2 + AgNO3)-treatment to 2d- and 4d-germinated seedlings, respectively.
Fig. 4 in Induction of promising antibacterial prenylated isoflavonoids from different subclasses by sequential elicitation of soybean
Fig. 4. Isoflavonoid content (μmol/g DW) of "early" and "late" (H2O2 + AgNO3)-elicited (A) and (H2O2 + AgNO3)-elicited prior to R- or B- elicited (B) soybean seedlings over five days. Isoflavonoids are classified into four main families (from top to bottom); ie. glycosylated isoflavonoids (white), non-prenylated aglycones (patterned), glyceollins (light grey) and prenylated isoflavones (dark grey). Phaseol contents (black) are also depicted for sequential elicitation treatments (B). Error bars indicate the standard deviation of three biological replicates. Quantification of the individual isoflavonoids over time per treatment can be found in Tables S2–S4. Statistical analysis (Tukey's test, p <0.05) of the over-time differences in each isoflavonoid subclass within the same treatment can be found in Table S5.
Fig. 3 in Induction of promising antibacterial prenylated isoflavonoids from different subclasses by sequential elicitation of soybean
Fig. 3. RP-UHPLC-PDA (280 nm) profiles of 96% (v/v) EtOH extracts of germinated (without any treatment application), ROS-primed and subsequently Rhizopus spp. (R)-elicited (Kalli et al., 2020), (H2O2 + AgNO3)-treated and (H2O2 + AgNO3)-treated and subsequently R-elicited soybean seedlings. Extracts correspond to 7d-old seedlings, where treatments (if any) were applied on the 4th day of germination. Peak numbers refer to compounds in Table S1.
Fig. 5 in Induction of promising antibacterial prenylated isoflavonoids from different subclasses by sequential elicitation of soybean
Fig. 5. Content (μmol/g DW) of prenylated isoflavonoids in (H2O2 + AgNO3)- elicited (and subsequently B. subtilis (B)- or Rhizopus spp. (R)- elicited) soybean seedlings in comparison to the recently proposed priming and elicitation treatment, ROS + R published by Kalli et al., 2020. Prenylated isoflavonoids were classified into (from right to left): C4-glyceollins (light grey), C2-glyceollins (light grey striped), prenylated isoflavones (grey), and phaseol (dark grey). Treatments are shown at their optimum day (4d for (H2O2 + AgNO3)-elicited and 3d for the sequentially elicited or ROS + R treatments) after "late" application with respect to maximum prenylated isoflavonoid accumulation. Error bars indicate the standard deviation of three biological replicates. Asterisks signify a statistically higher accumulation of a specific prenylated isoflavonoid subclass compared to the rest of the treatments (p <0.05).
Fig. 1 in Induction of promising antibacterial prenylated isoflavonoids from different subclasses by sequential elicitation of soybean
Fig. 1. Simplified biosynthetic pathway of the main prenylated isoflavonoids and their corresponding subclasses encountered in stressed soybeans (Glycine max). The prenyl group in its different configurations is highlighted in red. The different prenyltransferases involved in the biosynthesis of the two main subclasses of prenylated isoflavonoids (ie. glyceollins and prenylated isoflavones) are demonstrated. Both possible prenylation positions (C2 and C4) on the glycinol backbone for the synthesis of glyceollins are also depicted. Based on Suzuki et al., 2006, Yoneyama et al., 2016 and Dewick et al., 1970. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Raw data: Validation of a method for surveillance of nanoparticles in mussels using single particle inductively coupled plasma mass spectrometry
<p>The compressed archive contains SP-ICP-MS data to reproduce results for the paper with the working title "Validation of a method for surveillance of nanoparticles in mussels using single particle inductively coupled plasma mass spectrometry"".</p> <p>The remaining data to produce all other results, visualizations and statistics for the paper is included in the supplementary or GitHub. Associated code is deposited in a GitHub repository, github.com/arebruvold/mussel_validation .</p>
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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.