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13,864 results for “staging”
Adaptation to distinct habitats is maintained by contrasting selection at different life stages in sunflower ecotypes
<p><span>Conspecific populations living in adjacent but contrasting microenvironments represent excellent systems for studying natural selection. These systems are valuable because gene flow is expected to force genetic homogeneity except at loci experiencing divergent selection. A history of reciprocal transplant and common garden studies in such systems, and a growing number of genomic studies, have contributed to understanding how selection operates in natural populations. While selection can vary across different fitness components and life stages, few studies have investigated how this ultimately affects allele frequencies and the maintenance of divergence between populations. Here, we study two sunflower ecotypes in distinct, adjacent habitats by combining demographic models with genome-wide sequence data to estimate fitness and allele frequency change at multiple life stages. This framework allows us to estimate that only local ecotypes are likely to experience positive population growth</span> <span>(λ>1) and that the maintenance of divergent adaptation appears to be mediated via habitat- and life-stage-specific selection. We identify genetic variation, significantly driven by loci in chromosomal inversions, associated with different life history strategies in neighbouring ecotypes that optimize different fitness components and may contribute to the maintenance of distinct ecotypes. </span></p>
Fig. 7 in New data on the early stages and behaviour of the endangered species Callophrys mystaphia (Lepidoptera: Lycaenidae) and its first larval parasitoid, Cotesia sp. (Hymenoptera: Braconidae)
Fig. 7. Distribution map of Callophrys mystaphia Miller, 1913 (black circle: records based on scientific papers, empty circle: records of butterfly watchers) and Rheum ribes (square).
Fig. 2 in New data on the early stages and behaviour of the endangered species Callophrys mystaphia (Lepidoptera: Lycaenidae) and its first larval parasitoid, Cotesia sp. (Hymenoptera: Braconidae)
Fig. 2. Feeding larvae of Callophrys mystaphia Miller, 1913 on Rheum ribes host plant. (a–b – fully grown larvae, 20.vi.2020; c–d – final instar larvae approaching pupation, 4.vi.2020).
Fig. 6. a in New data on the early stages and behaviour of the endangered species Callophrys mystaphia (Lepidoptera: Lycaenidae) and its first larval parasitoid, Cotesia sp. (Hymenoptera: Braconidae)
Fig. 6. a – Parasitised larva of Callophrys mystaphia Miller, 1913; b–c – cocoons of the parasitoid; c – intentionally removed larva to reveal the cocoons, d–g – reared parasitoids of the genus Cotesia (Braconidae: Microgastrinae) from various perspectives.
Fig. 1 in New data on the early stages and behaviour of the endangered species Callophrys mystaphia (Lepidoptera: Lycaenidae) and its first larval parasitoid, Cotesia sp. (Hymenoptera: Braconidae)
Fig. 1. Habitus of Callophrys mystaphia Miller, 1913 (a – 29.iv.2020 during a field study, b–c – stretched specimen).
Fig. 4 in New data on the early stages and behaviour of the endangered species Callophrys mystaphia (Lepidoptera: Lycaenidae) and its first larval parasitoid, Cotesia sp. (Hymenoptera: Braconidae)
Fig. 4. Pupae of Callophrys mystaphia Miller, 1913 on 13.vi.2020 (a – dorsal, b – ventral, c – lateral view).
Fig. 8 in New data on the early stages and behaviour of the endangered species Callophrys mystaphia (Lepidoptera: Lycaenidae) and its first larval parasitoid, Cotesia sp. (Hymenoptera: Braconidae)
Fig. 8. Habitat of Callophrys mystaphia Miller, 1913 in Şirvan district, Siirt Prov., south-eastern Turkey, 1400 m a.s.l. (a – 1.vi.2020, b – 29.iv.2021).
Fig. 3 in Description of the first zoëal stage of Geograpsus crinipes (Dana, 1851) (Decapoda: Brachyura: Grapsidae) from the Red Sea
Fig. 3. Geograpsus crinipes (Dana, 1851), first zoëa: (A) lateral view and (B) dorsal view of abdomen and telson.
Datasets for article Effect of disease prevalence and growth stage on symptoms severity in the Turnip mosaic virus - Arabidopsis thaliana pathosystem
<p>Plants generate volatile organic compounds (VOCs) in response to biotic and abiotic stimuli that provide information about the physiological status of emitters to other individuals in the community. Nearby receivers adjust their own defenses in response to these chemical cues. The majority of studies to date has concentrated on the communication of abiotic stressors (<em>e.g</em>. salinity or drought) or herbivory. Less attention had received the role of VOCs during microbial infections and almost nothing has been done for viruses. Here we investigated the function of VOCs during turnip mosaic virus infection of <em>Arabidopsis thaliana</em>. First, we looked at the influence of two factors on the kinetics of symptoms progression in receivers, namely the prevalence of infection in the population and the growth stage of the receiver plants at inoculation. We found that young plants were more sensitive to the protective effect of VOCs than older ones, and that high infection prevalence results in a slower disease progression in receivers. Second, we looked into the possibility that jasmonates could be VOC candidates. To do this, we examined the kinetics of symptoms progression in jasmonate-insensitive and wild-type plants, and the results showed that the protective effect vanished in the mutant plants. Third, we investigated the possibility that root communication would be also relevant. Our findings showed that the kinetics of symptom progression across receivers was further slowed down when plants were housed in the same pot. Together, our preliminary findings point to a potential function for disease prevalence in plant communities in regulating the severity of symptoms, this effect being mediated by VOCs.</p>
Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage - DATASET
<p>This is the dataset associated with the paper</p> <p>Dui, L. G., Lomurno, E., Lunardini, F., Termine, C., Campi, A., Matteucci, M., & Ferrante, S. (2022). Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage. <em>Scientific Reports</em>, <em>12</em>(1), 21624.</p> <p>File content:</p> <ul> <li>characterizationSciRep22.xlsx: an Excel file <ul> <li>rows: subjects</li> <li>columns: <ul> <li>year of birth</li> <li>month of birth</li> <li>dominant hand (0=right, 1=left)</li> <li>sex (0=male, 1=female)</li> <li>mother tongue (0=non-Italian, 1=Italian)</li> </ul> </li> </ul> </li> <li>featureSciRep22: a Matlab file. For each sub-matrix, rows are subjects <ul> <li>"dataset" sub-matrix: each column is a feature computed from drawings</li> <li>"names_type" sub-matrix: metadata for the "dataset" matrix <ul> <li>column 1: feature name</li> <li>column 2: game the feature refers to</li> <li>column 3: feature type</li> </ul> </li> <li>"risk_questions": each column corresponds to the answer to one of the 15 items for assessing graphical abilities. 0=not selected, 1=selected</li> <li>"risk": the risk label computed from "risk_questions", 0=not at risk, 1= at risk</li> </ul> </li> </ul>
Dataset Graph 6: Stagings of Heiner Müller's Plays in Germany (East and West).
<p>Dataset for Graph 6. Data collected based on Ludwig, Janine. Heiner Müller, Ikone West: Das dramatische Werk Heiner Müllers in der Bundesrepublik; Rezeption und Wirkung. Frankfurt am Main: Lang, 2009, 53–4.</p> <p>Graph 6 shows the number of stagings of Heiner Müller’s plays in East and West Germany from 1957-1990.</p>
Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
<p>The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.</p>
Multi-stage sleep classification using photoplethysmographic sensor
<p>The conventional approach to monitoring sleep stages requires placing multiple sensors on the patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single sensor Photoplethysmographic (PPG) based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of ten patients. Data analysis was performed to obtain 82 features from the recordings, which were then classified against the sleep stages. The classification results using SVM with the polynomial kernel gave the overall accuracy of 84.66%, 79.62%, and 72.23% for two, three, and four-stage sleep classification. These results show that using only PPG; it is possible to conduct sleep stage monitoring. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.</p>
High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics
<p>Data to support the paper - <em>High-throughput crystallography for rapid early-stage fragment growth from crude arrays by low-cost robotics</em>. Data includes a summary of X-ray and LCMS results for the reactions executed on the OpenTrons, output reports and summaries from MSCheck (semi-automated LCMS analyzer tool) and the Python scripts used to execute single and multistep chemistry on the OpenTrons.</p> <p><strong>Abstract</strong></p> <p>We demonstrate that a simple workflow of array synthesis, combining low-cost robotics with analytic techniques to deconvolute crude reaction mixtures, is an effective way to collect structural data on a binding site. Starting from the high information content of the crystallographic fragment screens on PHIP(2) (second bromodomain of the pleckstrin homology domain interacting protein), a collection of more than 1800 compounds was enumerated. Several thousand <em>Crude Reaction Mixtures</em> (CRMs) were synthesized on one robotic platform, an OpenTrons OT-1 liquid handler, using reaction sequences of up to 5 chemical steps. Analysis via MScheck, an algorithm-based system for finding a m/z in a CRM, significantly shortened product identification protocol times. 957 usable X-ray diffraction datasets were acquired, which resolved as 22 reaction products binding to the protein, 19 with conserved poses relative to the original fragment and 3 with a new, unexpected binding pose. The 22 crystallographic hit compounds were subsequently tested with peptide displacement alpha-screen assay and time-resolved grating-coupled interferometry-based biosensor assays, which confirmed one molecule with an IC<sub>50</sub> = 34 μM and K<sub>D</sub> = 50 μM, from an inactive fragment. The procedures described are entirely formulaic and engineerable and the method is eminently scalable. We anticipate that this cheap, low solvent-use approach will yield vast amounts of data, enabling rapid SAR landscape exploration around fragments, leading to faster fragment to lead times.</p>
Figs 11, 12. A in Description of the immature stages and the adult female of Aulacigaster africana, the first known for the Afrotropical Aulacigastridae (Diptera: Schizophora)
Figs 11, 12. A. africana, third instar larva, cephalopharyngeal skeleton: (11) lateral view; (12) cranial half in a higher magnification. Scale bars: Fig. 11= 0.2 mm, Fig. 12 = 0.1 mm.
Figs 3–7. A in Description of the immature stages and the adult female of Aulacigaster africana, the first known for the Afrotropical Aulacigastridae (Diptera: Schizophora)
Figs 3–7. A. africana, egg and second instar larva: (3) egg, dorsal view; (4) egg, lateral view; (5) second instar larva, habitus, lateral view; (6) cephalopharyngeal skeleton of the 2nd instar larva; (7) an outline of the head and thorax of 2nd instar, lateral view. Scale bars: Fig. 5 = 1.0 mm, Figs 3, 4 = 0.4 mm, Fig. 7 = 0.2 mm, and Fig. 6 = 0.1 mm.
Figs 8–10. A in Description of the immature stages and the adult female of Aulacigaster africana, the first known for the Afrotropical Aulacigastridae (Diptera: Schizophora)
Figs 8–10. A. africana, third instar larva: (8) habitus, lateral view; (9) anterior spiracle; (10) left creeping welt on the 3rd abdominal segment, with a spine in lateral view. Scale bars: Fig. 8 = 1.0 mm, Fig. 10 = 0.4 mm, Fig. 9 = 0.2 mm.
Figs 1, 2. A in Description of the immature stages and the adult female of Aulacigaster africana, the first known for the Afrotropical Aulacigastridae (Diptera: Schizophora)
Figs 1, 2. A. africana, female: (1) terminalia, caudal view; (2) the unpaired spermatheca, lateral view. Scale bar: Fig. 1 = 0.2 mm, Fig. 2 = 0.1 mm.
Data for: Chronic exposure to odors at naturally occurring concentrations triggers limited plasticity in early stages of Drosophila olfactory processing
<p>In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly <em>Drosophila</em>, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is re-encountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. Here, we investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, we found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. We further investigated the DL5 olfactory coding channel and found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses, or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment.</p>
Figure 12 in DESCRIPTION OF THE LARVAE OF CRICOTOPUS FESTIVELLUS (KIEFFER 1906) AND CRICOTOPUS DIVERSUS (BOESEL 1983) WITH KEYS TO DISCRIMINATION OF LARVAL, PUPAL AND ADULT STAGES (DIPTERA: CHIRONOMIDAE) Abstract
Figure 12. Comparison of inferior volsellae of C. diversus (left) and C. festivellus (right). Same individuals as Fig. 11.
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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.