Deep Exploration 2.1 Full Version [PATCHED]
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The comprehensive manual describes methodologies for data acquisition, processing, reporting, and archiving for ship-based exploratory ocean mapping operations deeper than 200 meters (656 feet). In the context of the U.S. national strategy and Seabed 2030, OER is sharing this manual as a contribution to broader efforts to develop standard ocean mapping protocols and to serve as a guide for other interested public and private entities conducting deepwater mapping and exploration.
TensorRT provides INT8 using quantization-aware training and post-training quantization and FP16 optimizations for deployment of deep learning inference applications, such as video streaming, recommendations, fraud detection, and natural language processing. Reduced-precision inference significantly minimizes latency, which is required for many real-time services, as well as autonomous and embedded applications.
Russian state-run press agency TASS reported on Thursday, Sept. 7, that Roscosmos wants to make Vostochny capable of launching super-heavy rockets with interplanetary spacecraft. Therefore, the cosmodrome could be used in the future for a variety of space launches, including deep space exploration missions.
Moreover, Russian President Vladimir Putin revealed on earlier Thursday that Russia is interested in sending these deep space missions from Vostochny in cooperation with other countries. In particular, Roscosmos is especially interested in the exploration of Mars by 2030 jointly with U.S. partners. The space agency announced that it will inform the public about specific agreements on these missions when more details are available.
Tomasz Nowakowski is the owner of Astro Watch, one of the premier astronomy and science-related blogs on the internet. Nowakowski reached out to SpaceFlight Insider in an effort to have the two space-related websites collaborate. Nowakowski's generous offer was gratefully received with the two organizations now working to better relay important developments as they pertain to space exploration.
Active Sensor. A measuring instrument in the earth exploration-satellite service or in the space research service by means of which information is obtained by transmission and reception of radio waves. (RR)
Passive Sensor. A measuring instrument in the earth exploration-satellite service or in the space research service by means of which information is obtained by reception of radio waves of natural origin. (RR)
Get 6 special bottles of orange wine every month with info about the wine and pairing suggestions. Explore a wide range of orange wines from all over the world. From sparkling to light-bodied to medium and full-bodied. From tropical fruit to savory umami wines. The 6 bottle box is a monthly deep exploration.
The Deep Space Gateway offers new opportunities for fundamental and applied scientific research. Although primarily driven by human exploration goals, the Deep Space Gateway could support research on life sciences, physical sciences, astronomy, Earth sciences, planetary sciences, or technology.
If research is to be performed on this strategic platform then it must be designed and prepared with an understanding of the capabilities that enable this research. The research opportunities will be limited to those that have limited impact on design and resource requirements. The platform will be optimised to perform the exploration enabling tasks for which it has been defined. Research enabling capabilities may then be incorporated into the system where programmatic, operational, and technical parameters allow.
The development of computational methods to predict three-dimensional (3D) protein structures from the protein sequence has proceeded along two complementary paths that focus on either the physical interactions or the evolutionary history. The physical interaction programme heavily integrates our understanding of molecular driving forces into either thermodynamic or kinetic simulation of protein physics16 or statistical approximations thereof17. Although theoretically very appealing, this approach has proved highly challenging for even moderate-sized proteins due to the computational intractability of molecular simulation, the context dependence of protein stability and the difficulty of producing sufficiently accurate models of protein physics. The evolutionary programme has provided an alternative in recent years, in which the constraints on protein structure are derived from bioinformatics analysis of the evolutionary history of proteins, homology to solved structures18,19 and pairwise evolutionary correlations20,21,22,23,24. This bioinformatics approach has benefited greatly from the steady growth of experimental protein structures deposited in the Protein Data Bank (PDB)5, the explosion of genomic sequencing and the rapid development of deep learning techniques to interpret these correlations. Despite these advances, contemporary physical and evolutionary-history-based approaches produce predictions that are far short of experimental accuracy in the majority of cases in which a close homologue has not been solved experimentally and this has limited their utility for many biological applications.
We demonstrate in Fig. 2a that the high accuracy that AlphaFold demonstrated in CASP14 extends to a large sample of recently released PDB structures; in this dataset, all structures were deposited in the PDB after our training data cut-off and are analysed as full chains (see Methods, Supplementary Fig. 15 and Supplementary Table 6 for more details). Furthermore, we observe high side-chain accuracy when the backbone prediction is accurate (Fig. 2b) and we show that our confidence measure, the predicted local-distance difference test (pLDDT), reliably predicts the Cα local-distance difference test (lDDT-Cα) accuracy of the corresponding prediction (Fig. 2c). We also find that the global superposition metric template modelling score (TM-score)27 can be accurately estimated (Fig. 2d). Overall, these analyses validate that the high accuracy and reliability of AlphaFold on CASP14 proteins also transfers to an uncurated collection of recent PDB submissions, as would be expected (see Supplementary Methods 1.15 and Supplementary Fig. 11 for confirmation that this high accuracy extends to new folds).
The AlphaFold network directly predicts the 3D coordinates of all heavy atoms for a given protein using the primary amino acid sequence and aligned sequences of homologues as inputs (Fig. 1e; see Methods for details of inputs including databases, MSA construction and use of templates). A description of the most important ideas and components is provided below. The full network architecture and training procedure are provided in the Supplementary Methods.
We also use a variant of axial attention within the MSA representation. During the per-sequence attention in the MSA, we project additional logits from the pair stack to bias the MSA attention. This closes the loop by providing information flow from the pair representation back into the MSA representation, ensuring that the overall Evoformer block is able to fully mix information between the pair and MSA representations and prepare for structure generation within the structure module.
Although AlphaFold has a high accuracy across the vast majority of deposited PDB structures, we note that there are still factors that affect accuracy or limit the applicability of the model. The model uses MSAs and the accuracy decreases substantially when the median alignment depth is less than around 30 sequences (see Fig. 5a for details). We observe a threshold effect where improvements in MSA depth over around 100 sequences lead to small gains. We hypothesize that the MSA information is needed to coarsely find the correct structure within the early stages of the network, but refinement of that prediction into a high-accuracy model does not depend crucially on the MSA information. The other substantial limitation that we have observed is that AlphaFold is much weaker for proteins that have few intra-chain or homotypic contacts compared to the number of heterotypic contacts (further details are provided in a companion paper39). This typically occurs for bridging domains within larger complexes in which the shape of the protein is created almost entirely by interactions with other chains in the complex. Conversely, AlphaFold is often able to give high-accuracy predictions for homomers, even when the chains are substantially intertwined (Fig. 5b). We expect that the ideas of AlphaFold are readily applicable to predicting full hetero-complexes in a future system and that this will remove the difficulty with protein chains that have a large number of hetero-contacts.
In particular, AlphaFold is able to handle missing the physical context and produce accurate models in challenging cases such as intertwined homomers or proteins that only fold in the presence of an unknown haem group. The ability to handle underspecified structural conditions is essential to learning from PDB structures as the PDB represents the full range of conditions in which structures have been solved. In general, AlphaFold is trained to produce the protein structure most likely to appear as part of a PDB structure. For example, in cases in which a particular stochiometry, ligand or ion is predictable from the sequence alone, AlphaFold is likely to produce a structure that respects those constraints implicitly.
To quantify the effect of the different sequence data sources, we re-ran the CASP14 proteins using the same models but varying how the MSA was constructed. Removing BFD reduced the mean accuracy by 0.4 GDT, removing Mgnify reduced the mean accuracy by 0.7 GDT, and removing both reduced the mean accuracy by 6.1 GDT. In each case, we found that most targets had very small changes in accuracy but a few outliers had very large (20+ GDT) differences. This is consistent with the results in Fig. 5a in which the depth of the MSA is relatively unimportant until it approaches a threshold value of around 30 sequences when the MSA size effects become quite large. We observe mostly overlapping effects between inclusion of BFD and Mgnify, but having at least one of these metagenomics databases is very important for target classes that are poorly represented in UniRef, and having both was necessary to achieve full CASP accuracy. 2b1af7f3a8