Vgg16 Backbone
Vgg16 Backbone - Related Videos
08:48
Development of a Backbone Cyclic Peptide Library as Potential Antiparasitic Therapeutics Using Microwave Irradiation
0 Views •
Cited by 10 •
2016
A simple and general method for the synthesis of cyclic peptides using microwave irradiation is outlined. This procedure enables the synthesis of backbone cyclic peptides with a collection of different conformations while retaining the side chains and the pharmacophoric moieties., and therefore, allows to screen for the bioactive conformation.
10:31
Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability
0 Views •
Cited by 6 •
2022
To overcome the limitations of classical site-directed mutagenesis, proline analogs with specific modifications were incorporated into several fluorescent proteins. We show how the replacement of hydrogen by fluorine or of the single by double bonds in proline residues ("molecular surgery") affects fundamental protein properties, including their folding and interaction with light.
02:10
The Nucleosome Core Particle
0 Views •
2020
Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins. The paradox Nucleosomes, paradoxically, perform two opposite functions simultaneously. On the one hand, their main responsibility is to protect the delicate DNA strands from physical damage and help achieve a higher compaction ratio. While on the other hand, they must allow polymerase enzymes to access DNA...
08:33
Hydrogel Synthesis
0 Views •
2023
Source: Amber N. Barron, Ashlea Patterson, and Taylor D. Sparks, Department of Materials Science and Engineering, The University of Utah, Salt Lake City, UT Hydrogels are a versatile class of cross-linked polymers produced through relatively simple procedures and with generally inexpensive materials. They can be formed from solution and involve a polymer backbone formed from monomer reagents, an initiator which makes the polymer reactive and a crosslinking species which binds the polymer chains...
14:44
Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
0 Views •
Cited by 4 •
2013
The NMR-solution structure of a metallochaperone model peptide with Cu (I) was determined, and a detailed protocol from sample preparation and 1D and 2D data collection to a three-dimensional structure is described.
FAQs
Its repeated small convolutions let the network build features progressively: early processing can represent edges and textures, while later processing forms higher-level visual representations. The five max-pooling stages intervene by reducing spatial resolution as this hierarchy develops. This arrangement gives engineers a clear reference for tracing how image information changes before a task-specific prediction head.
Each max-pooling stage reduces spatial resolution, so later layers receive representations that are progressively less detailed in their spatial layout. That reduction is part of the backbone’s hierarchy rather than an isolated preprocessing step: it accompanies the transition from lower-level image patterns toward higher-level representations. Engineers therefore assess pooling when balancing feature abstraction against retained image detail.
The architecture exposes a practical trade-off between richer hierarchical representation and computational demands. Its 13 convolutional layers and five pooling stages provide a consistent sequence for extracting increasingly high-level information, while spatial reduction changes the amount of image detail carried forward. In engineering studies, this regularity supports comparison of feature quality, training requirements, and efficiency rather than treating accuracy alone as the outcome.
Pretrained weights allow the feature-extraction stage to start from learned visual representations instead of requiring all features to be learned from a new engineering dataset. This is especially useful when labeled images are limited, because the backbone can supply transferable features to a task-specific head. The approach can reduce training requirements for classification, detection, segmentation, or visual-inspection workflows.
An image is passed through the convolutional and pooling stages to produce hierarchical visual features. Those features are then forwarded to a task-specific prediction head, whose role depends on the engineering objective. A practical workflow therefore separates general image representation from the final task output, making the same backbone suitable for multiple downstream applications.
The shared feature-extraction stages provide a common visual representation, while the downstream prediction head is selected or designed for the intended task. This separation lets engineers reuse the pretrained feature representation across different image-based workflows. The relevant outcome is not one universal output, but task-dependent predictions built from the same hierarchical information.
Its regular structure makes individual design elements easy to study: stacked 3 × 3 convolutions, rectified linear unit activations, and five pooling stages form a consistent feature-extraction sequence. Researchers can use that structure to examine transfer learning and accuracy-versus-computation trade-offs, while engineering teams can evaluate whether transferable features reduce training demands for their visual data.