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170 results for “novel interactions”
Fire-severity effects on plant-fungal interactions after a novel tundra wildfire disturbance: implications for arctic shrub and tree migration
Background-Vegetation change in high latitude tundra ecosystems is expected to accelerate due to increased wildfire activity. High-severity fires increase the availability of mineral soil seedbeds, which facilitates recruitment, yet fire also alters soil microbial composition, which could significantly impact seedling establishment. Results - We investigated the effects of fire severity on soil biota and associated effects on plant performance for two plant species predicted to expand into Arctic tundra. We inoculated seedlings in a growth chamber experiment with soils collected from the largest tundra fire recorded in the Arctic and used molecular tools to characterize root-associated fungal communities. Seedling biomass was significantly related to the composition of fungal inoculum. Biomass decreased as fire severity increased and the proportion of pathogenic fungi increased. Conclusions - Our results suggest that effects of fire severity on soil biota reduces seedling performance and thus we hypothesize that in certain ecological contexts fire-severity effects on plant-fungal interactions may dampen the expected increases in tree and shrub establishment after tundra fire.
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.
<p><b>fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions - supplementary data.</b></p><p>Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.</p>
Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.
<p>This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms </p> <p>We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references). </p> <p>This repository contains 3 different files:</p> <p>TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions</p> <p>TableS2_physical_priors.xlsx - contains three tabs:<br> ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) + ChIP-seq datasets (GSE28350, GSE81521) from (Hochbaum et. al, 2011, Li et. al, 2016).</p> <p>eY1HATAC- contains 3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016).</p> <p>motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1</p> <p>TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources)</p> <p>---</p> <p>Description of methodology to obtain regulatory interactions in TableS2:</p> <p>Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations.</p> <p>For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands.</p> <p>An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs.</p> <p>For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair.</p> <p>For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).</p>
Fig. 10 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 10. Comparisons of inflammatory cells recruited to inflammatory foci in cane toads, Rhinella marina (a) and native frogs, Cyclorana australis (b). Each anuran species was exposed to infective larvae of Rhabdias hylae (white bars) and Rhabdias pseudosphaerocephala (grey bars). Graphs show average values ± 1 S.E.M.
Fig. 7 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 7. Histological investigation of lungworm infection in anurans. Graphs show the proportion of (a) metamorph native frogs (Cyclorana australis) and (b) metamorph cane toads (Rhinella marina) infected with lungworms, not infected with lungworms, or with inflammatory 'foci' (probable cases of a lungworm larva penetrating the anuran's body but failing to survive).
Fig. 2 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 2. The distribution of lungworm larvae in cane toad metamorphs. (a) Toad metamorphs infected with Rhabdias hylae (native frog lungworm) and (b) toad metamorphs infected with Rhabdias pseudosphaerocephala (cane toad lungworm). Data in panel (b) are from Pizzatto et al. (2010), with permission. LUNG refers to adult lungworms found within the lung, SKIN/MUSCLE refers to larvae found in the skeletal muscle or subcutaneous tissue, HEAD refers to larvae detected in the head or neck region (excluding those found in eye tissue), EYE indicates larvae found in the eye or periocular tissue, and COELOM denotes larvae within the coelom or coelomic membranes.
Fig. 1. Histological image depicting a in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 1. Histological image depicting a transverse section of (a) R. hylae larva in the connective tissue of the head of a cane toad and (b) the inflammatory response composed primarily of macrophages and multinucleated giant cells surrounding the parasite. Haematoxylin and eosin stain, 400× magnification, scale bar equals 30 μm.
Fig. 4 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 4. Effect of time since exposure to Rhabdias hylae larvae on cane toad metamorphs: (a) shows the number of larvae found in toads and (b) shows the number of foci (areas of inflammation with no visible larvae) in toads, as determined by histological methods.
Fig. 9 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 9. Change in the average number of inflammatory foci (probable cases of larval parasites breaking down) observed in all anurans over time. Graph shows average values ± 1 S.E.M.
Fig. 6 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 6. Effects of Rhabdias hylae infection on cane toad metamorphs: (a) the average percentage of neutrophils and (b) lymphocytes around inflammation sites over time in cane toads infected with Rhabdias hylae. Graphs show average values ±1 S.E.M.
Fig. 3 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 3. The state of Rhabdias hylae larvae in cane toads as a function of days-post treatment. The graph shows larval numbers as the percentage of total larvae that were seen at each time period.
Fig. 5 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 5. Average inflammation severity surrounding Rhabdias hylae larvae and foci (probable larvae being broken down by the host's immune system) within infected cane toads at different numbers of days post-infection. Graph shows average values ±1 S.E.M.
Fig. 8 in Host-parasite interactions during a biological invasion: The fate of lungworms (Rhabdias spp.) inside native and novel anuran hosts
Fig. 8. Changes through time (days post-infection) on the relative numbers of anurans that were infected with lungworms, and that contained adult versus juvenile stages of the parasites involved. Data are shown for two lungworm species (Rhabdias hylae from native frogs, and Rhabdias pseudosphaerocephala from invasive cane toads) and for two types of host: the native frog, Cyclorana australis, and the cane toad, Rhinella marina. The panels show data for (a) C. australis infected with R. pseudosphaerocephala, (b) C. australis infected with R. hylae, (c) cane toads infected with R. pseudosphaerocephala and (d) cane toads infected with R. hylae.
Figure 11. Cognitive architecture of the process of social signals perception-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>A possible cognitive architecture and formalization of the process of learning via<br> multisensory integration is presented in figure 11. The formal description of the proposed cognitive<br> architecture, capable of interpreting social-communication signals, signs and symbols, is based on<br> multisensory integration at the level of perception, parallel processing at the level of interpretation<br> and decision making followed by verbalization, as well as performing an action (eye contact,<br> gesture, mimicking) at the level of behaviour.</p>
Figure 9. The impossible figure (right) is not noticeable as such at first glance-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>In the lexical domain a similar effect of holistic word processing is described in (Anstis,<br> 2005b). The viewers were presented with pairs of three-letter words in quick succession and asked<br> to report if the upper halves of the successively presented words were identical. Surprisingly, even<br> when the upper halves of the words were orthographically identical, the error rate was reliably<br> higher than expected and in comparison with matching identical successive words. As the author of<br> the study Stuart Anstis points out: “students were processing the words not as separable parts, but<br> holistically as perceptual units that could not be perceptually split apart. These results show that in<br> normal circumstances, the visual system cannot, or does not, divide words into upper and lower<br> halves” (Anstis, 2005b, p. 239).The author relates the results of his study to studies of visual<br> perception of faces as evidence that the mechanism of holistic processing in the visual and the<br> lexical domains is essentially the same.</p>
Figure 8. Machine faces, perceived as more figure-like(left) and less figure-like (right)-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>In an experimental study on visual perception, addressing directly Gestalt phenomena, a new<br> Gestalt cue for figure-ground assignment was introduced (Vechera et al., 2002). The foreground<br> versus the background organization is a strong determinant for decisions on objects seen among<br> image elements. A well-known set of perceptual cues that are often called Gestalt cues are the size<br> or area, the symmetry and the convexity vs. concavity judgments. It is generally assumed that<br> figures are ‘small, symmetrical and convex’. The authors asked the question whether these cues are<br> all that are necessary for a region of the image to be judged as a figure. The main result of this study<br> is that regions in the lower portion of a stimulus array appear more figure-like than regions in the<br> upper portion of the display.</p>
Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong<br> emotional response to the distorted face to the right in figure 6.</p>
Figure 5. The distortion is barely noticeable if the faces are viewed in the reversed position-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>According to the feature-based processing theories of human faces the main elements,<br> noticed and remembered in a face, are the eyes, the nose and the mouth (Thompson, 1980; Anstis,<br> 2005a). If, however, we distort some of the elements of a face, these should influence perception,regardless of the position of the image – upright or reversed – from the observer viewpoint. Figure 5<br> presents the reversed image of the face on the left and the reversed distorted face on the right. The<br> distortion was achieved by rotating the eyes of the image in the vertical direction.<br> Figure</p>
Figure 4. Main elements of a face, according to the feature-based processing theories-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>In 1980 Peter Thompson proposed a new experimental paradigm for investigation of<br> perception, called “face thatcherization” (also named “Thomson illusion”) (Thompson, 1980).<br> Imagine that the following face, depicted in figure 4, is a photo of the then UK Prime Minister<br> Margaret Thatcher.</p>
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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.