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111 results for “Adaptive dynamics”
The role of fire in the carbon dynamics of the boreal forest I. - Response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach (2003-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
The diet–intestinal microbiota dynamics and adaptation in an elevational migration bird, the Himalayan bluetail (Tarsiger rufilatus)
<p>Migratory birds experience changes in their environment and diet during seasonal migrations, thus requiring interactions between diet and gut microbes. Understanding the co-evolution of the host and gut microbiota is critical for elucidating the rapid adaptations of avian gut microbiota. However, dynamics of gut microbial adaptations concerning elevational migratory behavior, which is prevalent but understudied in montane birds remain poorly understood. We focused on the Himalayan bluetail (<em>Tarsiger rufilatus</em>) in the montane forests of Mt. Gongga to understand the diet-gut microbial adaptations of elevational migratory birds. Our findings indicate that elevational migratory movements can rapidly alter gut microbial composition and function within a month. There was a significant interaction between an animal-based diet and gut microbiota across migration stages, underscoring the importance of diet in shaping microbial communities. Furthermore, the gut microbial composition of <em>T. rufilatus</em> may be potentially altered by high-altitude acclimatization. An increase in fatty acid and amino acid metabolism was observed in response to low temperatures and limited resources, resulting in enhanced energy extraction and nutrient utilization. Moreover, microbial communities in distinct gut segments varied in relative abundance and responses to environmental changes. While the bird jejunum exhibited greater susceptibility to food and environmental fluctuations, there was no significant difference in metabolic capacity among gut segments. This study provides initial evidence of rapid diet-gut microbial changes in distinct gut segments of elevational migratory birds and highlights the importance of seasonal sample collection. Our findings provide a deeper understanding of the unique high-altitude adaptation patterns of the gut microbiota for montane elevational migratory birds.</p>
AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes
<p>Proteins often exist in multiple conformational states, influenced by the binding of ligands or substrates. The study of these states, particularly the apo (unbound) and holo (ligand-bound) forms, is crucial for understanding protein function, dynamics, and interactions. In the current study, we use AlphaFold2 that combines<span> randomized</span> <span><span> </span>alanine<span> </span>sequence masking<span> </span>with shallow multiple sequence alignment<span> </span>subsampling to expand the conformational diversity of the predicted structural<span> </span>ensembles and<span> </span>capture conformational changes between apo and holo protein forms. Using several well-established datasets of<span> </span>structurally diverse apo-holo protein pairs, the proposed approach </span><span>enables<span> </span>robust predictions of apo and holo structures and conformational ensembles, while also displaying notably similar dynamics distributions. These observations are consistent with<span> </span>the view </span><span> </span>that the intrinsic dynamics of allosteric proteins is defined by the structural topology of the fold and favors conserved conformational motions driven by soft modes among orthologs. We also found<span> </span>a significant <span>correlation </span>between conformational flexibility and <span> </span>AlphaFold2 metric of statistical significance pLDDT for the apo-holo pairs in which ligand binding induced local moderate conformational changes. For apo-holo pairs exhibiting larger structural changes, this relationship<span> </span>becomes nonlinear, reflecting inability of AlphaFold2 confidence metrics to identify high energy functional conformations. Our findings support the notion that AlphaFold2 approaches can yield reasonable accuracy in predicting minor conformational adjustments between apo and holo states, especially for proteins with <span> </span>moderate localized changes upon ligand binding. However, for large, hinge-like domain movements, AF2 tends to predict the most stable domain orientation which is typically the apo form rather than the full range of functional conformations characteristic of the holo ensemble. These results indicate that modeling of multiple functional states of proteins may require more accurate detection of flexible region conformations and cannot solely rely on the pLDDT metric as the major determinant of the prediction accuracy in reproducing functional conformational ensembles.<span> </span></p>
Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes
<p>We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to offload sets of patches to the GPU. Our studies show that multithreaded, concurrent, non-deterministic access to the GPU leads to performance breakdowns, since the GPU memory bookkeeping as offered through OpenMP's map clause, i.e., the allocation and freeing, becomes another runtime challenge besides expensive data transfer and actual computation. We, therefore, propose to retain the memory management responsibility on the host: A caching mechanism acquires memory on the accelerator for all CPU threads, keeps hold of this memory and hands it out to the offloading threads upon demand. We show that this user-managed, CPU-based memory administration helps us to overcome the GPU memory bookkeeping bottleneck and speeds up the time-to-solution of Finite Volume kernels by more than an order of magnitude.</p>
Genomic features for adaptation and evolutionary dynamics of four Asian domestic carps
<p><span>The four major Asian domestic carps, namely grass carp, black carp, bighead carp, and silver carp, belonging to <span>Cypriniformes, <span>and are among the most important aquaculture species and sources of animal protein in China. they <span>have similar habitats, closely phylogenetic relationships, and large body sizes. However, they differ in their diet preferences, behavior, and physical traits. Here, to better understand their evolution, we generated the chromosome-level genomes of the four domestic carps. We uploaded the assembled genomes of four carps, the gene annotation files in gff format of four carps, the protein sequences files of four carps, and the analysis code or pipeline used in the article, and the readme file. This study <span>shed light on the genomic bases</span><span> driving species divergence and adaptation, providing valuable insights for future research in this field.</span></span></span></span></span></p>
The diet–intestinal microbiota dynamics and adaptation in an elevational migration bird, the Himalayan bluetail (Tarsiger rufilatus)
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Genomic features for adaptation and evolutionary dynamics of four Asian domestic carps
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Drivers and dynamics of a massive adaptive radiation in cichlid fishes
<p>Adaptive radiation is the likely source of much of the ecological and morphological diversity of life. How adaptive radiations proceed and what determines their extent remains elusive in most cases. Here we report the in-depth examination of the spectacular adaptive radiation of cichlid fishes in African Lake Tanganyika. Based on whole-genome phylogenetic analyses, multivariate morphological measurements of three ecologically relevant trait complexes (body shape, upper oral jaw morphology, and lower pharyngeal jaw shape), scoring of pigmentation patterns, and approximations of the ecology of virtually all ~240 cichlid species endemic to Lake Tanganyika, we show that the radiation occurred within the confines of the lake and that morphological diversification proceeded in consecutive trait-specific pulses of morphospace expansion. We provide empirical support for two theoretical predictions on how adaptive radiations proceed, the 'early-burst' scenario (for body shape) and the stages model (for all traits investigated). Through the analysis of two genomes per species and by taking advantage of the uneven distribution of species in subclades of the radiation, we further show that species richness scales positively with per individual heterozygosity, but is not correlated with transposable element content, number of gene duplications, or genome-wide levels of selection in coding sequences.</p>
Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"
<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>
03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p> <p> </p>
06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, <em>run_adaptiveMD.py</em> : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p>
Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p><strong>00_Caver.tar.gz </strong>- Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA). <br>final_clustering<br>├── tunnel_custers # contains caver output files for individual tunnels clusters <br>│ ├── ...<br>├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters <br>│ ├── ...</p> <p><strong>01_CaverDock_Tunnels_Profile.tar.gz</strong> - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained. <br># Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are:<br>- <em>calculations/*/caverdock.conf</em> : The config file input for caverdock calculation. <br>-<em> calculations/*/DBE.pdbqt </em>: Input file for the substrate DBE.<br>- <em>calculations/*/stripped_system*.pdbqt </em>: Input file for the Protein/Receptor<br>- c<em>alculations/*/stripped_system*.dsd </em>: Tunnel discretization file. Notes: <br>- <em>calculations/*/stripped_system*.pdb </em>: PDB file for the tunnel. </p> <p><strong>02_Minimization_and_Equilibration.tar.gz</strong> - Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations.</p> <p><strong>03_HTMD_Bulk</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. <br><strong>04_HTMD_Cavity</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.<br><strong>05_HTMD_Cavity_Bulk</strong> -<strong> </strong>separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. <br><strong>06_HTMD_Tunnels</strong> - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p><strong>07_MD-Analysis.tar.gz</strong> - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K. <br># Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are:<br>- <em>Epoch_Convergence/epochs_dist_counts.csv</em> : Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs. <br>- <em>Distances/*/dist_s_r*.csv</em> : Contains .csv file for the frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM <br>- <em>RMSD_RMSF/*/*.csv</em> : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program. Example input: pytraj.rmsd(traj, mask='1-295@CA') Example input: pytraj.rmsf(traj, mask=':1-295', options='byres')<br>- <em>COM_RMSF/.xlsx</em> : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213)<br>- <em>Kinetics/kinetics.txt</em> : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates.<br>- <em>Percentages/.csv</em> : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å).<br>- <em>Tunnels_lengths/.csv</em> : Contains <em>tunnel_lengths.csv</em>, <em>Summary of tunnel lengths.xlsx</em> files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in <em>tunnel_lengths.csv</em> and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the <em>Summary of tunnel lengths.xlsx</em> file. <br>- <em>Sample_proportions/.csv</em> : Contains <em>sample_proportions.csv</em> file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html<br> or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path<br> - <em>*.py</em> : Python scripts used to build and analysis Markov state models with use case and distances of ligand.<br>- <em>Generated_models/</em> : Contains <em>models_rep[].dat</em> files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below:<br>├── Cavity<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>├── Cavity_Bulk<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>└── Tunnels<br> ├── model_rep1.dat<br> ├── model_rep2.dat<br> └── model_rep3.dat </p> <p><br><strong>08_TransportTools.tar.gz</strong> - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools<br>results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>- <em>event.csv</em> file contains the aggregated summary of events inferred from the <em>4-filtered_events_statistics.txt</em> files of each results of respective schemes</p> <p><strong>09_MSM_states.tar.gz</strong> - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below: <br>├── Bulk<br>│ ├── rep1 # MSM states for replicate 1<br>│ ├── rep2 # MSM states for replicate 2<br>│ ├── rep3 # MSM states for replicate 3<br>├── Cavity<br>│ ├── rep1 <br>│ ├── rep2 <br>│ ├── rep3 <br>├── Cavity&Bulk<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3<br>├── Tunnels<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3</p> <p><br><strong>10_MSM_fingerprints.tar.gz</strong> - Contains the Markov state models (MSMs) distances generated from repository dir <strong>09_MSM_states</strong> consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package.<br>- <em>MSM_Distances/*/rep*/*.csv</em> : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM </p> <p><br><strong>11_ULS_clustering_and_transition_assignments.tar.gz</strong> - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files:<br>1. for the transition detection of DBE and the classification in Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and <br>2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown.<br># the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels:<br>├── average_tunnel_utilization_per_scheme.png<br>├── average_tunnel_utilization.png<br>├── Bulk<br>│ ├── Bulk_run_htmd_0_combined_df.csv<br>│ ├── Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Bulk_run_htmd_1_combined_df.csv<br>│ ├── Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Bulk_run_htmd_2_combined_df.csv<br>│ └── Bulk_run_htmd_2_transitions_counts.csv<br>├── Bulk&Cavity<br>│ ├── Cavity&Bulk_run_htmd_0_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_1_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_2_combined_df.csv<br>│ └── Cavity&Bulk_run_htmd_2_transitions_counts.csv<br>├── Cavity<br>│ ├── Cavity_run_htmd_0_combined_df.csv<br>│ ├── Cavity_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity_run_htmd_1_combined_df.csv<br>│ ├── Cavity_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity_run_htmd_2_combined_df.csv<br>│ └── Cavity_run_htmd_2_transitions_counts.csv<br>├── parse_distances_msm.py<br>├── schemes_comparison_piechart_per_scheme.png<br>└── Tunnels<br> ├── Tunnels_run_htmd_0_combined_df.csv<br> ├── Tunnels_run_htmd_0_transitions_counts.csv<br> ├── Tunnels_run_htmd_1_combined_df.csv<br> ├── Tunnels_run_htmd_1_transitions_counts.csv<br> ├── Tunnels_run_htmd_2_combined_df.csv<br> └── Tunnels_run_htmd_2_transitions_counts.csv</p> <p> </p>
05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. </p> <p># The forders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Exo-Metabolomics Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"
<p>Exo-Metabolomics Data for "<em>Phocaeicola vulgatus</em> shapes the long-term growth dynamics and evolutionary adaptations of <em>Clostridioides difficile</em>"</p>
Illumina Sequencing Data for "Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile"
<p>Illumina Sequencing Data for "<em>Phocaeicola vulgatus</em> shapes the long-term growth dynamics and evolutionary adaptations of <em>Clostridioides difficile</em>"</p>
Lineage-Specific Class-A GPCR Dynamics Reflect Diverse Chemosensory Adaptations in Lophotrochozoa
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Data from: Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation
<p><span><span>Behavioral experience and flexibility are crucial for survival in a constantly changing environment. Despite evolutionary pressures to develop adaptive behavioral strategies in a dynamically changing sensory landscape, the underlying neural correlates have not been well explored. Here, we use genetically encoded voltage imaging to measure signals in primary somatosensory cortex (S1) during sensory learning and behavioral adaptation in the mouse. In response to changing stimulus statistics, mice adopt a strategy that modifies their detection behavior in a context dependent manner as to maintain reward expectation. Surprisingly, neuronal activity in S1 shifts from simply representing stimulus properties to transducing signals necessary for adaptive behavior in an experience dependent manner. Our results suggest that neuronal signals in S1 are part of an adaptive framework that facilitates flexible behavior as individuals gain experience, which could be part of a general scheme that dynamically distributes the neural correlates of behavior during learning. </span></span></p>
Testing Dynamically Adaptive Systems: A Preliminary Literature Review
<p><strong>Context.</strong> Dynamically Adaptive Systems (DAS) encompasses a large class of systems that adapt their behaviours in response to changes in their context and/or predefined optimisation goals. Advanced software architectures support these adaptations robustly and efficiently at runtime. Yet, testing such architectures remains challenging as tests also need to cope with contextual and behavioural changes (addition, modification or removal) to be useful and prevent undesired behaviours to be deployed.<br> <strong>Objective.</strong> The objective of this study is to identify what strategies are used to test DAS and raise evidence on techniques and tools that achieve high defect detection even in unpredictable contexts.<br> <strong>Method.</strong> We performed a literature review of 389 studies published from the year 2001 until 2019. These studies cover the testing of Dynamic Software Product Lines, Context-Aware Systems and Dynamically Adaptive Systems.<br> <strong>Results.</strong> Our analysis comprised three fundamental aspects of testing strategies for DAS: executing the test cases, the test methods, and the test tools. Our preliminary findings indicate that the literature offers a large number of strategies to cope with such aspects. However, the studies analyzed do not show how to design or execute the test cases in practice.</p>
Data from: Helophyte Glyceria spiculosa adapts to environmental changes by adjusting bud bank dynamics
<p>Exploring plant adaptation strategies under habitat variations from the perspective of bud banks is essential for understanding vegetation regeneration under global changes. To date, less attention has been paid to how bud banks respond to environmental changes. In this study, Glyceria spiculosa was selected as the subject. Field bud bank surveys were conducted in three vegetation zones: wet meadow, tussock marsh, and marsh. A pot experiment containing three flooding periods (no flooding, flooding for 1-5 weeks, and flooding for 6-10 weeks), four nitrogen levels (1, 10, 30, and 100 mg/L), and two nitrogen forms (ammonium nitrogen and nitrate nitrogen) was carried out to further investigate the relationship between bud banks and environmental factors. Rhizome internode buds dominated in the natural vegetation zone, with their density peaking in the marsh zone and minimizing in the tussock marsh zone. Soil nutrients and water depth impacted tiller bud density. Tiller bud density was highest in pot experiment. Both ammonium nitrogen and elevated nitrogen levels promoted bud bank densities, and ammonium nitrogen further enhanced the tiller bud density increase induced by higher nitrogen levels. Nitrogen level was the primary factor influencing total, tiller, and rhizome internode bud densities, whereas flooding period was the most crucial determinant of rhizome apical bud density. Our results reveal that the adjustment of bud bank type and density is a crucial strategy for plants to adapt to environmental changes, emphasizing the significance of nutrient and water variations in shaping plant reproductive strategies and adaptive capacity.</p> <p> </p> <p>The data included different types of bud bank density, soil and water nutrient data from field investigation, corresponding different types of bud bank density and initial plant height extracted from pot experiment.</p> <h3>Variables</h3> <ul> <li>site: site</li> <li>plot: plot</li> <li>quadrat: quadrat</li> <li>wetland.type: wetland type</li> <li>Tzongya: density of the total buds</li> <li>Tfennieya: density of the tiller buds (buds growing vertically upward from the base of the plant)</li> <li>Tgenjingjieya: density of the rhizome internode buds (buds located on the rhizome internodes)</li> <li>Tgenjingdingya: density of the rhizome apical buds (buds located at the tip of the rhizome)</li> <li>WTN: water total nitrogen</li> <li>WTP: water total phosphorus</li> <li>WNO3: water nitrate nitrogen</li> <li>WNH4: water ammonium nitrogen</li> <li>STN: soil total nitrogen</li> <li>STP: soil total phosphorus</li> <li>SNO3-N: soil nitrate nitrogen</li> <li>SNH4-N: soil ammonium nitrogen</li> <li>water.depth: water depth</li> <li>Nform: nitrogen form(AN:ammonium nitrogen; NN:nitrate nitrogen)</li> <li>Flooding: flooding periods (ck: no flooding throughout; F1: flooding in weeks 1-5, F2: flooding in weeks 6-10)</li> <li>Nlevel: nitrogen levels (N1:1; N2: 10;N3: 30; N4: 100 mg/L)</li> <li>zongya: density of the total buds</li> <li>fennieya: density of the tiller buds (buds growing vertically upward from the base of the plant)</li> <li>genjingjieya: density of the rhizome internode buds (buds located on the rhizome internodes)</li> <li>genjingdingya: density of the rhizome apical buds (buds located at the tip of the rhizome)</li> <li>InitialH: Initial height</li> </ul>
ScienceDex guides
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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.