five

Data from: Ant societies buffer individual-level effects of parasite infections

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Mendeley Data2024-06-25 更新2024-06-29 收录
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https://zenodo.org/records/4944606
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Parasites decrease host fitness and can induce changes in host behavior, morphology, and physiology. When parasites exploit social insects, they influence not only infected individuals but the society as a whole. Workers of the ant Temnothorax nylanderi are an intermediate host for the cestode Anomotaenia brevis. We studied a heavily parasitized population and found that while parasite infection had strong and diverse consequences for individual workers, colony fitness remained unchanged. On the individual level, we uncovered differences among the three worker types: infected and healthy workers from parasitized colonies and healthy workers from non-parasitized colonies. Infected workers were smaller than healthy ones and had, as parasite load increased, smaller heads. Behavioral changes extended to all workers from parasitized colonies, which were less active but groomed more. Healthy workers from parasitized colonies showed behavioral patterns intermediate to those of infected workers and healthy workers from non-parasitized colonies. Despite the lower activity level, an important fitness parameter - per-worker productivity - remained unaltered in parasitized colonies. However, the investment strategies of parasitized colonies changed as their sex ratio became male-biased and male body size increased. In short, ant colonies can buffer the drain of resources by the parasite despite strong effects on individual workers.
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2023-06-28
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<h1 align="center" style="font-size: 36px;"> <span style="color: #FFD700;">AQCat25 Dataset:</span> Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis </h1> ![datset_schematic](https://cdn-uploads.huggingface.co/production/uploads/67256b7931376d3bacb18de0/W1Orc_AmSgRez5iKH0qjC.jpeg) This repository contains the **AQCat25 dataset**. AQCat25-EV2 models can be accessed [here](https://huggingface.co/SandboxAQ/aqcat25-ev2). The AQCat25 dataset provides a large and diverse collection of **13.5 million** DFT calculation trajectories, encompassing approximately 5K materials and 47K intermediate-catalyst systems. It is designed to complement existing large-scale datasets by providing calculations at **higher fidelity** and including critical **spin-polarized** systems, which are essential for accurately modeling many industrially relevant catalysts. 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Log in via the Command Line:** Open your terminal and run the following command: ```bash hf auth login ``` ### 2.2 Get the Helper Scripts You may copy the scripts directly from this repository, or download them by running the following in your local python environment: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="SandboxAQ/aqcat25", repo_type="dataset", allow_patterns=["scripts/*", "README.md"], local_dir="./aqcat25" ) ``` This will create a local folder named aqcat25 containing the scripts/ directory. ### 2.3 Download Desired Dataset Splits Data splits may be downloaded directly via the Hugging Face UI, or via the `download_split.py` script (found in `aqcat25/scripts/`). ```bash python aqcat25/scripts/download_split.py --split val_id ``` This will download `val_id.tar.gz` and extract it to a new folder named `aqcat_data/val_id/`. ### 2.4 Query the Dataset Use the `query_aqcat.py` script to filter the dataset and extract the specific atomic structures you need. 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