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Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
The unfolded protein response (UPR) is a cellular stress response related to the endoplasmic reticulum (ER) stress. [1] It has been found to be conserved between mammalian species, [2] as well as yeast [1] [3] and worm organisms. The UPR is activated in response to an accumulation of unfolded or misfolded proteins in the lumen of the
ER retention refers to proteins that are retained in the endoplasmic reticulum, or ER, after folding; these are known as ER resident proteins. Protein localization to the ER often depends on certain sequences of amino acids located at the N terminus or C terminus. These sequences are known as signal peptides, molecular signatures, or sorting ...
HIV uses an efficient mechanism to dislocate a single-membrane-spanning host protein, CD4, from the ER and submits it to ERAD. The Vpu protein of HIV-1 is a protein on the ER membrane and targets newly made CD4 in the endoplasmic reticulum for degradation by cytosolic proteasomes. [3] Vpu only utilizes part of the ERAD process to degrade CD4.
The endoplasmic reticulum is found in most eukaryotic cells and forms an interconnected network of flattened, membrane-enclosed sacs known as cisternae (in the RER), and tubular structures in the SER. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum is not found in red blood cells, or spermatozoa.
In co-translational translocation, the entire ribosome–mRNA complex binds to the outer membrane of the rough endoplasmic reticulum (ER), and the new protein is synthesized and released into the ER; the newly created polypeptide can be immediately secreted or stored inside the ER for future vesicle transport and secretion outside the cell.
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There are two closely related variants of the Erdős–Rényi random graph model. A graph generated by the binomial model of Erdős and Rényi (p = 0.01)In the (,) model, a graph is chosen uniformly at random from the collection of all graphs which have nodes and edges.