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476 results for “Erosive”
Figure 3 in What is the matter with Corbicula fluminea (Mollusca: Bivalvia: Cyrenidiae)? Occurrence of unusual shell erosion in Northern Italy
Figure 3. Mean (± standard deviation) (a) length (mm); (b) thickness (mm); and (c) shell dry weight (g).
Figure 2 in What is the matter with Corbicula fluminea (Mollusca: Bivalvia: Cyrenidiae)? Occurrence of unusual shell erosion in Northern Italy
Figure 2. The two semi-natural streams of the lower River Ticino catchment in south-west Lombardy (Northern Italy).
Figure 1 in What is the matter with Corbicula fluminea (Mollusca: Bivalvia: Cyrenidiae)? Occurrence of unusual shell erosion in Northern Italy
Figure 1. Corbicula fluminea with shell damage collected in (a) Gaviola Stream and (b) Castellana Stream.
Figure 4 in What is the matter with Corbicula fluminea (Mollusca: Bivalvia: Cyrenidiae)? Occurrence of unusual shell erosion in Northern Italy
Figure 4. Regression analysis of length (L) vs shell thickness (ShT). (a) Gaviola all specimens; (b) Gaviola small specimens; (c) Gaviola large specimens; (d) Castellana all specimens; (e) Castellana small specimens; (f) Castellana large specimens.
Figure 7 in What is the matter with Corbicula fluminea (Mollusca: Bivalvia: Cyrenidiae)? Occurrence of unusual shell erosion in Northern Italy
Figure 7. Regression analysis of (a) length (L) vs shell thickness (ShT) and (b) shell thickness (ShT) vs shell dry weight (ShDW) of the Vernavola specimens.
Data for: Coral adaptive capacity insufficient to halt global transition of coral reefs into net erosion under climate change
<p>Projecting the effects of climate change on net reef calcium carbonate production is critical to understanding the future impacts on ecosystem function, but prior estimates have not included corals' natural adaptive capacity to such change. Here we estimate how the ability of symbionts to evolve tolerance to heat stress, or for coral hosts to shuffle to favourable symbionts, and their combination, may influence responses to the combined impacts of ocean warming and acidification under three representative concentration pathway emissions scenarios (RCP2.6, RCP4.5, RCP8.5). We show that symbiont evolution and shuffling both individually and when combined favours persistent positive net reef calcium carbonate production. However, our projections of future net calcium carbonate production under climate change vary both spatially and by RCP. For example, 19–35% of modelled coral reefs are still projected to have net positive net calcium carbonate production by 2050 if symbionts can evolve increased thermal tolerance, depending on the RCP. Without <span>symbiont adaptive capacity,</span> the number of coral reefs with positive net calcium carbonate production drops to 9–13% by 2050. Accounting for both symbiont evolution and shuffling, we project median positive net calcium carbonate production of coral reefs will still occur under low greenhouse emissions (RCP2.6) in the Indian Ocean, and even under moderate emissions (RCP4.5) in the Pacific Ocean. However, adaptive capacity will be insufficient to halt the transition of coral reefs globally into erosion by 2050 under severe emissions scenarios (RCP8.5).</p>
Data, Code and Computational Environment for: Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion
<p><strong>Data</strong></p> <p>The csv file "2023-06-27_data.csv" contains genetic profiles for all olive trees analyzed in the publication: Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion". The genotypes are provided in the GenAlEx format.</p> <p> </p> <p><strong>Code</strong></p> <p>Code is provided in the compressed folder "Tourvas_et_al_Olive". It is structured as a R project and can easily be opened, after decompressing, from the Rstudio interface. If you prefer to review and/or reuse code you can access it from the "analysis" folder inside the "Tourvas_et_al_Olive" folder.</p> <p> </p> <p><strong>Computational Environment</strong></p> <p>A tarball for the Docker image "tourvas_et_al_olive" is also provided. This is the recommended way to reproduce the results of the publication: "Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion".</p> <p>It is assumed that you already have Docker installed on your system. If not, please visit <a href="https://docs.docker.com/get-started/">https://docs.docker.com/get-started/</a></p> <p>To use it:</p> <ul> <li>download the image file tourvas_et_al_olive.tar</li> <li>load it with docker with the command:</li> </ul> <pre><code class="language-bash">docker load --input tourvas_et_al_olive.tar</code></pre> <ul> <li>then launch the Docker container with the command:</li> </ul> <pre><code class="language-bash">docker run --name popgen --rm -dp 8787:8787 -e ROOT=TRUE -e DISABLE_AUTH=true -v "`pwd`":/home/rstudio/working nikostourvas/tourvas_et_al_olive</code></pre> <ul> <li>start your favorite web browser and go to: http://localhost:8787/</li> <li>from the bottomright pane of the Rstudio server click on the directory "Tourvas_et_al_Olive" and open the project by clicking on the "Tourvas_et_al_Olive.Rproj" file</li> <li>launch the scripts inside the "analysis" directory and run them to reproduce results</li> </ul>
Small scale spatial variability in diffusive erosion and deposition simulation data
<p>Small scale spatial variability in diffusive erosion and deposition simulation data</p>
Replication Package for the Paper: Towards Automated Identification of Violation Symptoms of Architecture Erosion
<p><strong>Abstract of this Study</strong></p> <div> <div>Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation deviates from the intended architecture. To prevent this, development teams need to understand early enough the symptoms of erosion, and particularly violations of the intended architecture. One feasible way is through the automated identification of architecture violations from textual artifacts, and particularly code reviews. In this paper, we developed 15 machine learning-based and 4 deep learning-based classifiers with three pre-trained word embeddings to identify violation symptoms of architecture erosion from developer discussions in code reviews. Specifically, we looked at code review comments from four large open-source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We then conducted a survey and semi-structured interviews to acquire feedback from the involved participants who discussed architecture violations in code reviews, to validate the usefulness of our trained classifiers. Moreover, we conducted additional comparative experiments by evaluating Large Language Model (LLM)-based classifiers, including GPT-4o, Qwen-2.5, and DeepSeek-R1. The results show that, for ML/DL-based classifiers, the SVM classifier based on <em>word2vec</em> pre-trained word embedding performs the best with an F1-score of 0.779. In most cases, classifiers with the <em>fastText</em> pre-trained word embedding model can achieve relatively good performance. Moreover, 200-dimensional pre-trained word embedding models outperform classifiers that use 100- and 300-dimensional models. For ML/DL-based classifiers, ensemble classifiers based on the majority voting strategy can enhance the classifier and outperform the individual classifiers. The findings derived from the online survey and interviews conducted with the involved developers reveal that the violation symptoms identified by our approaches have practical value and can provide early warnings for impending architecture erosion. Furthermore, LLM-based classifiers consistently outperform traditional ML/DL models, with GPT-4o yielding the highest F1-score of 0.851, though ensemble strategies offered no further performance gains for ensemble LLM-based classifiers in our case. We investigate the automated identification of violation symptoms from code reviews using both traditional ML/DL and state-of-the-art LLM techniques. Our contributions include the automated approach for identifying violation symptoms in code reviews, a systematic comparison of ML/DL and LLM approaches, and practitioner-centered insights into their practical usefulness, ultimately contributing to better architectural conformance and sustainability in software systems.</div> </div> <p><strong>Structure of the Replication Package</strong></p> <ul> <li><strong>data.zip</strong> includes: (1) extracted features (<strong>extracted_features</strong>) as inputs of classifiers, that is, word vectors of the extracted violation symptoms based on the three pre-trained word embedding models (i.e., word2vec, fastText, GloVe). (2) <strong>word_embedding</strong> includes pre-trained word embedding models. Due to the large size, we listed the download URL Download_url.txt, and we used embedding_dim.py to change the dimensions of the fastText models. (3) <strong>Violation symptoms.xlsx</strong> and <strong>Randomly_selected_comments.xlsx</strong> represent the review comments labeled as violations and non-violations, respectively.</li> <li><strong>scripts.zip </strong>includes the Python scripts used to run the experiments, including data preprocessing and classifier training.</li> <li><strong>survey and interview.zip</strong> include the survey form, interview protocol and questions, and the template of customized emails that we sent to participants.</li> </ul> <p><strong>Experiment Steps</strong></p> <p>1. Preprocessing and feature extraction: </p> <ul> <li>Run feature_extraction.py to conduct preprocessing and feature extraction after adjusting appropriate parameters.</li> <li>It includes five steps: (1) Tokenization (2) Noise Removal (3) Stop words Removal (4) Capitalization Conversion (5) Stemming.</li> <li>Feature selection methods: word2vec, fastText, and Glove.</li> </ul> <p>2. Training classifiers: </p> <ul> <li>Run <em>Classifiers_ML.py</em> to train machine learning-based classifiers.</li> <li>Run <em>Classifiers_DL_classifiers.py</em> to train deep learning-based classifiers.</li> <li> <div> <div>Run <em>LLM.py</em> to generate LLM-based classifiers.</div> <div>Run <em>LLM_performance.py</em> to evaluate the performance of LLM-based classifiers.</div> <div> <div> <div>Run <em>LLM_voting.py</em> to conduct voting strategy for ensemble LLM-based classifiers.</div> </div> </div> </div> </li> <li>Machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Bernoulli Naive Bayes (NB), and k-Nearest Neighbor (kNN).</li> <li>Deep learning algorithm: TextCNN.</li> <li> <div> <div>Large language models: GPT-4o, Qwen-2.5, DeepSeek-R1.</div> </div> </li> </ul> <p><strong>Experiment Environment</strong></p> <p>Required packages and their versions:</p> <ul> <li>torch==1.11.0</li> <li>numpy==1.22.3</li> <li>gensim==4.1.2</li> <li>fasttext==0.9.2</li> <li>pandas==1.4.1</li> <li>torchtext==0.12.0</li> <li>sklearn==0.0</li> <li>scikit-learn==1.0.2</li> <li>w2vembeddings==0.1.2</li> <li>matplotlib==3.5.1</li> <li>tqdm==4.62.3</li> <li>nltk==3.7 </li> <li> <div> <div>openai==1.95.1</div> </div> </li> </ul>
Bone Erosions in Rheumatoid Arthritis - Characterization Evaluated by Imaging and Histology
ClinicalTrials.gov study NCT04645381. IPD Sharing: NO. Countries: 1. Publications: 1.
Re-EValuating the Inhibition of Stress Erosions (REVISE) Trial
ClinicalTrials.gov study NCT03374800. IPD Sharing: YES. Countries: 8. Publications: 6.
Humira in Rheumatoid Arthritis - Do Bone Erosions Heal?
ClinicalTrials.gov study NCT00696059. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Inflammatory Role of Epstein-Barr Virus (EBV) in Atrophic and Erosive Forms of Oral Lichen Planus
ClinicalTrials.gov study NCT02276573. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Distal Erosions and Nail Psoriasis
ClinicalTrials.gov study NCT02813720. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of the Classical Healing Concept With the Complete Remission Concept After Treatment With Pantoprazole in Adult Patients With Erosive GERD (Gastroesophageal Reflux Disease) (BY1023/M3-342)
ClinicalTrials.gov study NCT00325676. IPD Sharing: Not stated. Countries: 4. Publications: 1.
Study to Evaluate the Safety and Efficacy of CJ-12420 in Patients With Non Erosive Reflux Disease
ClinicalTrials.gov study NCT02556021. IPD Sharing: NO. Countries: 1. Publications: 1.
Re-EValuating the Inhibition of Stress Erosions (REVISE) - COVID-19 Cohort Study
ClinicalTrials.gov study NCT05715567. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Diode Laser and Topical Steroid Therapy in the Treatment of Erosive Oral Lichen Planus
ClinicalTrials.gov study NCT05951361. IPD Sharing: YES. Countries: 1. Publications: 3.
Progression of Bone Erosions in Rheumatoid Arthritis Assessed by HR-pQCT and Conventional X-ray
ClinicalTrials.gov study NCT03429426. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
A Study to Evaluate REbamiPide as an Adjuvant Regimen to Heal erosIve Reflux Esophagitis
ClinicalTrials.gov study NCT02755753. IPD Sharing: NO. Countries: 2. Publications: 24.
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International Brain Laboratory public data
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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.