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60 results for “Adaptive sampling”
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after salinity increase. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
<p>Complex bulk samples of insects from biodiversity surveys present a challenge for taxonomic identification, which could be overcome by high-throughput imaging combined with machine learning for rapid classification of specimens. These procedures require that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. However, such transfer learning may be problematic for the study of new samples not previously encountered in an image set, e.g. from unexplored ecosystems, and require methods of domain adaptation that reduce the differences in the feature distribution of the source and target domains (training and test sets). We assessed the efficiency of domain adaptation for family-level classification of bulk samples of Coleoptera, as a critical first step in the characterisation of biodiversity samples. Neural network models trained with images from a global database of Coleoptera were applied to a biodiversity sample from understudied forests in Cyprus as the target. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images and on dataset complexity. The accuracy of between-datasets predictions (across disparate source-target pairs that do not share any species or genera) was at most 82% and depended greatly on the standardisation of the imaging procedure. Algorithms for domain adaptation significantly improved the prediction performance of models trained by non-standardised, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, but the imaging conditions and classification algorithms need careful consideration.</p>
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 5. (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after salinity increase. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3. Flood debris from which the sample for Appendix 1 in Coleoptera in Floods: Biotic Surveys, Fish Food, Adaptation, Reconstruction of Paleoenvironments, and Preconstruction of Neoenvironments
Fig. 3. Flood debris from which the sample for Appendix 1 was taken: Rock Creek Trail, Kensington, Maryland, USA, 21 April 2017.
MD simulations files for: Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning
<p>Here's a rephrased version of the README file:</p> <p>#### Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning</p> <p>**Authors:** Mingyuan Zhang, Hao Wu, Yong Wang</p> <p>This repository contains the official implementation for the paper "Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning" by Mingyuan Zhang, Hao Wu, and Yong Wang. Included are all trajectories from our MD simulations in the form of PLUMED COLVAR files, as well as all analysis scripts and files needed to replicate the results and figures presented in both the main text and Supporting Information (SI) of the paper.</p> <p>The paper features two examples: Ala2 and Ala10. For each, we have organized all the associated simulation files as they were during our automated simulation pipeline. The directory structure is the same for both examples. Here, we use Ala2, found in the `Ala2` folder, as an example:</p> <p>### Key Components</p> <p>- **Automated Pipeline Implementation:** The pipeline is implemented in `Ala2/7-adaptive-40ps/ala2.ipynb`. This implementation is ready to use once all required packages are installed, and gmx/gmx_mpi/plumed are callable within the notebook. After configuring the environment and specifying parameters like `gpu_id`, `ntomp`, and `n_sim` according to your hardware, running the blocks will replicate the entire pipeline.</p> <p>- **Analysis Scripts:** The scripts to replicate the results or figures from the main text or SI are organized in three files: `Ala2/7-adaptive-40ps/AdaptiveSamplingAnalysis.ipynb`, `Ala2/7-adaptive-40ps/compare_with_msm.ipynb`, and `Ala2/7-adaptive-40ps/opes/COLVAR/analysis.ipynb`.</p> <p>### Directory Structure</p> <p>Under the `Ala2` main directory, there are seven subdirectories:</p> <p>- **`Ala2/1-topol/`**: Contains files generated during system construction, including the final Gromacs topology file `topol.top`, which is necessary for running the automated simulation script.</p> <p>- **`Ala2/2-em/`, `Ala2/3-nvt/`, `Ala2/4-npt/`**: These directories store files generated during energy minimization and NVT/NPT equilibration. The `Ala2/4-npt/npt.gro` file is required to run the automated simulation script.</p> <p>- **`Ala2/mdp/`**: Contains all mdp files used, including `Ala2/mdp/md_detail.mdp`, which is necessary for running the automated simulation script.</p> <p>- **`Ala2/7-adaptive-40ps/`**: Contains all simulation and analysis scripts, along with files required to replicate the study related to the automated pipeline.</p> <p> 1. **`Ala2/7-adaptive-40ps/CV/`**: Stores all COLVAR files from adaptive sampling simulations.<br> <br> 2. **`Ala2/7-adaptive-40ps/opes/`**: Contains all files related to OPES simulations, including raw data for the final FES plots found in `Ala2/7-adaptive-40ps/opes/COLVAR/`. The script for replicating OPES and FES estimation figures is located in `Ala2/7-adaptive-40ps/opes/COLVAR/analysis.ipynb`.<br> <br> 3. **`Ala2/7-adaptive-40ps/figures/`**: Includes all original figures from the main text and SI, saved at 600 dpi.<br> <br> 4. **`Ala2/7-adaptive-40ps/traj_and_dat/`**: Stores all PLUMED `*.dat` files for the `DRIVER` utility in adaptive sampling simulations, a topology file `input.pdb` for PLUMED `MOLINFO`, and a topology file `seed_ref.pdb` for MDAnalysis adaptive sampling seed `*.gro` generation. Note that all `*.xtc` files from adaptive sampling were deleted to reduce the package size.<br> <br> 5. **Seed Index Files:** Seed indices for each round are stored as `Ala2/7-adaptive-40ps/round{i}_seed.txt`, necessary for figure replication.<br> <br> 6. **Automated Pipeline Notebook:** Implemented in `Ala2/ala2.ipynb`. Ensure that all imported packages are installed and gromacs (both gmx and gmx_mpi)/plumed can be called within the Jupyter notebook.<br> <br> 7. **Adaptive Sampling Analysis:** Scripts for analyzing adaptive sampling trajectories are found in `Ala2/AdaptiveSamplingAnalysis.ipynb`. This notebook contains scripts to replicate all figures related to adaptive sampling.<br> <br> 8. **MSM Comparison:** Analysis scripts for MSM comparison are located in `Ala2/compare_with_msm.ipynb`. This notebook contains scripts to replicate figures used for MSM/OPES comparison.</p> <p>- **`Ala2/8-adaptive-400ps/`**: Contains all simulation files (except xtc) for an additional adaptive sampling dataset computed for MSM comparison.</p> <p>### Contact Information</p> <p>We are continuing to test and improve the pipeline, so a tutorial is not yet available. Please feel free to reach out with any questions related to the implementation via email at mingyuanzhang@zju.edu.cn or by raising an issue on our GitHub page: https://github.com/yongwangCPH/papers/tree/main/2024/ALICE.</p>
Sequencing summary files for "Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples"
<p>Sequencing summary files for experiments in "Nanopore adaptive sampling: a tool for enrichment of low abundance species in metagenomic samples". </p>
Large‐scale genome sampling reveals unique immunity and metabolic adaptations in bats
<p>Comprising more than 1,400 species, bats possess adaptations unique among mammals including powered flight, unexpected longevity given small body size, and extraordinary immunity. Some of the molecular mechanisms underlying these unique adaptations includes DNA repair, metabolism and immunity. However, analyses have been limited to a few divergent lineages, reducing the scope of inferences on gene family evolution across the Order Chiroptera. We conducted an exhaustive comparative genomic study of 37 bat species encompassing a large number of lineages, with a particular emphasis on multi-gene family evolution across immune system and metabolic genes. Genomes were obtained from public repositories and one genome (<i>Phyllostomus hastatus</i>) was assembled de novo. In agreement with previous analyses, we found lineage-specific expansions of the APOBEC3 and MHC-I gene families, and loss of the proinflammatory PYHIN gene family. We inferred more than 1,000 gene losses unique to bats, including genes involved in the regulation of inflammasome pathways such as epithelial defense receptors, the natural killer gene complex and the interferon-gamma induced pathway. Gene set enrichment analyses revealed genes lost in bats are involved in defense response against pathogen-associated molecular patterns and damage-associated molecular patterns. Gene family evolution and selection analyses indicate bats have evolved fundamental functional differences compared to other mammals in both innate and adaptive immune system, with the potential to enhance anti-viral immune response while dampening inflammatory signaling. In addition, metabolic genes have experienced repeated expansions related to convergent shifts to plant-based diets. Our analyses support the hypothesis that, in tandem with flight, ancestral bats had evolved a unique set of immune adaptations whose functional implications remain to be explored.</p>
PurIST Classification-Guided Adaptive Neoadjuvant Chemotherapy by RNA Expression Profiling of EUS Aspiration Samples
ClinicalTrials.gov study NCT04683315. IPD Sharing: NO. Countries: 1. Publications: 1.
Send-In Sample Collection for Comprehensive Analyses of Innate and Adaptive Immune Responses During Acute COVID-19 and Convalescence
ClinicalTrials.gov study NCT04582903. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
22G-Adapt Needle Biopsy Versus Fine-needle Aspiration in Endoscopic Ultrasound-guided Sampling of Solid Lesions
ClinicalTrials.gov study NCT06563232. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation
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Data from: Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA
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Large‐scale genome sampling reveals unique immunity and metabolic adaptations in bats
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Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change
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Data from: Adaptive niche‐based sampling to improve ability to find rare and elusive species: Simulations and field tests
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Data from: Evidence of neutral and adaptive genetic divergence between European trout populations sampled along altitudinal gradients
Species with a wide geographical distribution are often composed of distinct subgroups which may be adapted to their local environment. European trout (Salmo trutta species complex) provide an example of such a complex consisting of several genetically and ecologically distinct forms. However, trout populations are strongly influenced by human activities, and it is unclear to what extent neutral and adaptive genetic differences have persisted. We sampled 30 Swiss trout populations from heterogeneous environments along replicated altitudinal gradients in three major European drainages. More than 850 individuals were genotyped at 18 microsatellite loci which included loci diagnostic for evolutionary lineages and candidate markers associated with temperature tolerance, reproductive timing and immune defence. We find that the phylogeographic structure of Swiss trout populations has not been completely erased by stocking. Distinct genetic clusters corresponding to the different drainages could be identified, although nonindigenous alleles were clearly present, especially in the two Mediterranean drainages. We also still detected neutral genetic differentiation within rivers which was often associated with the geographical distance between populations. Five loci showed evidence of divergent selection between populations with several drainage-specific patterns. Lineage-diagnostic markers, a marker linked to a quantitative trait locus for upper temperature tolerance in other salmonids and a marker linked to the major histocompatibility class I gene were implicated in local adaptation and some patterns were associated with altitude. In contrast, tentative evidence suggests a signal of balancing selection at a second immune relevant gene (TAP2). Our results confirm the persistence of both neutral and potentially adaptive genetic differences between trout populations in the face of massive human-mediated dispersal.
Data from: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection
Precise detection of invasive cancer on whole-slide images (WSI) is a critical first step in digital pathology tasks of diagnosis and grading. Convolutional neural network (CNN) is the most popular representation learning method for computer vision tasks, which have been successfully applied in digital pathology, including tumor and mitosis detection. However, CNNs are typically only tenable with relatively small image sizes (200x200 pixels). Only recently, Fully convolutional networks (FCN) are able to deal with larger image sizes (500x500 pixels) for semantic segmentation. Hence, the direct application of CNNs to WSI is not computationally feasible because for a WSI, a CNN would require billions or trillions of parameters. To alleviate this issue, this paper presents a novel method, High-throughput Adaptive Sampling for whole-slide Histopathology Image analysis (HASHI), which involves: i) a new efficient adaptive sampling method based on probability gradient and quasi-Monte Carlo sampling, and, ii) a powerful representation learning classifier based on CNNs. We applied HASHI to automated detection of invasive breast cancer on WSI. HASHI was trained and validated using three different data cohorts involving near 500 cases and then independently tested on 195 studies from The Cancer Genome Atlas. The results show that (1) the adaptive sampling method is an effective strategy to deal with WSI without compromising prediction accuracy by obtaining comparative results of a dense sampling (~6 million of samples in 24 hours) with far fewer samples (~2,000 samples in 1 minute), and (2) on an independent test dataset, HASHI is effective and robust to data from multiple sites, scanners, and platforms, achieving an average Dice coefficient of 76%.
Data from: The relative power of genome scans to detect local adaptation depends on sampling design and statistical method
Although genome scans have become a popular approach towards understanding the genetic basis of local adaptation, the field still does not have a firm grasp on how sampling design and demographic history affect the performance of genome scans on complex landscapes. To explore these issues, we compared 20 different sampling designs in equilibrium (i.e. island model and isolation by distance) and nonequilibrium (i.e. range expansion from one or two refugia) demographic histories in spatially heterogeneous environments. We simulated spatially complex landscapes, which allowed us to exploit local maxima and minima in the environment in 'pair' and 'transect' sampling strategies. We compared FST outlier and genetic–environment association (GEA) methods for each of two approaches that control for population structure: with a covariance matrix or with latent factors. We show that while the relative power of two methods in the same category (FST or GEA) depended largely on the number of individuals sampled, overall GEA tests had higher power in the island model and FST had higher power under isolation by distance. In the refugia models, however, these methods varied in their power to detect local adaptation at weakly selected loci. At weakly selected loci, paired sampling designs had equal or higher power than transect or random designs to detect local adaptation. Our results can inform sampling designs for studies of local adaptation and have important implications for the interpretation of genome scans based on landscape data.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.