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Extracted microbial terms from Wikipedia - Marine Microbiology related pages (unfiltered)

<p>Marine microorganism related terms were extracted from various Wikipedia pages to scan the ODIS graph using the Wikipedia API library. The pages include topics such as "Marine microorganisms", "Marine microbiome", "Marine viruses", "Marine bacteria", "Bacterioplankton", "Bacterial motility", "Marine prokaryotes", "Marine archaea", "Marine protists", "Marine fungi", "Mycoplankton", "Marine microanimals", "Ichthyoplankton", "Marine primary production", "Algae", "Marine microplankton", "Marine microbenthos", "Sea ice microbial communities", "Hydrothermal vent microbial communities", "deep biosphere", and "microbial dark matter". </p> <p> </p> <p>We used the english version of the spaCy library, a language processing (NLP) tool, to process text content extracted from Wikipedia and extract meaningful terms. We passed the combined text content to the spaCy model to generate a Doc object, which contains words and their associated linguistic information. After extracting these words from the Doc object, spaCy keeps track of their frequencies using a dictionary and filters out words that are not purely alphabetic, common stop words, and those shorter than four characters. Then, these words are each converted  to lowercase and sorted based on their frequency.</p>

Extracted microbial terms from Wikipedia - Marine Microbiology related pages (refined)

<p>From the dataset titled "Extracted microbial terms from Wikipedia - Marine Microbiology related pages (unfiltered)" available on Zenodo https://doi.org/10.5281/zenodo.12648788, we reviewed the full dataset and narrowed it down to a subset of 100 terms directly related to microbiology. We excluded terms such as "marine", "organisms", "ocean", "life" as they are not strictly related to microbiology. Additionally, terms like "algae" and "plants" were removed since they are not microbial entities. We also avoided including verbs such as "found", "classify", and "form" because they may be related to sentences or methods in microbiology, but not strictly to microbial entities.</p>

Predominant Marine Microbial Taxa extracted from the SILVA Database

<p>Predominant Marine Microbial Taxa extracted from the SILVA Database, as described in Overmann, J., Lepleux, C. (2016). Marine Bacteria and Archaea: Diversity, Adaptations, and Culturability. In: Stal, L., Cretoiu, M. (eds) The Marine Microbiome. Springer, Cham. https://doi.org/10.1007/978-3-319-33000-6_2. </p> <p> </p> <p>We extracted the specific microbial child clauses from the SILVA database and <span>curated the headers as follows:</span></p> <ul> <li><span>The species name's epithet was removed, and any duplicate generic names were eliminated. </span></li> <li><span>We removed non-alphabetic characters from the list, such as /*-_ and numbers </span></li> <li><span>Additionally, we excluded names shorter than four characters and those containing more than two consecutive identical letters.</span></li> </ul>

SILVA database - all extracted bacterial and archaeal taxa (unfiltered)

<p>As a first attempt to target microbially related data in the ODIS graph, we extracted all the child clauses of bacterial and archaeal taxa from the SILVA database , that is all headers from the sequences of both domains of life. We saved this dataset in form of a textfile and curated the headers as follows:</p> <ul> <li>The species name's epithet was removed, and any duplicate generic names were eliminated.</li> <li>We removed non-alphabetic characters from the list, such as /*-_ and numbers</li> <li>Additionally, we excluded names shorter than four characters and those containing more than two consecutive identical letters.</li> </ul> <p>Even after organizing the dataset, there were still some taxonomic names that were not specific to microbes. These names might have been used as acronyms for specific categories (e.g. "unio" for "uniola" or "unionicola"), as species identifiers like "urhd," or as part of the species name, such as "blood," "texas," or even created purely for creative purposes by the namegiver, such as "unicorn."</p>

opencc-zeroJul 2024View details →

dryad36/100

Lipid extraction alters amino acid composition and bulk, but not amino acid, carbon and nitrogen isotope values

<p>Rationale: Concerns exist over observed shifts in value and variance of nitrogen isotopes following physicochemical extraction of lipids from organic matter. The mechanisms behind these apparent changes in bulk tissue δ15N values are not fully understood yet have major implications for analytical costs and integrity of data interpretations.</p> <p>Methods: Changes in proximate analysis, amino acid composition, C:N ratios, bulk tissue and amino acid δ13C and δ15N values, and resulting isotope‐based food web metrics were compared between lipid‐intact and lipid‐extracted muscle tissue of fishes spanning <1% to >20% muscle fat content to identify mechanisms of nitrogen isotope fractionation associated with physicochemical lipid extraction.</p> <p>Results: Bulk δ13C and δ15N values increased and %N, C:N ratios and crude protein content decreased following lipid extraction. Resulting bulk isotope niche spacing and overlap varied significantly between lipid‐intact and lipid‐extracted tissues. While amino acid composition significantly changed during lipid extraction, particularly for lipid‐associated amino acids (e.g., Glu, Lys, Ser), individual amino acid δ13C and δ15N values, and their associated compound‐specific isotope analysis of amino acids (CSIA‐AA)‐based food web metrics, did not.</p> <p>Conclusions: Physicochemical lipid extraction caused significant tissue composition changes (e.g., leaching of amino acids and 15N‐deplete nitrogenous waste) that affected δ13C and δ15N values and tissue %C and %N beyond simply removing lipids. However, lipid extraction did not alter individual amino acid δ13C or δ15N values or their associated CSIA‐AA‐based food web metrics.</p>

opencc-by-4.0Mar 2014View details →

Fig. 3 in Skin extract from Rhamdia quelen (Siluriformes: Heptapteridae) does not promote stress in conspecifics

Fig. 3. Locomotion displayed by Rhamdia quelen. Symbols

Figure 3 in Cytotoxicity of extracts and compounds isolated from Croton echioides in animal tumor cell (HTC)

Figure 3. Clerodan diterpenes tested.

Figure 2. Principal Component 1 in New antioxidant lauryl-free herbal shampoo formulation with a Brazilian plant extract

Figure 2. Principal Component 1 (PC1) and Principal Component 2 (PC2).

Figure 6 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential

Figure 6. Comparative %age inhibition of AChE by Au NPs, crud extract and allanzanthane.

Figure 5 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential

Figure 5. UV-Visible spectra of green synthesized gold nanoparticles.

Figure 7 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential

Figure 7. Comparative %age inhibition of BChE by Au NPs, crude extract and Allanzanthane.

Figure 2 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential

Figure 2. TEM images of green synthesized Au NPs at different magnifications.

Figure 1 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential

Figure 1. SEM images of green synthesized of Au NPs at different magnifications.

Figure 3 in Green synthesis of gold nanaoparticles using Delphinium Chitralense tuber extracts, their characterization and enzyme inhibitory potential

Figure 3. FTIR spectrum of green synthesized gold nanoparticles.

opencc-by-4.0Jun 2024View details →

Extracted sequences of fungal OTUs on coarse woody debris Bavarian Forest

<p>Extarcted sequences of fungal communities in coarse woody debris, sampled in the Bavarain Forest, Germany, in 2012, 2013 and 2015. A deadwood experiment with 67 stems (length 5m, dia ~33cm; 34x <em>Fagus sylvatica</em> and 33x <em>Abies alba</em>) was established in 2011. Per sampling year four drilling cores per stem were collected and pooled. PCR was performed on ITS2 region using barcoded gITS7 and ITS4. Treatment describes whether it is an experimentally created forest gap or a closed canopy. Taxonomic information and community composition only references to the most abundant OTUs used in Rieker et al. (2024): How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys. <em>Biological Conservation.</em></p> <p>Sample collection and processing are further described in</p> <p>Rieker et al. (2024): How to best detect threatened deadwood fungi – comparing metabarcoding and fruit body surveys. <em>Biological Conservation.</em></p> <p>and lab protocols follow Baldrian et al. (2016): Fungi associated with decomposing deadwood in a natural beech-dominated forest. <em>Fungal ecology, 23, 109-122.</em></p>

The role of non-native plant species in modulating riverbank erosion: a systematic review - extracted coded articles dataset

<p>Data extraction sheet of code typologies for the review titled: The role of non-native plant species in modulating riverbank erosion: a systematic review. The uploaded csv file contains all the extracted values for each typology. </p>

opencc-by-4.0Apr 2024View details →

A machine learning framework for extracting information from biological pathway images in the literature

<p><strong>466 target chemicals_selected chemicals:</strong></p> <p>Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.</p> <p> </p> <p><strong>466 target chemicals_statistics:</strong><br>Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).</p> <p> </p> <p><strong>arrow detection_bounding box labels:</strong><br>Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".</p> <p> </p> <p><strong>arrow detection_test dataset:</strong></p> <p>Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).</p> <p> </p> <p><strong>arrow detection_training and validation datasets:</strong></p> <p>Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.</p> <p> </p> <p><strong>EBPI outputs:</strong></p> <p>Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.</p> <p> </p> <p><strong>text classification_training, validation and test datasets:</strong></p> <p>Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.</p>

[Data augmentation in a TTL] - Fictive dataset (27.5M) with up to 5k reactions per template // (13'953 template extracted from USPTO-FULL IBM version)

<p>Full generated fictive dataset, containing 27.5M reactions with up to 5000 reactions per radius 1 reaction template (13'953 reaction templates from USPTO-full, IBM version).</p> <p>Title of the manuscript:</p> <p>"Data augmentation in a Triple Transformer Loop retrosynthesis model"</p> <p>Abstract: </p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div dir="auto"> <div>Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits its usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM®P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM+R by TTL transformer T3. We generated up to 5,000 reactions per template, resulting in 27.5 million validated fictive reactions covering the chemical space of the original UPSTO dataset. To exemplify the use of this dataset, we show that a single-step retrosynthesis transformer model trained with a template equilibrated subset of 1,097,374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div></div>

opencc-by-4.0Mar 2018View details →

The influence of waves on morphodynamic impacts of energy extraction at a tidal stream turbine site in the Pentland Firth: MIKE3 HD result files

<p>This dataset consists of the MIKE3 HD result files from simulations conducted for the paper 'The influence of waves on morphodynamic impacts of energy extraction at a tidal stream turbine site in the Pentland Firth' ( <a href="https://doi.org/10.1016/j.renene.2018.02.035">https://doi.org/10.1016/j.renene.2018.02.035</a> )</p> <p>Sets of 2D area result files are provided for sediment transport results (ST in file name), hydrodynamic results (HD in file name) and spectral wave results (SW in file name) in different datasets. This dataset contains HD files.</p> <p>Each file name provides details of the resuls contained: time of simulation (Jan or Jun); presence (Turb) or abscence (NoTurb) of tidal stream turbines in the simulations; and inclusion of waves in the simulations (tideAndWave or tide_only).</p> <p>Details of the methodology are given in the above paper. Please reference the paper in any use of these results.</p>