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167 results for “stoichiometry”
ChromBPNet models and data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains ChromBPNet models and data used to train the models for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>.</p> <p>`data` contains bigwigs and regions (peaks + non-peaks) used for training each of the models. See `data/README.txt` for more details.</p> <p><strong>Models:</strong></p> <p><em>Loading the model:</em></p> <p>The models were trained using tf1.14. The models are provided in h5 format for tf1.14 (py3.7) and SavedModel format for tf2.X. tf2.X tested only for py3.8-11, tf2.8-13.</p> <p>To load the models in tf1.14:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model.h5")</code></pre> <p>In tf2:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model_dir")</code></pre> <p>If all fails, you can load the architecture as provided in `model_arch.py` with default parameters (`bpnet_seq` for bias model and `chrombpnet` for chrombpnet model), and then load the weights using `model.load_weights` from the weights provided in the `weights` directory.</p> <p> </p> <p><em>Usage:</em></p> <p>The bias models take as input one-hot sequence of length 2000. It has 2 outputs, a vector of logits of length 2000, and 1 logcounts scalar:</p> <pre><code class="language-python"># seq_one_hot of length B x 2000 x 4 out_bias_logits, out_bias_logcounts = bias_model.predict(seq_one_hot) # out_bias_logits: B x 2000 # out_bias_logcounts: B x 1</code></pre> <p>The ChromBPNet model takes as input a one-hot sequence of length 2000, bias logits of length 2000 and bias log-counts scalar. It has the same output types as the bias model. To run the chrombpnet model to obtain predictions:</p> <pre><code class="language-python">pred_profile, pred_logcounts = chrombpnet_model.predict([seq_one_hot, out_bias_logits, out_bias_logcounts]) # pred_profile: B x 2000 # pred_logcounts: B x 1 </code></pre> <p>If you wish to obtain the "de-biased" predictions (see Methods), simply pass in zeros instead of the bias model predictions as:</p> <pre><code class="language-python">pred_profile_debiased, pred_logcounts_debiased = chrombpnet_model.predict([seq_one_hot, np.zeros((seq_one_hot.shape[0], 2000)), np.zeros((seq_one_hot.shape[0], 1))])</code></pre> <p>To obtain predicted per-base predicted counts (with or without bias):</p> <pre><code class="language-python">pred_per_base_counts = scipy.special.softmax(pred_profile, axis=-1) * (np.exp(pred_logcounts)-1) # pred_per_base_counts: B x 2000 </code></pre> <p>Note that in general predicted counts can't be compared across models as they are not corrected for sequencing depth.</p> <p> </p> <p><em>Note:</em></p> <p>All bias models used across folds are identical, except for the final intercept term in the counts output (see Methods), that is specific to each cell state, fold combination.</p> <p> </p> <p><em>Folds:</em></p> <p>The splits used for training the different folds are as below:</p> Fold Test Chromosomes Validation Chromosomes 0 chr1 chr8, chr10 1 chr2, chr19 chr1 2 chr3, chr20 chr2, chr19 3 chr6, chr13, chr22 chr3, chr20 4 chr5, chr16, chrY chr6, chr13, chr22 5 chr4, chr15, chr21 chr5, chr16, chrY 6 chr7, chr18, chr14 chr4, chr15, chr21 7 chr11, chr17, chrX chr7, chr18, chr14 8 chr9, chr12 chr11, chr17, chrX 9 chr8, chr10 chr9, chr12 <p>Remaining chromosomes were used as the training chromosome for each fold.</p>
Data from: Allometric and trait-based patterns in parasite stoichiometry
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Intraspecific variation in the organismal stoichiometry of the Least Killifish tracks spatial variation in periphyton composition
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Higher phosphorus and water use efficiencies and leaf stoichiometry contribute to legume success in drylands
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Widespread controls of leaf nutrient resorption by nutrient limitation and stoichiometry
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Data from: Plant traits and tissue stoichiometry explain nutrient transfer in common arbuscular mycorrhizal networks of temperate grasslands
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Spatial variance of organic matter respiration stoichiometry in the Arctic Ocean
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Body size, light intensity and nutrient supply determine plankton stoichiometry in mixotrophic plankton food webs
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Moose selection for resource stoichiometry
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Global leaf sulphur stoichiometry and the relationships with nitrogen and phosphorus: phylogeny, growth form and environmental controls
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Herbivore dung stoichiometry drives competition between savanna trees and grasses
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Data from: Plant-type dominates fine-root C:N:P stoichiometry across China: a meta-analysis
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Data from: Long-term stoichiometry and fates highlight animal egestion as nutrient repackaging, not recycling, in aquatic ecosystems
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Rapid evolution of consumptive and non-consumptive predator effects on prey population densities, bioenergetics and stoichiometry
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Imprint of tree species mycorrhizal association on microbial-mediated enzyme activity and stoichiometry
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Environmental stoichiometry mediates phytoplankton diversity effects on communities’ resource use efficiency and biomass
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Interactive effects of rising temperature and nutrient enrichment on aquatic plant growth, stoichiometry, and palatability
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Grazing intensity significantly changes the C:N:P stoichiometry in grassland ecosystems
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Substrate mass, nitrogen, extracellular enzyme, and microbial biomass and stoichiometry dynamics: Nitrogen limitation in decomposition
Modern agriculture and fossil fuel combustion contribute to the transfer of N from largely inert pools (atmospheric N2, fossil fuel reserves) to biologically reactive forms that can be transported downwind from agricultural or industrial areas to ecosystems that historically may have experienced low levels of N inputs. Understanding how increased N inputs alter the cycling of another biologically important element, C, has been impeded by uncertainties about N effects on the process of decomposition. To date, ecologists remain unable to predict when, where, and in what forms N addition stimulates rates of decomposition. For example, recent work showed that in eight low-N sites in Central Minnesota, litter N was positively correlated with decomposition, suggesting N limitation of decomposition, yet addition of inorganic N fertilizer increased decomposition in only two of eight sites. These paradoxical results call into question the assumption that the often-observed correlation between substrate N concentration and decomposition arises because N limits decomposition. Research is addressing three interrelated questions:* (1) Why do litter N and externally supplied N have contrasting effects on decomposition in low-N ecosystems? (2) Do different forms of N (organic vs. inorganic; substrate vs. externally supplied) affect the activity, function and composition of the decomposer community differently, and, if so, what are the consequences for decomposition? (3) What are temporal dynamics of the activity, function, and composition of the decomposer community and do these dynamics depend upon the amount and forms of N supplied to the decomposer community?* These questions will be addressed using a 4-y decomposition experiment manipulating the quantity and form of N available to decomposers via use of substrates ranging in N concentrations and of inorganic (ammonium nitrate) and organic (amino acids) N fertilizers. The response of microbial biomass, stoichiometry, efficiency
Data from: Biological stoichiometry of oleaginous microalgal lipid synthesis: The role of N:P supply ratios and growth rate on microalgal elemental and biochemical composition
<p>Biological Stoichiometry is an ecological framework connecting the balance of elements to the functioning of organisms. Here, we applied this framework to study the relationships between carbon:nitrogen:phosphorus (C:N:P) ratios and synthesis of industrial high value biochemicals in the highly oleaginous alga <em>Tetradesmus bernardii.</em> We expected an increase in protein content with increasing cellular N content and decreasing C:N stoichiometry, and an increase in lipid content with increasing C:N and C:P stoichiometry. We tested these hypotheses by exposing <em>T. bernardii</em> to N and P limitation at a range of N:P supply ratios in chemostats set at low and high dilution rates. Following expectations, the cellular protein content increased with the N content, and decreased with cellular C:N ratios across all treatments. Carbohydrates and lipids largely followed the relative availability of C and increased under both N and P limitation, with higher C:N and C:P ratios. Specifically, lipid content increased by 100–125% upon N and P limitation, with a shift towards more neutral lipids at the cost of glycolipids and phospholipids. Generally, we observed a re-allocation of cellular C from protein to carbohydrates upon modest N limitation, and towards lipids under P and severe N limitation. Our results demonstrate stoichiometrically predictable patterns of industrially valuable compounds in an oleaginous microalga.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.
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