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2,031 results for “transformation”
FIGURE 52 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURE 52. Cladistic analysis of Toxotarsinae. Single most parsimonious tree recovered with TNT in the implied weighting analysis (k = 1.3281). Tree length = 104; CI=0.577; RI=0.718. Values above the branches indicate bootstrap (BS, left) and jackknife (JK, right) supports. Values below the branches indicate the absolute Bremer (BR, left) and relative fit difference (RFD, right) supports for each branch.
FIGURES 19–24 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 19–24. Thoracic characters of Sarconesia chlorogaster and outgroup Chrysomya megacephala. 19–20. Pruinosity of scutellum. 19. C. megacephala. 20. S. chlorogaster. 21–22. Setae on ventral surface of stem of R vein, shown by arrows. 21. Absent (C. megacephala). 22. Present (S. chlorogaster). 23–24. Disposition of setae on dorsal surface of R 4+5 vein, highlighted by arrows. 23. From stem halfway to crossvein r-m (C. megacephala). 24. Only on stem (S. chlorogaster). Abbreviations: psut sct—postsutural scutum, sctl—scutellum, R 4+5 —vein R 4+5, r-m—crossvein r-m.
The ikaite to calcite transformation: Implications for palaeoclimate studies
<p>Dataset for Vickers, M.L., Vickers, M., Rickaby, R.E., Wu, H., Bernasconi, S.M., Ullmann, C.V., Bohrmann, G., Spielhagen, R.F., Kassens, H., Schultz, B.P. and Alwmark, C., 2022. The ikaite to calcite transformation: Implications for palaeoclimate studies. <em>Geochimica et Cosmochimica Acta</em>, <em>334</em>, pp.201-216.</p>
Hairy root transformation system for common bean
<p>Hairy root transformation system for common bean published in the paper named "Optimizing CRISPR/Cas9 gene editing in common bean (<em>Phaseolus vulgaris </em>L.) using a hairy root transformation system and <em>in silico</em> prediction models"</p>
A practical guide to loss measurements using the Fourier transform of the transmission spectrum
<p>Datasets of the simulations and measurements presented in the publication</p>
Characterization and identification of gentiopicrin metabolites transformed by intestinal bacteria
<p><strong>Figure captions:</strong></p> <p><strong>Fig</strong><strong>ure </strong><strong>S</strong><strong>1</strong> HPLC chromatographs of gentiopicrin incubated for 0min. (A) HPLC chromatographs of gentiopicrin incubated for 30min. (B)HPLC chromatographs of gentiopicrin incubated for 30min. (C)</p> <p><strong>Fig</strong><strong>ure </strong><strong>S2</strong> HPLC chromatographs of gentiopicrin incubated for 90min. (A) HPLC chromatographs of gentiopicrin incubated for 120min. (B)HPLC chromatographs of gentiopicrin incubated for 180min. (C)</p> <p><strong>Fig</strong><strong>ure </strong><strong>S3 </strong>The MS<sup>1 </sup>mass spectra of gentiopicrin acquired by LC/MS<sup>n</sup>-IT-TOF. (A) The MS<sup>2</sup> mass spectra of gentiopicrin acquired by LC/MS<sup>n</sup>-IT-TOF. (B) The MS<sup>1 </sup>mass spectra of metabolite G-M1 acquired by LC/MS<sup>n</sup>-IT-TOF. (C) The MS<sup>2</sup> mass spectra of metabolite G-M1 acquired by LC/MS<sup>n</sup>-IT-TOF. (D) The MS<sup>1 </sup>mass spectra of metabolite G-M3 acquired by LC/MS<sup>n</sup>-IT-TOF. (E) The MS<sup>2</sup> mass spectra of metabolite G-M3 acquired by LC/MS<sup>n</sup>-IT-TOF. (F)</p> <p><strong>Fig</strong><strong>ure </strong><strong>S4 </strong>The <sup>1</sup>H-NMR spectrum of G-M1 and G-M2<strong>.</strong></p> <p><strong>Fig</strong><strong>ure </strong><strong>S5 </strong>The <sup>13</sup>C-NMR spectrum of G-M1 and G-M2<strong>.</strong></p> <p><strong>Fig</strong><strong>ure </strong><strong>S6 </strong>The <sup>1</sup>H-<sup>1</sup>H COSY spectrum of G-M1 and G-M2<strong>.</strong></p> <p><strong>Fig</strong><strong>ure </strong><strong>S7 </strong>The HSQC spectrum of G-M1 and G-M2<strong>.</strong></p> <p><strong>Fig</strong><strong>ure </strong><strong>S8 </strong>The HMBC spectrum of G-M1 and G-M2<strong>.</strong></p> <p><strong>Figure S9 </strong>HPLC chromatographs of gentiopicrin treated immediately after incubation for 1 h. (A). HPLC chromatographs of gentiopicrin incubation solution after concentration. (B)</p> <p><strong>Fig</strong><strong>ure </strong><strong>S10 </strong>The MS<sup>1 </sup>mass spectra of metabolite G-M4 acquired by LC/MS<sup>n</sup>-IT-TOF. (A) The MS<sup>2</sup> mass spectra of metabolite G-M4 acquired by LC/MS<sup>n</sup>-IT-TOF. (B) The MS<sup>1 </sup>mass spectra of metabolite G-M5 acquired by LC/MS<sup>n</sup>-IT-TOF. (C) The MS<sup>2</sup> mass spectra of metabolite G-M5 acquired by LC/MS<sup>n</sup>-IT-TOF. (D) The MS<sup>1 </sup>mass spectra of metabolite G-M6 acquired by LC/MS<sup>n</sup>-IT-TOF. (E) The MS<sup>2</sup> mass spectra of metabolite G-M6 acquired by LC/MS<sup>n</sup>-IT-TOF. (F)</p>
Semantic Parameter Matching in Web APIs with Transformer-based Question Answering
<p>This repository contains the evaluation results of our study, as well as datasets and model checkpoints. <br> For a detailed overview regarding the provided materials, please refer to README.md.</p>
Dataset (n=1671) of the publication "Engaging small-scale private forest owners for transformative change towards integrative conservation"
<p>The excel file contains the replies of small-scale private forest owners in Lower Saxony, Germany, to certain questions of a survey analyzed within the following publication: “Engaging small-scale private forest owners for transformative change towards integrative conservation” .<br> <br> The questions and responses included in this dataset cover their objectives, management activities, perspectives, and forest stand structures. Further details are provided in the second sheet of the file called “Code explanation”.<br> <br> More information about the methods of data collection can be found in the before-mentioned publication.</p>
Transformer Models for Disconnection-Aware Triple Transformer Loop
<p>Models of the Triple Transformer Loop for retrosynthesis trained using OpenNMT.</p> <p>Full details in <a href="https://doi.org/10.1039/d3sc01604h">Chemical Science</a>.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <div> <div> <div class="highlighter--icon highlighter--icon-copy"> </div> <div class="highlighter--icon highlighter--icon-change-color"> </div> <div class="highlighter--icon highlighter--icon-delete"> </div> </div> </div>
Estimating Compositions and Nutritional Values of Seed Mixes based on Vision Transformers
<p>The cultivation of seed mixtures for local pastures is a traditional mixed cropping techniques of cereals and legumes for producing at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably both to the evolution of the European regulations on the use phyto-sanitary products, and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value. Many efforts therefore remain to be made to acquire and aggregate technical and economical references to evaluate to what extent the cultivation of seed mixtures could positively contribute to secure and reduce costs on herd feeding. The work presented in this paper proposes to evaluate recent deep learning techniques that could be transferred to an online or smartphone application to automatically estimate the nutritive value of harvested seed mixes to help farmers better managing the yield and thus engage them to promote and contribute to better knowledge of this type of cultivation. For this purpose, we have built an original image dataset containing 4,749 images of seed mixes, covering 11 seed varieties, with which we have compared 2 types of deep learning models. Our results highlight the potential of this method, and show that the best performing model is a recent state-of-the-art Vision Transformer pre-trained with self-supervision (BeiT). It allows an estimation of the nutritive value of seed mixtures with a coefficient of determination <span class="math-tex">\(R^2\)</span> Score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale.</p>
Quantum model transformation
<p>KDM to UML: A Quantum Model Transformation</p>
FedCSIS 2023 Classifying Industrial Sectors with a Domain Adapted Transformer - Datasets and Configuration files
<p>Supplementary material for the FedCSIS 2023 regular paper "Classifying Industrial Sectors from German Textual Data with a Domain Adapted Transformer". We provide the datasets containing raw .csv and .spacy binaries, mapping (i) German Wikipedia Text and (ii) Jobpostings to WZ2008 sectors and divisions. Additionally, spacy model configuration files are provided.</p>
Dataset of moment coordinate transformations
<p>The dataset used to generate the results in "Moment tracking and their coordinate transformations for macroparticles with an application to plasmas around black holes", available as a preprint at https://arxiv.org/abs/2308.01276</p> <p>PhaseSpaceData contains the data used to generate figure 5.</p> <p>SchwarzschildResults contains the data used to generate figure 7a and figure 8a.</p> <p>KruskalSzekeresResults contains the data used to generate figure 7b and 8b.</p>
Fig. 3 in Microbial transformation of capsaicin by several human intestinal fungi and their inhibitory effects against lysine-specific demethylase 1
Fig. 3. The metabolites of capsaicin transformed by Rhizopus oryzae R2701 and its hypothetical biotransformation pathway.
Fig. 1 in Microbial transformation of capsaicin by several human intestinal fungi and their inhibitory effects against lysine-specific demethylase 1
Fig. 1. The metabolites of capsaicin transformed by Aspergillus fumigatus PB4204, Aspergillus japonicus Y4009A, and the hypothetical biotransformation pathway.
Transformable nano-antibiotics for mechanotherapy and immune activation against drug-resistant Gram-negative bacteria
<p><span>The dearth of antibiotic candidates against Gram-negative bacteria and the rise of antibiotic resistance create a global health concern. The challenge lies in the unique Gram-negative bacterial outer membrane that provides the impermeable barrier for antibiotics and sequesters antigen presentation. We designed a transformable nano-antibiotics (TNA) which can transform from nontoxic nanoparticles to bactericidal nanofibrils with reasonable rigidity (Young's modulus, 21.6 ± 5.9 MPa) after targeting β-barrel assembly machine A (BamA) and lipid polysaccharides (LPS) of Gram-negative bacteria. After morphological transformation, the TNA can penetrate and damage the bacterial envelope, disrupt electron transport and multiple conserved biosynthetic and metabolic pathways, burst bacterial antigen release from the outer membrane, and subsequently activate innate and adaptive immunity. TNA kills Gram-negative bacteria in vitro and in vivo with undetectable resistance through multiple bactericidal modes of action. TNA-treatment-induced vaccination results in rapid and long-lasting immune responses, protecting against lethal re-infections.</span></p>
Fig. 5 in Transformation of 15-ene steviol by Aspergillus niger, Cunninghamella bainieri, and Mortierella isabellina
Fig. 5. Simultaneous analysis of cytokine profiles produced in cultured medium of THP-1 cells with or without lipopolysaccharide (LPS) ± 10 μM of compounds, detected with a human cytokine antibody array (Abcam, ab133997). A) Representative images. B) Quantitative data.
Fig. 6 in Transformation of 15-ene steviol by Aspergillus niger, Cunninghamella bainieri, and Mortierella isabellina
Fig. 6. Molecular docking mode of I) compounds 2, and 10–12 with glucocorticoid receptor (GR)-binding sites: a), b), c), and d) are respective binding modes of compounds 2, 10, 11, and 12 at their active site of GR; e), f), g), and h) are respective 2D ligand interaction diagrams of compounds 2, 10, 11, and 12; II) dexamethasone with GR binding. The green dashed line represents hydrogen bond and amino acid interactions; the tan amino acids represent van der Waals interactions; the pink dashed line represents alkyl and pi-alkyl interactions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots
Fig. 5. Cytotoxic activities of transformed E. lathyris root extract in human carcinoma and embryonic cell lines. a) DU-145 (prostate) b) HeLa (cervix) c) MCF-7 (breast) d) MDA-MB-231 (breast) e) and H2347 (lung) were treated with DMSO (carrier), or increasing concentrations of transformed root MeOH extracts (31.3 μg/ml, 52.5 μg/ml, 125 μg/ml, 250 μg/ml). Titer-Glo® (Promega) was used to count cell lines 48 h post-treatment. Each dose and timepoint was performed in triplicate. Asterisks indicate statistical significance in comparison to carrier (DMSO) control assessed by one-way ANOVA (**,P <0.01; *,P <0.05).
Fig. 1 in Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots
Fig. 1. Establishment and culture maintenance of transformed E. lathyris roots. (a) Stem explants of 3-week-old greenhouse grown plants were used for co-culture with A. rhizogenes. (b) Roots emerged from callus at the site of infection after 2–3 weeks.(c) Adventitious roots displaying the characteristic of the "hairy root" phenotype. (d) Growth characteristics of the isogenic root line used in this study on agar media and in (e) liquid media.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.