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10,767 results for “Contribution”
Phenotypic plasticity contributes more to the variations in nutrient resorption than genetic differentiation in a grassland dominant
<p class="MsoNormal"><span>1. P</span><span>henotypic plasticity and genetic differentiation are the two important processes determining the leaf nutrient resorption among and within plant species, which is critical for understanding the adaptability of plants</span><span>.</span><span> However, relative contributions of <span>these two processes</span> have never been quantified at a large geographical scale. </span></p> <p class="MsoNormal"><span>2. Here, we investigated intraspecific variations in nutrient resorption among 14 <em>Stipa breviflora</em> populations along a latitude gradient in 2018 and 2019. Furthermore, we sow seeds from these populations in two common gardens at different latitudes, and</span><span> </span><span>examined the variations in nutrient resorption. </span></p> <p class="MsoNormal"><span>3. Our results showed that nitrogen and phosphorus resorption efficiency (NRE and PRE)<span> among </span><em>S. breviflora </em>populations<span> </span><span>in nature were positively related to latitude, while this trend disappeared in the common gardens. </span>The heritability of <span>NRE and PRE was 11.45 % and 16.78 %, respectively. These results suggested that</span> phenotypic plasticity contributed much more than genetic variation to nutrient resorption of <em>S. breviflora</em>.<span> </span>Moreover, <span>the structural equation modeling (SEM) suggested that latitude indirectly affected </span>nutrient resorption mainly by altering soil nutrients. With the increasing of latitude, soil nutrients decreased while nutrient resorption increased<span>. This suggested</span> <a name="_Hlk78556987"></a><span>the main process regulating nutrient resorption is negative feedback to soil nutrient availability. </span></span></p> <p class="MsoNormal"><span>4. </span><span>Our study provides new insights into the role of nutrient resorption in plant adaptations to geographic variations.</span></p>
Model and observations used for Sigmond, M. et al, 'Large contribution of ozone-depleting substances to global and Arctic warming in the late 20th century", Geophysical Research Letters
<p>This dataset contains all the data used for the study "Large contribution of ozone-depleting substances to global and Arctic warming in the late 20th Century" by Sigmond et al, which was submitted to Geophysical Research Letters on July 26, 2022. It contains the following directories:</p> <p>- AR6: csv data files containing timeseries of Effective Radiative Forcing (ERF) as assessed by the IPCC Sixth Assessment Report (AR6).</p> <p>- CanESM5/DATA_siconc_canesm5_past_[expname] contain timeseries of September Sea Ice Extent (SSIE), where expname=historical (all forcing) or hist-noXXX (as historical, except that XXX is fixed to 1955 values)</p> <p>- CanESM5/DATA_tas_canesm5_past_[expname] contains timeseries of surface air temperature (am_: annual mean, arcam_: Arctic and annual mean, gmam_: Global and annual mean). expname as above </p> <p>- CanESM5/IN_TOA_COUPLED_CanESM5 contain timeseries of the global mean top of atmosphere net raditative flux. his=historical (all forcing) run, rms0056=fixed CFC run, rms0058=fixed CFC+stratospheric ozone run, rms0065: fixed CO2 run, and rms0066=fixed aerosol run.</p> <p>- CanESM5_uncoupled/DATA_rtmt_canesm5_YYYY: contains timeseries of RTMT (top of atmosphere radiative imbalance) in the uncoupled (fixed 1955 SST/SI) CanESM5 simulations. YYYY=fall: historical, faer: fixed aerosols, fco2: fixed CO2, fods: fixed CFC runs</p> <p>- OBSERVATIONS: contains timeseries of observational September Sea Ice Extent and surface air temperature used in the study</p> <p>- data_GSAT+ASAT+ERF+efficacies_2001-2005min1955.txt: contains the most import post-processed data </p> <p>Python code used to create these files from the raw model output are available at https://gitlab.com/michael.sigmond/cfc/</p>
Contribution of wheat and maize to soil organic carbon in a wheat-maize cropping system: a field and laboratory study
<p><span>Retention of crop biomass is widely recommended to improve soil organic carbon (SOC). However, the magnitude of contribution of aboveground residues and belowground roots from C3 and C4 crops to SOC is unclear. </span></p> <p><span>Data from a 10-year field experiment and a 60-day laboratory incubation were synthesized to identify the respective contribution of C3 (e.g., wheat) and C4 (e.g., maize) residues and roots to SOC, as well as its underlying mechanisms under no-till (NT) using <sup>13</sup>C labelling trace in wheat-maize rotations. </span></p> <p><span>The field experiment showed that residue retention significantly increased SOC accumulation, and SOC derived from wheat was 126.0% higher than that from maize. Conversion to NT promoted SOC derived from wheat and thus accumulated 17.6% higher SOC stock compared with plow tillage (PT) under residue returning at 0-20 cm soil depth (P<0.05). The data from laboratory incubation revealed the mechanisms that lower priming effects at 0-10 cm depth decreased total mineralization by 91.8% after inputs of wheat residues and roots compared with that of maize residues and roots, especially under NT compared with PT. Priming effects were negatively correlated with enzyme activities associated with the C recycle, SOC, and total nitrogen (TN) contents (P<0.01). NT increased enzyme activities, SOC, and TN contents and thus reduced priming effects and improved residual C. </span></p> <p><em><span>Synthesis and applications.</span></em><span> These results suggested that wheat may contribute more to SOC accumulation than maize, and carbon increment efficiency in farmland could be enhanced by considering the crucial roles of C3 crops in SOC accumulation. NT practice sustains the benefits of C3 crops to SOC sequestration</span> <span>in the upper soil depths.</span></p>
FIG. 1 in Contribution à la connaissance des Tetrigidae (Orthoptera) de Guyane
FIG. 1. — Localisation des spécimens observés en 2019. Sources: Wikimedia Maps.
Evidence of an additional center of apple domestication in Iran, with contributions from the Caucasian crab apple <i>Malus orientalis</I> Uglitzk. to the cultivated apple gene pool
<p>Abstract</p> <p>Divergence processes in crop-wild fruit tree complexes in pivotal regions for plant domestication such as the Caucasus and Iran remain little studied. We investigated anthropogenic and natural divergence processes in apples in these regionsusing 26 microsatellite markers amplified in 550 wild and cultivated samples. We found two genetically distinct cultivated populations in Iran that are differentiated from <em>Malus domestica</em>, the standard cultivated apple worldwide. Coalescent-based inferences showed that these two cultivated populations originated from specific domestication events of <em>Malus orientalis</em> in Iran. We found evidence of substantial wild-crop and crop-crop gene flow in the Caucasus and Iran, as has been described in apple in Europe. In addition, we identified seven genetically differentiated populations of wild apple (<em>M. orientalis</em>), not introgressed by the cultivated apple. Niche modeling combined with genetic diversity estimates indicated that these wild populations likely resulted from range changes during past glaciations. This study identifies Iran as a key region in the domestication of apple and <em>M. orientalis</em> as an additional contributor to the cultivated apple gene pool. Domestication of the apple tree therefore involved multiple origins of domestication in different geographic locations and substantial crop-wild hybridization, as found in other fruit trees. This study also highlights the impact of climate change on the natural divergence of a wild fruit tree and provides a starting point for apple conservation and breeding programs in the Caucasus and Iran.</p> <p>Methods</p> <p>Microsatellite genotyping data for <em>M. orientalis</em>, <em>M. sieversii</em> from Kazakhstan, <em>M. domestica</em> and <em>M. baccata</em> were from previously published studies, including: 207 <em>M. orientalis</em> individuals from Turkey, Armenia and Russia (23 sites, Tables S1 and S2, (Cornille et al., 2013; Cornille et al., 2012)), four apple cultivars from Armenia, 40 “pure” European cultivated <em>M. domestica</em> individuals (<em>i.e.</em>, not introgressed by <em>M. sylvestris</em>) (Tables S1 and S2, (Cornille et al., 2013, 2012)), 20 <em>M. sieversii</em> individuals from Kazakhstan (Cornille et al., 2013) and 22 <em>M. baccata</em> individuals from Russia (Cornille et al., 2012). We collected 257 new samples in 2017 in Iran and in 2018 in Kyrgyzstan (<em>M. sieversii</em>) for this study: 167 <em>M. orientalis</em> individuals from the Hyrcanian Forests and the Zagros region in Iran (Table S1), 48 local Iranian apple cultivars from the <em>Seed and Plant Improvement Institute</em> (Karaj, Iran) (Table S1) and 42 <em>M. sieversii</em> individuals from Kyrgyzstan. Note that for 18 of the 48 local Iranian samples, fruit size was measured (Table S1). Collections meet the requirements of the recently enacted Nagoya protocol on access to genetic resources and the fair and equitable sharing of benefits. Thus, a total of 550 individuals were analyzed, comprising 374 <em>M. orientalis</em>, 48 Iranian and four Armenian apple cultivars, 40 European apple cultivars belonging to <em>M. domestica, </em>62<em> M. sieversii </em>(from Kyrgyzstan and Kazakhstan) and 22<em> M. baccata</em> individuals (details are provided in Table S1).</p> <p>DNA from the new samples (<em>N </em>= 257) was extracted from dried leaves with the NucleoSpin plant II DNA extraction kit (Macherey & Nagel, Düren, Germanyâ) following the manufacturer’s instructions. Multiplex microsatellite PCR amplifications were performed with a multiplex PCR kit (Qiagen Inc.â) for 26 microsatellite markers as previously described (Cornille et al., 2012; Patocchi, Frei, Frey, & Kellerhals, 2009). Note that on each DNA plate, we included three controls, <em>i.e.,</em> one sample of <em>M. orientalis, </em>one of <em>M. sieversii</em> and one of <em>M. domestica</em> for which data were already available (Cornille et al., 2013). Genotypes of the controls were compared with the 2013 dataset. We retained only multilocus genotypes for which < 20 % of the data was missing. The suitability of these markers for population genetics analyses has been demonstrated in previous studies (Cornille et al., 2013; Cornille, Gladieux, & Giraud, 2013; Cornille et al., 2012). </p> <p>Software used</p> <p>-STRUCTURE <a href="https://web.stanford.edu/group/pritchardlab/structure.html">https://web.stanford.edu/group/pritchardlab/structure.html</a></p> <p>-ABCtoolbox: <a href="http://cmpg.unibe.ch/software/ABCtoolbox/">http://cmpg.unibe.ch/software/ABCtoolbox/</a></p> <p>-fastsimcoal2 <a href="http://cmpg.unibe.ch/software/fastsimcoal27/">http://cmpg.unibe.ch/software/fastsimcoal27/</a></p> <p>-Genepop: <a href="https://genepop.curtin.edu.au/">https://genepop.curtin.edu.au</a></p> <p>-R software</p>
Heterozygote advantage and pleiotropy contribute to intraspecific color trait variability
<p><span>The persistence of intrapopulation phenotypic variation typically requires some form of balancing selection since drift and directional selection eventually erode genetic variation.</span> <span>Heterozygote advantage remains a classic explanation for the maintenance of genetic variation in the face of selection. However, examples of heterozygote advantage, other than those associated with disease resistance are rather uncommon. </span><span>Across most of its distribution, males of the aposematic moth Arctia plantaginis have two hindwing phenotypes determined by a heritable one locus-two allele polymorphism (genotypes: WW/Wy = white morph, yy = yellow morph). Using genotyped moths we show that the presence of one or two copies of the <em>yellow </em>allele affects several life-history traits. Reproductive output of both males and females, and female mating success are negatively affected by two copies of the <em>yellow </em>allele. Females carrying one <em>yellow </em>allele (i.e. Wy) have higher fertility, hatching success, and offspring survival than either homozygote, thus leading to strong heterozygote advantage. Our results indicate strong female contribution especially at the postcopulatory stage in maintaining the color polymorphism. The interplay between heterozygote advantage, <em>yellow </em>allele pleiotropic effect and morph-specific predation pressure may exert balancing selection on the color locus, suggesting that color polymorphism may be maintained through complex interactions between natural and sexual selection.</span></p>
Fig. 55. Cyparium collare Pic, 1920 in Contributions to the taxonomy of Neotropical Cyparium Erichson (Coleoptera: Staphylinidae: Scaphidiinae), with the description of five new species
Fig. 55. Cyparium collare Pic, 1920, distribution. Squares = literature; circles = new records.
Fig. 2 in Contributions to the taxonomy of Neotropical Cyparium Erichson (Coleoptera: Staphylinidae: Scaphidiinae), with the description of five new species
Fig. 2. Dorsal terminology. Model: Cyparium collare Pic, 1920.
Fig. 3 in Contributions to the taxonomy of Neotropical Cyparium Erichson (Coleoptera: Staphylinidae: Scaphidiinae), with the description of five new species
Fig. 3. Ventral terminology. Model: Cyparium collare Pic, 1920.
Proccessed Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"
<p><strong>Proccessed and Input Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"</strong></p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Contents:</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /ProcessedData/metabolomics/ contains processed metabolomics data from the project:</p> <p>/metabolomics/metabolites_allions_combined_norm_intensity.csv - file containing normalized intensities of ions detected across tissues with six measurement methods.<br> /metabolomics/metabolites_allions_combined_formulas_with_metabolite_filters_spatial100clusters_with_mean.csv - file containing metabolite attribution to spatial clusters and mean intensity values across tissues and conditions.</p> <p>Other files are described in README_ProcessedData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /ProcessedData/sequencing/ contains raw and normalized counts of metagenomics and metatransriptomics data mapped to bacterial genomes.</p> <p>Folder /ProccessedData/util/ contains files used for data preprocessing and attribution to chemical classes and pathways.</p> <p>Folder /ProcessedData/example_output/ contains example output of the pipelines:</p> <p>/output/model_results_SMOOTH_raw_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT.<br> /output/model_results_SMOOTH_normbyabsmax_reciprocal_problem_allions.csv - file containing estimated model parameters for the reverse problem (metabolite intensities) for the parameters estimated with the forward problem.<br> /output/model_results_SMOOTH_normbyabsmax_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value.<br> /output/model_results_SMOOTH_normbyabsmax_ONLYMETCOEF_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (only metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value.<br> /output/table_hierarchical_clustering_groups.csv - file containing attribution of the annotated metabolites to groups according to hierarchical clustering of the normalized model parameters.<br> /output/cgo_clustergrams_of_model_coefficients.mat - matlab object containing clustergram of the normalized model parameters and manually derived sub-clustergrams corresponding to different largest parameter values.</p> <p>Description of other files is provided in the file README_ProcessedData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p>Folder /InputData/ contains HMDB and KEGG tables used for metabolite annotations and chemical group analysis.</p> <p>Folder InputData_KEGGreaction_path contains matlab files with metabolite-metabolite paths calculated from KEGG reaction-pair information (Each matrix contains a subset of paths). These files are used by the script workflow_extract_keggECpathes_for_SPpairs_final.m.</p> <p>Folder InputData_metabolomics_data contains raw metabolomics data from six methods (three LC columns: C08, C18 and HILIC, and positive and negative acquisition modes) and file tissue_weights.txt with tissue weight information used for normalization.</p> <p>Folder InputData_sequencing_data contains folders ballgown_DNA and ballgown_RNA with results of metagenomic and metatranscriptomic data analysis (raw counts, GetMM normalized counts, EdgeR and DeSeq2 analysis). </p> <p>Description of folders is provided in the file readme_InputData.md.</p> <p>-----------------------------------------------------------------------------------------------------</p>
Contributions of Open-loop and Closed-loop Control in a Continuous Tracking Task Differ Depending on Attentional Demands during Practice
<p>We provide tracking data of the analysed segments after filtering, cognitive performance data and indivdual force levels. To this end, the data set contains three zip-files of numbers of csv-files and an additional csv-file. In each csv-file, comma delimits information within the rows. Participant numbers range between 1 and 21 where the first 11 numbers always refer to the participants of the single-task training group and the final 10 numbers refer to the participants of the dual-task training group.</p> <p>The <em>IndividualForceMaximum.csv</em> includes separate rows of participant’s information about training group, individual force maximum, and the 10% value of the force maximum, which was important for adapting the required force to the individual applicable leg force.</p> <p>The <em>CognitiveTaskData.zip </em>contains four csv-files. Two files are for the <em>TEST</em> sessions, which include either the <em>total</em> number of responses or the number of <em>correct</em> responses per trial for each participant within a row. The other two files are for the <em>TRAINING</em> data (either <em>total</em> number of responses or number of <em>correct</em> responses) of the cognitive task for the participants of the dual-task training group. The file includes 15 trials of the first and the last five training days (i.e., days 1 to 5, and days 18 to 22). For each participant the training data is presented in a row.</p> <p>The tracking data is split with respect to the training groups in either the single-task training group, i.e., <em>MotorTaskData_SingleTaskTrainingGroup.zip</em>, or the dual-task training group, i.e., <em>MotorTaskData_DualTaskTrainingGroup.zip</em>. Each of the zip-files includes <em>TESTING</em> and <em>TRAINING</em> data in csv-files for each participant <em>P</em> separately. We further separated the <em>TEMPLATE</em> data and the <em>PERFORMANCE</em> data, i.e., for instance template test file of P1 corresponds with performance test file of P1. Each trial is presented in a row, where the header includes the segment information (i.e., first segment = 1, second segment = 2, and third segment = 3) for each data point. <em>TEST </em>data include trials with the practiced second segment and the catch trials with another second segment. <em>TRAINING</em> data include the 15 trials of the first and the last five training days (i.e., days 1 to 5, and days 18 to 22).</p>
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses.
<p>The dataset to accompany the paper by Phoebe Chua, Dr. Dimos Makris, Prof. Dorien Herremans, Prof. Gemma Roig, and Prof. Kat Agres on Predicting emotion from music videos: exploring the relative contribution of visual and auditory information on affective responses.</p>
Neocortical Localization and Thalamocortical Modulation of Neuronal Hyperexcitability contribute to Fragile X Syndrome: EEG Data
<p>Cleaned 128-channel source electrode data analyzed in manuscript</p>
Figs 1, 2 in New contribution into concept of the tribe Deveniliini (Lepidoptera: Geometridae, Ennominae) based on skeleton-muscular anatomy of the male genitalia
Figs 1, 2. Devenilia corearia (Leech, 1891), musculature of the male genitala. 1 – the
Figs 3–6 in New contribution into concept of the tribe Deveniliini (Lepidoptera: Geometridae, Ennominae) based on skeleton-muscular anatomy of the male genitalia
Figs 3–6. Musculature of the male genitala. 3, 4 – Platycerota incertaria (Leech, 1891): 3 –
Figs 7–10 in New contribution into concept of the tribe Deveniliini (Lepidoptera: Geometridae, Ennominae) based on skeleton-muscular anatomy of the male genitalia
Figs 7–10. Musculature of the male genitala. 7–9 – Lomographa bimaculata (Fabricius,
Fig. 18 in Contribution to the Siberian Chrysididae (Hymenoptera). Part 1.
Fig. 18. Philoctetes lyubae Rosa, sp. n., holotype ♀: A – head, frontal view; B –
Fig. 17 in Contribution to the Siberian Chrysididae (Hymenoptera). Part 1.
Fig. 17. Philoctetes cynthiae Rosa, sp. n., allotype ♂: A – head, frontal view; B – head
Fig. 15 in Contribution to the Siberian Chrysididae (Hymenoptera). Part 1.
Fig. 15. Chrysis pavesii Rosa, sp. n., holotype ♀: A – head, frontal view; B – mesosoma,
Fig. 11. A in Contribution to the Siberian Chrysididae (Hymenoptera). Part 1.
Fig. 11. A – Chrysis auriceps Linsenmaier, ♂, Italy, habitus, dorsal view; B – Ch. pro-
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International Brain Laboratory public data
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OpenNeuro
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