Property such as inheritance, specification, generalization, complementarity, etc. Using analogy, we could say that fluid matrix in a human body is analogous to the data environment which the humans work and live in in an institution, or to the financial environment in a market, and to the economic environment in an economy.
Cells in a human body need oxygen, water and food carried by blood and lymph in the respiration and circulation systems respectively. This triggers an analogical reasoning in the human to be developed from analogy between cell and human. The relationship also allows certain unchanged property to be identified. It is called topo- logical invariant.
An example is the concept of constancy in cell, homeostasis in the human body, and the concept of stability in institution. All express equilibria. Equilibrium is thus a topological invariant. Homeomorphism between them deformation from one to another are therefore identifi- able, and so is topological reasoning.
It implies that we can explore how we keep stability in institution by looking at how homeostasis is maintained in the human body, and via homeo- morphism find the topologically equivalent process in institution. Another observation is control with feedback which exists in the human body i.
Cybernetics is also a topological invariant. An example on analogical and topolog- ical reasoning is as follows. A huge financial loss in an institution is analogous to a blood hemorrhage in the human body.
In hemorrhage, for example, the body, besides trying to clot the hemorrhage and indicating to CNS that water is needed, it immediately and automatically draws water and salt from the lymph to increase blood pressure for the delivery of blood to the capillaries while waiting for water supply from external source.
Lehman with Repo , can impose a wage reduction across the board, rather than just cut jobs Enron cut 20, jobs. This can be simply achieved by a policy change and observe it. Extension to an integrated biologically inspired scope We can extend Figure 3 to show the complete biological spectrum from which more ana- logical and topological properties can be identified for reasoning. Figure 4 is suggestive for investigating analogy and topology towards analogical and topo- logical reasoning further.
Take the part of amygdala alone which is involved in human irra- tional decision making driven by emotion. They connect to all 72 but six areas of the cortex where the original emotion such as greed, fraudulent intent hidden or malicious are initiated and influence the rational decisions.
The neurological, neurobiological description is still complicated. So, if we further simplify by considering the amygdala and other cortex areas as nodes and their connections are edges, the directed graphs will be much simpler. We have mentioned earlier the analogy between interstitial fluid in cells of humans Cannon, , data environment in humans of institution, financial environment in insti- tutions of market and economic environment of market in economy Keynes , Hayed We also identify equilibrium as a topological invariant among them.
We have seen numerous authors who have studied cybernetics cells Ashby, and in humans Wiener, , and extended by others in institutions Beer, , in markets Grubbstrom, and economy Hoffman, We can further identify the analogy among the supporting entities of different components bold types on the right of Figure 5. Figure 5:Sources of analogy and analogical reasoning Modeling human decisions Putting together all the above arguments, as illustrated in Figure 6, Figure 6:Mapping of perspectives to investigative approaches 1.
Perspective 1, P1. Enron case is more complicated as it involved group executives. These doctors prioritise the question of scientific method in the present context, in light of the well-known practice of scientific debate. As the Canadian doctors point out, advances in science occur when official narratives are questioned and independent paths are pursued.
One might add that this is also true of Covid vaccines—one of the most taboo subjects of discussion today; ironically, given that, in the scientific and ethical interest of public safety, it should be the most vigorously discussed. These were developed at breakneck speed Solis-Moreira , contrary to all previous scientific protocols, eschewing the normal, time-consuming and painstaking animal and human testing to ensure their safety.
Each vaccine under development must first undergo screenings and evaluations to determine which antigen should be used to invoke an immune response. This preclinical phase is done without testing on humans.
An experimental vaccine is first tested in animals to evaluate its safety and potential to prevent disease. If the vaccine triggers an immune response, it is then tested in human clinical trials in three phases.
Given that, despite this solemn reassurance by WHO, the Covid vaccines were not subjected to such supposedly time-consuming, rigorous testing, it is not at all surprising to find medical doctors taking issue with their distribution throughout the world.
Despite progress on early multidrug therapy for Covid patients, the current mandate is to immunise the world population as quickly as possible.
The lack of thorough testing in animals prior to clinical trials, and authorisation based on safety data generated during trials that lasted less than 3. Given the high rate of occurrence of adverse effects, and the wide range of types of adverse effects that have been reported to date, as well as the potential for vaccine- driven disease enhancement, Th2-immunopathology, autoimmunity, and immune evasion, there is a need for a better understanding of the benefits and risks of mass vaccination, particularly in the groups that were excluded in the clinical trials.
Despite calls for caution, the risks of SARS-CoV-2 vaccination have been minimised or ignored by health organisations and government authorities.
We appeal to the need for a pluralistic dialogue in the context of health policies, emphasising critical questions that require urgent answers if we wish to avoid a global erosion of public confidence in science and public health. It is not difficult to ascertain that there are many conflicting reports about their safety see Mallapaty and Callaway ; RT News —just two among many such reports , which underscore my claim, that a differend is at work in this fraught terrain.
In both of these reports one witnesses mutually incompatible claims, side by side, about the efficacy of the vaccines, on the one hand, and their adverse effects, on the other. For lack of space, I leave aside the related question of alternative treatments that have shown efficacy against the coronavirus, such as Ivermectin see Olivier a , among others.
What would it Take to Remove this Differend? To make matters worse, while one might argue that one side is evidently dogmatic in its claims while the other is not, both adduce at least ostensible evidence for their claims. This would arguably remove the present differend by dissolving the reasons for its emergence in the first place.
Fuellmich and his team present the faulty PCR test and the order for doctors to label any comorbidity death as a Covid death as fraud. The CDC admits that any tests over 28 cycles are not admissible for a positive reliable result. According to Article , conducting biological experiments on protected persons is a grave breach of the Convention.
Protects recipients from getting the virus This gene-therapy does not provide immunity and double-vaccinated can still catch and spread the virus. Reduces deaths from the virus infection This gene-therapy does not reduce deaths from the infection. Double-Vaccinated infected with Covid have also died. Reduces circulation of the virus This gene-therapy still permits the spread of the virus as it offers zero immunity to the virus.
Reduces transmission of the virus This gene-therapy still permits the transmission of the virus as it offers zero immunity to the virus. CA [2] : It is clear in the statistical reporting data that this experiment is resulting in death and injury yet all the politicians, drug companies and so-called experts are not making any attempt to stop this gene-therapy experiment from inflicting harm on a misinformed public.
Unlike the lives of the global elites Carter ; Castells , — , their lives have been turned upside down by everything listed and discussed in this paper, from lockdowns through PCR- tests to the pressure to be vaccinated. This does not seem, at the time of writing this paper July , to be something that is likely to change for the better soon—unless an institutional event such as the one alluded to in the discussion, above, of the international court case being pursued by Dr Reiner Fuellmich and his international team, were to yield unexpected results.
Lyotard thus does not model the different phrase regimens as a marketplace of ideas, since the existence of one phrase regimen may mean the violent silencing of another. Acknowledgement The author wishes to thank the National Research Foundation of South Africa, which contributed financially to making the research possible that led to the publication of this article.
References Astuti, I. Accessed June 21, BBC News. Accessed June 24, Berenbaum, M. Accessed July 6, Breaking News-CA. Accessed July 9, Accessed July 10, Carrington, D. Accessed April 10, Carter, Z. Accessed July 7, Castells, M. The Rise of the Network Society. Second edition.
Oxford: Wiley-Blackwell. CDC a. In the present case, CT showed massive wall thickening with prominent contrast enhancement in the mucosa. The outer layer showed edematous thickening. Upper gastrointestinal endoscopy showed edematous thickening of the stomach and diffusely distributed erosion throughout the descending duodenum. Colonoscopy showed generalized edema from the transverse colon to the rectum. The clinical imaging findings in the previous case were similar to the diffuse mural thickening on CT and denuded and erythematous mucosa on endoscopy in the present case.
The difference from the previous report was that the present patient survived with intensive supportive therapy. Therefore, the patient received 2.
In the present case, intravenous CPA was immediately stopped after the development of abdominal symptoms and the patient received 1 g CPA in total.
Whether the cumulative dose of CPA influences the length of enteritis is uncertain, but the lower total CPA dose may be one of the reasons for the survival of the present patient. Nonetheless, this case confirmed that enteritis could develop not only with oral administration but also with intravenous injection of CPA. With respect to treatment, no specific therapy exists for CPA-induced enteritis.
However, the associated tissue injury is severe and can last for months, resulting in the loss of serum proteins including albumin and gamma globulin. Therefore, aggressive supportive therapies including hyperalimentation, replenishment of albumin and gamma globulin, and PE in severe cases are important. In conclusion, severe enteritis is a rare but life-threatening adverse effect of CPA. Immediate discontinuation of CPA and persistent supportive treatment are crucial for survival.
The authors thank the staff of the department of hematology and rheumatology, Tohoku University, for helpful discussions. Advanced Search. This Article. Academic Rules and Norms of This Article. Citation of this article. Cyclophosphamide-associated enteritis presenting with severe protein-losing enteropathy in granulomatosis with polyangiitis: A case report. Corresponding Author of This Article. Publishing Process of This Article. Research Domain of This Article.
Gastroenterology and Hepatology. This was despite previous successes in chess, first achieved by the IBM Deep Blue system, which utilized brute-force computation. A brute-force approach to chess is possible in part due to its relatively modest state-space van den Herik et al. Although DeepMind has yet to employ reinforcement learning in a publicly disclosed version of AlphaFold , its reliance on the pattern-recognition capabilities of neural networks was key to tackling the scale of protein conformation space.
Helping matters was the fact that protein conformation space is not arbitrarily complex but is instead strongly constrained by biophysically realizable stable configurations, of which evolution has in turn explored only a subset. This still vast but more constrained conformation space represents the natural umwelt for machine learning. Games provide an ideal environment for training and assessing learning methods by virtue of having a clear winning score, which yields an unambiguous objective function.
Protein structure prediction, unusually for many biological problems, has a similarly well defined objective function in terms of metrics that describe structural agreement between predicted and experimental structures. This led to the creation of a biennial competition CASP for assessing computational methods in a blind fashion. Multiple metrics of success are used in CASP, each with different tradeoffs, but in combination they provide a comprehensive assessment of prediction quality.
AlphaGo was initially trained using recorded human games, but ultimately it achieved superhuman performance by learning from machine self-play Silver et al.
It is fortunate that these structures sufficiently cover fold space to train a program of AlphaFold 2's capabilities, but it does raise questions about the applicability of the AlphaFold 2 approach to other polymers, most notably RNA. Furthermore, much of the success of AlphaFold 2 rests on large amounts of genomic data, as the other essential inputs involve sequence alignments of homologous protein families Gao et al.
Finally, despite the similarities between protein structure prediction and Go, there exists a profound difference in the ultimate objectives. Many of the advances in structure prediction over the past two decades were first demonstrated in CASP experiments, which run every two years and focus on the prediction of protein structure. Typically, sequences of recently solved structures not yet publicly released or of structures in the process of being solved are presented to prediction groups with a three-week deadline for returning predictions Kryshtafovych et al.
In all cases, CASP targets are chosen for their ability to stress the capabilities of modern prediction systems: a recently solved structure from the PDB chosen at random will on average be easier to predict than most CASP targets, including TBM targets. CASP predictions are assessed using multiple metrics that quantify different aspects of structural quality, from global topology to hydrogen bonding. After a period of rapid progress in the first few CASP challenges, characterized by homology modeling and fragment assembly, progress in protein structure prediction slowed during the late s and early s.
Much of the progress over the past decade has been driven by two ideas: the development of co-evolutionary methods De Juan et al. Starting with CASP12 in , both approaches began to show significant progress. When considering only backbone accuracy i. When considering all side-chain atoms, AlphaFold 2 achieves an r.
Despite the single-domain focus of CASP14, the publicly released version of AlphaFold 2 appears capable of predicting structures of full-length proteins, although inter-domain arrangement remains a challenge Fig. Three of these were components of oligomeric complexes, and two had structures determined by NMR. Poor performance on an oligomer may reflect the fact that AlphaFold 2 was trained to predict individual protein structures a different category exists for multimeric targets , reflecting the focus of CASP on predicting the structures of single-domain proteins.
On the one hand, it may be reflective of NMR structures being less accurate than those derived from X-ray crystallography. On the other hand, it may result from the dominance of crystallographic structures in the AlphaFold 2 training data; in which case, AlphaFold 2 is best understood as a predictor of structures under common crystallization conditions.
Confirming this hypothesis and understanding its basis may enhance our understanding of experimentally determined structures. AlphaFold 2 is almost certain to impact experimental structural determination in other ways, for example by extending the applicability of molecular replacement as a means of tackling crystallographic phasing McCoy et al.
As structural biology continues its shift towards protein complexes and macromolecular machines, particularly with the rapid growth in single-particle cryoEM, accurate in silico models of individual monomers may prove to be a valuable source of information on domains. Looking further ahead to in situ structural biology, i. In models based on multiple sequence alignments MSAs , up to and including the first version of AlphaFold Senior et al.
This serves as a source of information on spatial contacts, including contacts that are distant along the primary sequence and which play a critical role in determining 3D folds.
Inherently, such predictions are over-determined and self-inconsistent too many distances are predicted, and they can be in disagreement with each other or not physically plausible and physics-based engines are therefore necessary to resolve inconsistencies and generate realizable 3D structures some exceptions exist; AlQuraishi, b ; Ingraham, Riesselman et al. The resulting fusion of statistical MSA-based approaches with machine-learning elements and classic physics-based methods represented a critical advance in structure prediction and paved the way for the deep-learning approaches used by AlphaFold.
AlphaFold 2 departs from previous work on MSA-based structure prediction in several ways, firstly by starting with raw MSAs as inputs rather than summarized statistics, and secondly by predicting the final 3D structure rather than distograms as output Jumper et al. The attention mechanism is a key feature of the AlphaFold 2 architecture. At its core, attention enables neural networks to guide information flow by explicitly choosing and learning how to choose which aspects of the input must interact with other aspects of the input.
Attention mechanisms were first developed in the natural language-processing field Cho et al. Originally, attention was implemented as a component within architectures such as recurrent neural networks, but the most recent incarnation of the approach, so-called Transformers, have attention as the primary component of the learning system Vaswani et al. In a Transformer, every input token, for example a word in a sentence or a residue in a protein, can attend to every other input token.
This is performed through the exchange of neural activation patterns, which typically comprise the intermediate outputs of neurons in a neural network. Three types of neural activation patterns are found in Transformers: keys, queries and values. In every layer of the network, each token generates a key—query—value triplet. Keys are meant to capture aspects of the semantic identity of the token, queries are meant to capture the types of tokens that the sending token cares about, and values are meant to capture the information that each token needs to transmit.
None of these semantics are imposed on the network; they are merely intended usage patterns, and Transformers learn how best to implement key—query—value triplets based on training data. Once the keys, queries and values are generated, the query of each token is compared with the key of every other token to determine how much and what information, captured in the values, flows from one token to another.
This process is then repeated across multiple layers to enable more complex information-flow patterns. Additionally, in most Transformers, each token generates multiple key—query—value triplets.
The AlphaFold 2 trunk consists of two intertwined Transformers, one operating on the raw MSA, iteratively transforming it into abstract dependencies between residue positions and protein sequences, and another operating on homologous structural templates, iteratively transforming them into abstract dependencies between residue positions i.
If no structural templates are available, AlphaFold 2 starts with a blank slate. The two Transformers are not independent, but update each other through specialized information channels. The structure module employs a different form of Transformer. In the next sections, we discuss how these architectural features may explain the success of AlphaFold 2 and point to future developments.
To understand what motivated the use of Transformers in AlphaFold 2, it is helpful to consider the types of inductive biases that neural network architectures impose and how they coincide with our understanding of protein biophysics. Some of the main difficulties in protein modeling stem from the substantial role that long-range dependencies play in folding, as amino acids far apart in the protein chain can often be close in the folded structure.
In critical phenomena, correlation length diverges, i. For example, the distinction between liquid and gas phases disappears at criticality. This contrasts with most familiar situations in physics, where events on different spatial and temporal scales decouple. Much of our description of macroscopic systems is possible because large-scale phenomena are decoupled from the microscopic details: hydrodynamics accurately describes the motion of fluids without specifying the dynamics of every molecule in the fluid.
A major challenge in machine learning of proteins, and many other natural phenomena, is to develop architectures capable of capturing long-range dependencies. It is noteworthy that there is a significant correlation between the resolution of the structure solved and the number of cis peptides detected. Moreover, most of the refinement programs, which have been widely used in recent years such as X-PLOR 6 , only allow for the possibility of a cis conformation of an Xaa—Pro bond but will, unless specified explicitly, force any other peptide bond into the trans conformation.
Huber and Steigemann realized this as a potential problem as early as 7. In proteins in which non-proline cis peptide bonds have been unequivocally identified, they often occur at or near functionally important sites and are very likely involved in the function of the molecules. One example is coagulation factor XIII 8 , in which an Arg—Tyr cis peptide bond has been found near the active site, and a Gln—Phe cis peptide bond at the dimerization interface of the molecule. This strongly suggests a functional role for them, as has been proposed for the cis peptide bonds in carboxypeptidase A, dihydrofolate reductase and most recently in the intein gyrA 9 , In conclusion, we would like to emphasize two points: first, the importance of the cis conformation of peptide bonds in protein structures, especially if it is a Xaa—nonPro peptide bond, and second, the possibility that many cis peptide bonds may have passed unnoticed due to the limited resolution of the data and to the refinement protocol used.
Ramachandran, G. CAS Google Scholar. Schmid, F. USA 75 , — Stewart, D. MacArthur, M. Bernstein, F. Science , —
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