rives portrays modern Christianity as pagan using snippets of history, Church history and lots of eisegesis to prove his hypothesis that he is right and the rest of us are wrong. In his book he lays down the ground work seemingly taken from other authors like acharya s, various atheists and other authors to make his point.
While rives may have heard all my arguments countering his accusations and eisegesis I am not the only Christian that has sought after the answers to what rives, youngblood and many other wannabe hebrew roots clowns have poisoned the internet with false accusations and desires to see all of Christendom under the Law of Moses.
As you have already noticed I have included information about hebrew roots, judaizers and questions about the Law of Moses as counter information authored by various Christian apologetic websites. As part of my investigation of rives claims I have forwarded rives website information to each website and others who I have not included in this post their response to my queries / questions are easily read in this post.
In order to provide specific answers to Questions fellow Christians ask about rives and his doctrines I decided to buy his books in order to gain understanding about his philosophy and doctrines. My intent IS NOT TO SUPPORT rives teachings but to counter the videos and other products he willing sells on his website. While my answers are brief in this post it is my later intent on focusing a larger more informational response to rives products that hopefully will shed more light on what he is actually doing in attacking Christian culture and to a larger extent fellow Christians who are not giving in to a specific philosophy that rives believes is true. Periodically check back on my blog and see how I answer rives products at a later date.
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.
a, Backbone accuracy (lDDT-Cα) for the redundancy-reduced set of the PDB after our training data cut-off, restricting to proteins in which at most 25% of the long-range contacts are between different heteromer chains. We further consider two groups of proteins based on template coverage at 30% sequence identity: covering more than 60% of the chain (n = 6,743 protein chains) and covering less than 30% of the chain (n = 1,596 protein chains). MSA depth is computed by counting the number of non-gap residues for each position in the MSA (using the Neff weighting scheme; see Methods for details) and taking the median across residues. The curves are obtained through Gaussian kernel average smoothing (window size is 0.2 units in log10(Neff)); the shaded area is the 95% confidence interval estimated using bootstrap of 10,000 samples. b, An intertwined homotrimer (PDB 6SK0) is correctly predicted without input stoichiometry and only a weak template (blue is predicted and green is experimental).
To train, we use structures from the PDB with a maximum release date of 30 April 2018. Chains are sampled in inverse proportion to cluster size of a 40% sequence identity clustering. We then randomly crop them to 256 residues and assemble into batches of size 128. We train the model on Tensor Processing Unit (TPU) v3 with a batch size of 1 per TPU core, hence the model uses 128 TPU v3 cores. The model is trained until convergence (around 10 million samples) and further fine-tuned using longer crops of 384 residues, larger MSA stack and reduced learning rate (see Supplementary Methods 1.11 for the exact configuration). The initial training stage takes approximately 1 week, and the fine-tuning stage takes approximately 4 additional days.
Currently approved drugs for advanced HCC and timeline of pivotal clinical trials. The lines along the timeline indicate the time from the actual study start to FDA approval. The red boxes represent first-line therapies, and the green boxes represent second-line therapies
On April 27, 2017, the U.S. FDA expanded the indication for regorafenib as a second-line treatment for advanced HCC patients previously treated with sorafenib . Approval was based on a randomized, placebo-controlled international phase III trial designed to assess the safety and efficacy of regorafenib in patients with HCC progressing during sorafenib treatment (RESORCE; NCT01774344) . Regorafenib had a longer OS than placebo (mOS, 10.6 vs. 7.8 months, HR=0.63, p
Apatinib has been investigated as second-line therapy in Chinese advanced HCC patients through a phase III study AHELP (NCT02329860, Table 1), which showed significantly longer OS and PFS compared to placebo and exhibited a tolerable safety profile. The mOS was 8.7 vs. 6.8 months (HR=0.785, p=0.0476), the mPFS was 4.5 vs. 1.9 months (HR=0.471, p
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