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2,235 results for “engineering”
Overcoming nutritional immunity by engineering iron-scavenging bacteria for cancer therapy
<p>Certain bacteria demonstrate the ability to target and colonize the tumor microenvironment, a characteristic that positions them as innovative carriers for delivering various therapeutic agents in cancer therapy. Nevertheless, our understanding of how bacteria adapt their physiological condition to the tumor microenvironment remains elusive. In this work, we employed liquid chromatography-tandem mass spectrometry to examine the proteome of <em>E. coli</em> colonized in murine tumors. Compared to <em>E. coli </em>cultivated in the rich medium, we found that <em>E. coli </em>colonized in tumors notably upregulated the processes related to ferric ions, including enterobactin biosynthesis and iron homeostasis. This finding indicated that the tumor is an iron-deficient environment to <em>E. coli</em>. We also found that the colonization of <em>E. coli </em>in the tumor led to an increased expression of lipocalin 2 (LCN2), a host protein that can sequester enterobactin. We therefore engineered <em>E. coli</em> to evade the nutritional immunity provided by LCN2. By introducing the IroA cluster, the <em>E. coli</em> synthesizes the glycosylated enterobactin, which creates steric hindrance to avoid the LCN2 sequestration. The IroA-<em>E. coli</em> showed enhanced resistance to LCN2 and significantly improved the anti-tumor activity in mice. Moreover, the mice were cured by the IroA-<em>E. coli </em>treatment became resistant to the tumor re-challenge, indicating the establishment of immunological memory. Overall, our study underscores the crucial role of bacteria's ability to acquire ferric ions within the tumor microenvironment for effective cancer therapy.</p>
Dataset for Engineering multifunctional dynamic hydrogel for biomedical and tissue regenerative applications
<p><span>Hydrogels have emerged in various biomedical applications, including tissue engineering and medical devices, due to their ability to imitate the natural extracellular matrix (ECM) of tissues. However, conventional static hydrogels lack the ability to dynamically respond to changes in their surroundings to withstand the robust changes of the biophysical microenvironment and to trigger on-demand functionality such as drug release and mechanical change. In contrast, multifunctional dynamic hydrogels can adapt and respond to external stimuli and have drawn great attention in recent studies. It is realized that the integration of nanomaterials into dynamic hydrogels provides numerous functionalities for a great variety of biomedical applications that cannot be achieved by conventional hydrogels. This review article provides a comprehensive overview of recent advances in designing and fabricating dynamic hydrogels for biomedical applications. We describe different types of dynamic hydrogels based on breakable and reversible covalent bonds as well as noncovalent interactions. These mechanisms are described in detail as a useful reference for designing crosslinking strategies that strongly influence the mechanical properties of the hydrogels. We also discuss the use of dynamic hydrogels and their potential benefits. This review further explores different biomedical applications of dynamic nanocomposite hydrogels, including their use in drug delivery, tissue engineering, bioadhesives, wound healing, cancer treatment, and mechanistic study, as well as multiple-scale biomedical applications. Finally, we discuss the challenges and future perspectives of dynamic hydrogels in the field of biomedical engineering, including the integration of diverse technologies.</span></p>
Agile Minds, Innovative Solutions, and Industry–Academia Collaboration: Lean R&D Meets Problem-based Learning in Software Engineering Education
<p>Supplementary materials of the paper "Agile Minds, Innovative Solutions, and Industry–Academia Collaboration: Lean R&D Meets Problem-based Learning in Software Engineering Education"</p>
Einsum Benchmark: Enabling the Development of Next-Generation Tensor Execution Engines
<p>Modern artificial intelligence and machine learning workflows rely on efficient tensor libraries. However, tuning tensor libraries without considering the actual problems they are meant to execute can lead to a mismatch between expected performance and the actual performance. Einsum libraries are tuned to efficiently execute tensor expressions with only a few, relatively large, dense, floating-point tensors. But, practical applications of einsum cover a much broader range of tensor expressions than those that can currently be executed efficiently. For this reason, we have created a benchmark dataset that encompasses this broad range of tensor expressions, allowing future implementations of einsum to build upon and be evaluated against. In addition, we also provide generators for einsum expression and converters to einsum expressions in our repository, so that additional data can be generated as needed. The benchmark dataset, the generators and converters are released openly and are publicly available at <a href="https://benchmark.einsum.org" target="_blank" rel="noopener">https://benchmark.einsum.org</a>.</p> <p>The broader data collection process included contributions from individuals whose data was transformed. We duly acknowledge the following for making their data publicly available:</p> <ul> <li><strong>Fichte, Johannes; Hecher, Markus; Florim Hamiti</strong>: <a href="../records/10031810" rel="nofollow">Model Counting Competition 2020</a></li> <li><strong>Fichte, Johannes; Hecher, Markus</strong>: Model Counting Competition <a href="../records/10006441" rel="nofollow">2021</a> <a href="../records/10014715" rel="nofollow">2022</a> <a href="../records/10012822" rel="nofollow">2023</a></li> <li><strong>Fichte, Johannes; Hecher, Markus; Woltran, Stefan; Zisser, Markus</strong>: <a href="../records/1299752" rel="nofollow">A Benchmark Collection of #SAT Instances and Tree Decompositions</a></li> <li><strong>Meel, Kuldeep S.</strong>: <a href="../records/3793090" rel="nofollow">Model Counting and Uniform Sampling Instances</a></li> <li><strong>Automated Reasoning Group at the University of California, Irvine</strong>: <a href="https://github.com/dechterlab/uai-competitions">UAI Competitions</a></li> <li><strong>Martinis, John M. et al.</strong>: <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.k6t1rj8" rel="nofollow">Quantum supremacy using a programmable superconducting processor Dataset. Dryad.</a></li> </ul> <p>Moreover, we thank the following authors of open source software used to generated instances:</p> <ul> <li><strong>Gray, Johnnie</strong>: <a href="https://quimb.readthedocs.io/en/latest/index.html" rel="nofollow">quimb</a>, <a href="https://cotengra.readthedocs.io/en/latest/" rel="nofollow">cotengra</a></li> <li><strong>Soos, Mate, Meel, Kuldeep S</strong>: <a href="https://github.com/meelgroup/arjun">Arjun</a></li> <li><strong>Stoian, Mihail</strong>: <a href="https://github.com/stoianmihail/Netzwerk">Netzwerk</a></li> <li><strong>Liu, Jinguo; Lua, Xiuzhe; Wang, Lei</strong>: <a href="https://github.com/QuantumBFS/Yao.jl">Yao.jl</a></li> <li><strong>Liu, Jinguo</strong>: <a href="https://github.com/QuantumBFS/YaoToEinsum.jl">YaoToEinsum.jl</a></li> </ul> <p> </p>
Precision-Engineered Metal-Organic Frameworks (PE-MOFs)
<p>This Zenodo record hosts the computationally predicted structures of 94,823 Precision-Engineered Metal-Organic Frameworks (PE-MOFs), designed using a fine-tuned Reverse Topological Approach (RTA). The structures are provided as part of a large-scale effort to systematically explore the vast combinatorial design space of metal and organic building units (BUs), pairing them based on geometric signatures and topological compatibility.</p> <p>These structures are optimized and curated for applications such as post-combustion CO2 capture.</p> <p>In this repository, you will find:</p> <p>Fully optimized structures of PE-MOFs: cif files provided in standard formats compatible with molecular simulation tools for further analysis and exploration.<br>Note: This Zenodo record only provides the computational structures. For the accompanying code, tools, and data used to generate these structures, please visit the GitHub repository here. https://github.com/xiaoyu961031/Fine-tuned-RTA</p> <p>If you use this dataset in your work, please cite our related publication:<br>Wu, X., Jiang, J. (2024). Precision-engineered metal-organic frameworks: Fine-tuning reverse topological structure prediction and design. Chemical Science, 2024, DOI: 10.1039/D4SC05616G </p>
3D Laser Scanning Data: Public Square in Murcia and Engineering Laboratory at the University of Alicante
<p>This dataset includes 3D terrestrial laser scans obtained using the Leica C10 ScanStation. The data covers two distinct scenarios:</p> <ol> <li> <p><strong>Public Square in Murcia Capital</strong>: This dataset includes two scan positions within a public square located in Murcia. Three HDTarget markers were placed, and their center or vertex coordinates are provided in the accompanying _vertices.txt file. The scans were conducted with the laser scanner leveled, but they are not registered.</p> </li> <li> <p><strong>Engineering Laboratory at the University of Alicante</strong>: This dataset consists of two scans of the Ground Engineering Laboratory at the University of Alicante. The scans were conducted with the same leveled laser scanner, and no targets were used. Between the two scans, some elements in the laboratory were slightly moved, which can be identified by comparing the point clouds.</p> </li> </ol>
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
(supplementary material) Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation
<div> <div> <div> <div>Supplementary material for paper <strong>"Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation"</strong></div> <div> </div> <div> <div> <div>The script for the paper can be found in this GitHub repository: https://github.com/awsm-research/LLM-for-code-review-automatiton</div> </div> </div> </div> </div> </div>
Supplementary Material - Women's Journey in STEM Education in Brazil: A Rapid Review on Engineering and Computer Science
<p>Supplementary Material for the Rapid Review - Women's Journey in STEM Education in Brazil in Engineering and Computer Science courses</p>
Harmonising Contributions: Exploring Diversity in Software Engineering through CQA Mining on Stack Overflow – Replication Package
<p>Community question-and-answering platforms dedicated to software engineering, such as Stack Overflow, have assumed indispensable roles in fostering a thriving global knowledge ecosystem. As these platforms suffer from diversity-related issues, investigating the underlying reasons behind such challenges becomes imperative to devise potential intervention strategies.</p> <p>The proposed study highlights Stack Overflow users’ contribution profiles, both in isolation and relative to various diversity metrics, including GDP and access to electricity. Finally, the study explores whether these contribution profiles extend to the city and state levels.</p> <p>This replication package complements our study, prompting future scholars to further examine our research process or conduct follow up analyses.</p>
Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output
<p>Research data supporting the findings of "<em>Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output</em>" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.</p>
Replication Package: Navigating the Complexity of Generative AI Adoption in Software Engineering
<p>This paper explores the adoption of Generative Artificial Intelligence (AI) tools and Large Language Models (LLMs) within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a structured interview study with 100 software engineers, drawing upon the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theoretical frameworks. Employing the Gioia Methodology, we derived a preliminary theoretical model of AI adoption in software: the Human-AI Collaboration and Adaptation Framework (HACAF). This model was then validated using Partial Least Squares – Structural Equation Modeling (PLS-SEM) based on data from 183 software professionals. Our research unveils the complex dynamics at play in AI adoption within software engineering. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.</p>
Dataset about Reproducibility in Software Engineering Research: A Systematic Mapping Study
<p>This artifact contains the results collected in one Systematic Mapping Study (SMS), about Reproducibility in Software Engineering Research. The results are associated with the selected studies and with the research questions considered.</p>
Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>
Input data of the multi-patch geometries used in: A. Farahat, H. M. Verhelst, J. Kiendl, M. Kapl, Isogeometric analysis for multi-patch structured Kirchhoff–Love shells, Computer Methods in Applied Mechanics and Engineering 411 (2023) 116060 DOI: 10.1016/j.cma.2023.116060
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Data related to feedstock supply, logistics and engineering work for Pilot 5 (Biosolvents) in WaysTUP! project
<p><span>The objectives of this dataset were to describe the feedstock supply, logistics and engineering work carried out for PILOT 5. A description of the design, construction and final start-up of the pilot is also included. </span><span>PILOT 5 is located in Athens (Greece). Feedstock material is source-separated biowaste provided by the Municipality of Vari-Voula-Vouliagmeni under the supervision of SUST. The necessary logistics for continuous and stable feedstock supply were arranged by SUST and verified by NTUA. </span><span>PILOT 5 is a pre-existing installation developed in the framework of the LIFE WASTE2BIO project (LIFE11 ENV/GR/000949 and</span><span> is installed in the premises of NTUA</span><span>. For the purposes of the WaysTUP!, this prototype plant was upgraded in order to meet the project’s needs. </span><span>It includes a dehydration unit, a bioconversion unit and a distillation unit for the recovery of the produced ethanol. The working volume of the bioreactors is 400L and up to</span><span> </span><span>80kg/d dried biowaste could be treated depending on the selected operational parameters. </span></p>
FIGURE 3 in Mixture formation in a partially stratified directly injected natural gas engine
FIGURE 3: The effect of varied gall density (low vs high) on leaves of T. cordata and T. tomentosa infested by P. tetratrichus on the total level of flavonols (a), anthocyanins (b) and tannins (c). Different letters (lower case for T. cordata and capital for T. tomentosa) above the bars indicate statistically significant differences among treatments (Kruskal-Wallis non-parametric test or Tukey's HSD test, P=0.05). The asterisks indicate statistically significant differences among the control treatments for T. cordata and T. tomentosa (Student's t-test; * – P<0.05; ** – P<0.01). Means SD of untransformed data are shown.
FIGURE 2 in Mixture formation in a partially stratified directly injected natural gas engine
FIGURE 2: The cross-sections through the edgerolling on T. cordata (a – d) and erineum on T. tomenosa (e-f) leaves at the early phase of development (a-c) and at the more expanded phase (d – f). Phenolics (deep-red in colour) are localised in the cells of the outer layer of the nutritive tissue of both gall types. Starch grains (arrow) dominate within the cells of hypertrophied parenchyma (d). The P. tetratrichus specimens are localised within distinguishable cavities of roll-gall (b, c, d). The presence of red coloured deposits within eriophyoid bodies (d, arrow) suggests that phenolics can be sequestered. Magnification: 90x (a, b, c, e, f); 180x (d).
FIGURE 1 in Mixture formation in a partially stratified directly injected natural gas engine
FIGURE 1: The effect of P. tetratrichus feeding on linden leaf morphology: edgerollings on T. cordata leaves at a low (a) and high (b) density and erinea on T. tomentosa leaves at a low (c) and high (d) density visible from the upper side of the leaf blade.
Artifacts of the Article: "Guidelines for Using Financial Incentives in Software-Engineering Experimentation"
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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)
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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.