China 
Africa 
Explore chapters and articles related to this topic
Scope and History of Microbioiogy
Published in Maria Csuros, Csaba Csuros, Klara Ver, Microbiological Examination of Water and Wastewater, 2018
Maria Csuros, Csaba Csuros, Klara Ver
After the groundwork of microbiology, new branches were developed including immunology and virology. Most recently, the development of a set of new methods called the recombinant DNA technique is developing research and practical applications in all areas of microbiology. DNA stands for deoxyribonucleic acid, a molecule that contains the genetic code (gene).
Posthumanism: Creation of ‘New Men’ Through Technological Innovation
Published in The New Bioethics, 2021
The expression and activity of genes depends not only the genetic code in the genome, but also on the microstructure (not the code) of the DNA itself and the proteins associated with its packaging in the chromosome; the chemical state of this microstructure and associated proteins constitutes the epigenome. Epigenetics is ‘the study of the mechanisms of temporal and spatial control of gene activity during the development of complex organisms’ (Holliday 1990, 329). Changes in the epigenome can be passed on the offspring via transgenerational epigenetic inheritance (Bernstein et al. 2007). Epigenetic changes wrought by one’s diet, behaviour, or surroundings can work their way into the germ line and echo far into the future (Morgan and Whitelaw 2008). Thus, changes in the phenotype include mechanisms that do not involve alterations of the DNA sequence; consequently, traits depend both on the genome and the epigenome, and modifications of the former alone may not result in the desired traits.
Beyond geometric complexity: a critical review of complexity theory and how it relates to architecture engineering and construction
Published in Architectural Science Review, 2019
Evangelos Pantazis, David Jason Gerber
The design process can be considered as one in which the designer (i.e. architect, engineer) navigates through an ill-defined problem domain and employs various strategies to elaborate the problem description. She then iteratively generates and evaluates design alternatives and after a number of iterations, i.e. when given a time-constraint, it proposes a solution (Gero 1996). In computational terms, the design process can be described as a purposeful (not random), constrained, decision making, exploratory and learning activity. Decision making implies a set of variables that relate to the problem definition and context. Search is the common process used in decision making. Exploration here is akin to changing the problem space within which the decision making occurs. Learning implies the restructuring of knowledge based on the cycle of pre-supposition–conjecture–analysis–evaluation cycle (Gero 2000). The ill-structured nature of the design problems, the existence of changing contextual factors and the engagement of the human factor do not allow the clear definition of the solution space to be explored and therefore increase the complexity of the design process. Non-linearity and the amount of interconnected design parameters between the conjecture-analysis cycles also increase the complexity. In an attempt to improve the latter, research and professional practice have focused more on automating traditional «manual» methods of production using computer-aided design and algorithmic design tools (Gero 1996; Scheurer 2007). Current parametric design systems have facilitated the design and management of non-standard geometries and at first sight seem to reduce complexities of the design process, at least in terms of algorithmic complexity. This is easily measured if we consider that the printout of a code for a parametric model together with a table of all the parameter sets is much shorter than all the workshop drawings (Scheurer 2010). However, there are complexities relating to the description of the problem and the definition of efficient design strategies which remain largely unresolved. For instance, in biological systems the blueprint of an organism, that is its genetic code, is considered as a set of instructions which are dependent on an environmental context for its interpretation and manifestation and is subject to evolution and adaptation. In architecture, despite digital design tools were developed to streamline the production of the blueprints of buildings, less focus has been put towards formalizing the encoding process. Although architectural design processes has been computer based for over 20 years, only recently there has been rigorous research towards the adaptation of computational methods for design exploration (Von Bülow 2007). In order to leverage the power of computation, more emphasis should be put on how design abstractions can be formally described to computers algorithmically so that similar to biology, evolutionary and learning mechanisms can be used in order to extend the cognitive capacity of designers and therefore explore new design schemes or evolve existing ones based on previous knowledge and/or experience.