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1,488 results for “helping”
Data from: Does the risk of sperm competition help explain cooperation between reproductive competitors? A study in the ocellated wrasse (Symphodus ocellatus)
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Data from: Combining familiarity and landscape features helps break down the barriers between movements and home ranges in a non-territorial large herbivore
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Data from: A major shift in diversification rate helps explain macroevolutionary patterns in primate species diversity
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Odors of non‐predatory species help prey moderate their risk assessment
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Data from: The multiple roles of β–diversity help untangle community assembly processes affecting recovery of temperate rocky shores
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Complementary water and nutrient utilization of perianth structural units help maintain long floral lifespan in Dendrobium
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Data from: Can novel genetic analyses help to identify low-dispersal marine invasive species?
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Data from: With a little help from my friends – Physiological integration facilitates invasion of wetland grass Elymus athericus into flooded soils
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Data from: Food sharing in vampire bats: reciprocal help predicts donations more than relatedness or harassment
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Data from: Wound treatment and selective help in a termite-hunting ant.
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Data from: Shift in ecological strategy helps marginal populations of shepherd's purse (Capsella bursa-pastoris) to overcome a high genetic load
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A targeted phylogenetic approach helps explain New World functional diversity patterns of two eudicot lineages
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Comprehensive taxon sampling and vetted fossils help clarify the time tree of shorebirds (Aves, Charadriiformes)
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Can sensors help in reducing costs associated with cultivated meat production?
<p>Cell culture medium is the most significant cost driver for cultivated meat production. Optimizing bioreactors’ design and instrumentation, through additional monitoring features via sensors can help in decreasing these costs, and achieving maximum cell production capacity per unit medium volume. <br> In Realsense1 (RS1) project, we focus on developing and testing novel low-cost sensors in the lab-on-a-chip environment i.e. microfluidic bioreactors, which enable significant reduction in time and cost of bioprocessing development due to the high degree of control over process variables through controlled fluid behavior characterized by the laminar flow, diffusion mixing and rapid energy dissipation. <br> In the follow-up Realsense 2 project, we use mathematical modeling and computational fluid dynamics coupled to empirical data generated in RS1 to identify the most efficient configuration for integrating sensors into stirred-tank bioreactors.</p> <p>This webinar discusses both projects, presents current results on microbioreactor design and fabrication and specific developed sensors, as well as the next steps for further scale-up options.</p>
Data from: Helping decisions and kin recognition in long-tailed tits: is call similarity used to direct help towards kin?
<p>Most cooperative breeders live in discrete family groups, but in a minority, breeding populations comprise extended social networks of conspecifics that vary in relatedness. Selection for effective kin recognition may be expected for individuals in such kin neighbourhoods to maximise indirect fitness. Using a long-term social pedigree, molecular genetics, field observations and acoustic analyses, we examine how vocal similarity affects helping decisions in the long-tailed tit <i>Aegithalos caudatus</i>. Long-tailed tits are cooperative breeders in which help is typically redirected by males that have failed in their own breeding attempts towards the offspring of male relatives living within kin neighbourhoods. We identify a positive correlation between call similarity and kinship suggesting that vocal cues offer a plausible mechanism for kin discrimination. Furthermore, we show that failed breeders choose to help males with calls more similar to their own. However, although helpers fine-tune their provisioning rates according to how closely related they are to recipients, their effort was not correlated with their vocal similarity to helped breeders. We conclude that although vocalisations are an important part of the recognition system of long-tailed tits, discrimination is likely to be based on prior association and may involve a combination of vocal and non-vocal cues.</p>
Quantifying shell outline variability in extant and fossil Laqueus (Brachiopoda: Terebratulida): are outlines good proxies for long-looped brachidial morphology and can they help us characterize species?
<p>Extant and extinct terebratulide brachiopod species have been defined primarily on the basis of morphology. What is the fidelity of morphological species to biological species? And how can we test this fidelity with fossils? Taxonomically and phylogenetically, the most informative internal feature in the brachiopod suborder Terebratellidina is the geometrically complex long-looped brachidium, which, given their fragile nature, are not commonly preserved in the fossil record. In their absence, it is essential to test other sources of morphological data when trying to recognize and identify species. We analyzed valve outlines and brachidia in the genus <i>Laqueus</i> to explore the utility of shell shape in discriminating extant and fossil species. Using geometric morphometric methods, we quantified valve outline variability using elliptical Fourier methods and tested whether long-looped brachidial morphology correlates with shell outline shape. We then built classification models based on machine learning algorithms using outlines as shape variables to predict fossil species' identities. Our results demonstrate that valve outline shape is significantly correlated with long-looped brachidial shape and that even relatively simple outlines are sufficiently morphologically distinct to enable extant <i>Laqueus</i> species to be identified, validating current taxonomic assignments. These are encouraging results for the study and delimitation of fossil terebratulide species, and their recognition as biological species. In addition, machine learning algorithms can be successfully applied to help solve species recognition and delimitation problems in paleontology, especially when morphology can be characterized quantitatively and analyzed statistically.</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The aim of the project is to analyse the inventory and functioning of scientific terms and special lexemes in textbooks for secondary schools of the Russian Federation with the help of modern methods of natural language processing and deep learning. The number of terms from different fields of knowledge that a pupil should learn during secondary school studies has never been evaluated. According to the preliminary evaluations made on the basis of the Model Basic Curriculum for General and Secondary Education in 2015 only the subject "Russian language" presupposes that a pupil finishing the 11th grade of secondary school should be able to understand, recognise and use about 1000 terms and terminological combinations. Thus, taking into account the number of school subjects, the total number of special vocabulary units studied in general education schools is measured in thousands. At the same time, the comparative characteristics of the inventory and functioning of terms in textbooks for different school subjects are not studied and remain unknown. The correlation between the terminological density of the text in school textbooks for different subjects and the place occupied by these subjects in the curriculum is not clear. The traditional way of compiling lists of scientific terms is simply by gleaning them from special texts and writing down manually. If this method is reliable in terms of intellectualisation of selection principles, it cannot be applied to large data sets and does not reflect either the frequency of use of terms, or the specificity of their syntagmatic connections, or the systemic relationship between terms. The current project is aimed at filling this gap by means of 1) creating a full-text corpus of school textbooks for 5–11 classes included in the Federal List compiled by the Ministry of Education, 2) automatic extraction, stratification, and mapping of terms with the help of distribution semantics algorithms, 3) creation and training of a deep neural network capable of predicting the subject, level of education and educational topic given a group of vector representations of terms as input. The results of the research can be of fundamental interest in the perspective of terminology science development and also have practical applications in the creation of different types of educational literature.</p> <p><em>Funding: The reported study was funded by RFBR, project number 19-29-14032</em></p>
The smell of cooperation: rats increase helpful behaviour when receiving odour cues of a conspecific performing a cooperative task
<p>Reciprocity can explain cooperative behaviour among non-kin, where individuals help others depending on their experience in previous interactions. Norway rats <i>(Rattus norvegicus)</i> cooperate reciprocally according to direct and generalized reciprocity. In a sequence of four consecutive experiments, we show that odour cues from a cooperating conspecific are sufficient to induce altruistic help of rats in a food-exchange task. When rats were enabled to help a non-cooperative partner while receiving olfactory information from a rat helping a conspecific in a different room, they helped their non-cooperative partner as if it was a cooperative one. We further show that the cues inducing altruistic behaviour are released during the act of cooperation and do not depend on the identity of the cue provider. Remarkably, olfactory cues seem to be more important for cooperation decisions than experiencing a cooperative act per se. This suggests that rats signal their cooperation propensity to social partners, which increases their chances to receive help in return.</p>
Additional information for manuscript entiteld "Host-parasitoid associations in marine planktonic time series: can metabarcoding help reveal them?" (PONE-D-20-17825R1)
<p><strong>Description:</strong></p> <p>This repository contains material to reproduce metabarcoding analyses based on the q-zip pipeline (https://github.com/PyoneerO/qzip). Raw fastq files can be downloaded from https://www.ebi.ac.uk/ena/browser/view/PRJEB37135. The used reference file can be downloaded from https://github.com/pr2database/pr2database/releases/tag/4.11.1. Please select the files created for the classifier implemented in mothur.</p> <p>The dockerfile in this repository can be used to set up the environment which inludes the installation of the needed versions of the needed tools.</p> <p>Twelve different analyses had been conducted. For each analysis one zip file had been created which contains the following files:</p> <p>- q-zip_commands.sh: the shell script to launch the pipeline</p> <p>- q-zip_parameters.txt: pipeline parameter file as input of the shell script</p> <p>- q-zip_workflow.log: log file containing stdout and sdterr</p> <p>- q-zip_seq_of_coms.txt: file containing each command executed during the pipeline run (minimal set of command to reproduce the results)</p> <p>- seq_number_stats.txt: file containing the sequence numbers at each filtering step</p> <p>- OTU tables in tsv and biom format (sequences and taxonomic annotation included)</p> <p>- Meta data map (here only including the raw file names)</p> <p>- swarm sequences in fasta format</p> <p> </p> <p><strong>The following analyses had been conducted:</strong></p> <p>- otu formation at swarm distance 1; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 5; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 10; default settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; relaxt settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 2; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 3; strict settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p>- otu formation at swarm distance 1; very strict settings settings for preceding sequence filtering and subsequent taxonomic annotation</p> <p><strong>Settings into more detail:</strong></p> <p>relaxt settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 3 bp - threshold of average quality within of 5</li> <li>vsearch paired-end merging: length of minimum overlap of 25 bp - number of mismatches allowed of 5 bp</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 75% - percentage mismatches allowed of 20%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.6</li> </ul> <p>default settings (used for the manuscript):</p> <ul> <li>trimmomatic filtering: sliding window length of 3 bp - threshold of average quality within of 8</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 5</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 90% - percentage mismatches allowed of 10%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.25 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.8</li> </ul> <p>strict settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 1 bp - threshold of average quality within of 15</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 0</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 90% - percentage mismatches allowed of 10%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.9</li> </ul> <ul> </ul> <p>very strict settings:</p> <ul> <li>trimmomatic filtering: sliding window length of 1 bp - threshold of average quality within of 15</li> <li>vsearch paired-end merging: length of minimum overlap of 50 bp - number of mismatches allowed of 0</li> <li>cutadapt primer removal: percentage primer to sequence overlap of 100% - percentage mismatches allowed of 0%</li> <li>vsearch eeMax filtering: max number of errors expected per sequence of 0.1 bp</li> <li>minimum sequence length of 300 bp and maximum sequence length of 550 bp</li> <li>mothur classification cutoff (refers to confidence threshold of NBC) of 0.9</li> </ul>
Pollution control can help mitigate future climate change impacts on European grayling in the UK
<p><span><u>Aim</u></span></p> <p><span>We compare the performance of habitat suitability models using climate data only or climate data together with water chemistry, land cover and predation pressure data to model the distribution of European grayling (<i>Thymallus thymallus</i>). From these models, we (1) investigate the relationship between habitat suitability and genetic diversity; (2) project the distribution of grayling under future climate change and (3) model the effects of habitat mitigation on future distributions. </span></p> <p><span><u>Location</u></span></p> <p><span>United Kingdom</span></p> <p><span><u>Methods</u></span></p> <p><span>Maxent species distribution modelling was implemented using a Simple model (only climate parameters) or a Full model (climate, water chemistry, land-use and predation pressure parameters). Areas of high and low habitat suitability were designated. Associations between habitat suitability and genetic diversity for both neutral and adaptive markers were examined. Distribution under minimal and maximal future climate change scenarios was modelled for 2050, incorporating projections of future flow scenarios obtained from the Centre for Ecology and Hydrology. To examine potential mitigation effects within habitats, models were run with manipulation of orthophosphate, nitrite and copper concentrations. </span></p> <p><span><u>Results</u></span></p> <p><span>We mapped suitable habitat for grayling in the present and the future. The full model achieved substantially higher discriminative power than the Simple model. For low suitability habitat, higher levels of inbreeding were observed for adaptive, but not neutral loci. Future projections predict a significant contraction of highly suitable areas. Under habitat mitigation, modelling suggests that recovery of suitable habitat of up to 10% is possible.</span></p> <p><span><u>Main conclusions</u></span></p> <p><span>Extending the climate-only model improves estimates of habitat suitability. Significantly higher inbreeding coefficients were found at immune genes, but not neutral markers in low suitability habitat indicating a possible impact of environmental stress on evolutionary potential. The potential for habitat mitigation to alleviate distributional changes under future climate change is demonstrated and specific recommendations are made for habitat recovery on a regional basis.</span></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.