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Answer:
Derived traits shared among the species or other groups in a dataset are key to helping us build trees. As shown above, shared derived traits tend to form nested patterns that provide information about when branching events occurred in the evolution of the species.
Explanation:
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It would be the pubis bone.
Answer:
A superbug refers to a germ that has formed resistance to multiple drugs that once treated the infection caused by the germ. The term “superbug” was developed by the media. While any germ may become a superbug, bacterial and fungal strains that routinely infect humans, animals, and crops are most likely to do so.
Superbugs are strains of bacteria that are resistant to several types of antibiotics. ... And the overuse and misuse of antibiotics helps to create drug-resistant bacteria. Here's how that might happen. When used properly, antibiotics can help destroy disease-causing bacteria.
Answer:
Results
We systematically analyze and compare how different modelling methodologies can be used to describe translation. We define various statistically equivalent codon-based simulation algorithms and analyze the importance of the update rule in determining the steady state, an aspect often neglected. Then a novel probabilistic Boolean network (PBN) model is proposed for modelling translation, which enjoys an exact numerical solution. This solution matches those of numerical simulation from other methods and acts as a complementary tool to analytical approximations and simulations. The advantages and limitations of various codon-based models are compared, and illustrated by examples with real biological complexities such as slow codons, premature termination and feedback regulation. Our studies reveal that while different models gives broadly similiar trends in many cases, important differences also arise and can be clearly seen, in the dependence of the translation rate on different parameters. Furthermore, the update rule affects the steady state solution.
Conclusions
The codon-based models are based on different levels of abstraction. Our analysis suggests that a multiple model approach to understanding translation allows one to ascertain which aspects of the conclusions are robust with respect to the choice of modelling methodology, and when (and why) important differences may arise. This approach also allows for an optimal use of analysis tools, which is especially important when additional complexities or regulatory mechanisms are included. This approach can provide a robust platform for dissecting translation, and results in an improved predictive framework for applications in systems and synthetic biology.
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