The Architecture of Complex Systems

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Complex_Dynamical_Systems_Theory

Complex Dynamical Systems Theory This article was written by Professor Alicia Juarrero, author of Dynamics in action:intentional behavior as a complex system.Complex Dynamical Systems TheoryComplexity is a systemic property. Adaptive evolving systems like ethnic cliques or complex social situations such as “knife crimes” are best understood as dynamic networks of interactions and relationships, not mere aggregates of static entities that can by analyzed by separately identifying and enumerating them. By deﬁnition, relata do not exist in individual particles, only in their inter-relationships. In short, dynamic relations, not isolated agents, constitute the basis from which complex dynamical systems theory takes its start. Thus, instead of attempting to construct the identity and dynamics of a self-organizing network from the bottom up by identifying separate individuals and only afterwards grouping them into what the investigator hopes is the appropriate aggregate, complex systems theory proceeds by letting the dynamic patterns produced by the ﬂows and processes involved identify the speciﬁc architecture in question. Because complex systems aredi ff erentiated into interlinked levels of organization – with no preferred level of granularity - the appropriate coarseness on which to ground a model is determined by the functional task of interest.Whether in the physical or social realms, if individuals are independent or even weakly interdependent no complex physical or social structure will emerge; connectivity and interaction are necessary conditions for the emergence of complexity. No closed system can spontaneously become di ff erentiated and show complex organization, form or structure. Stated di ff erently, complexity is the order that results from the interaction among multiple agents; while particles remain separate from each other, no increase in their number will ever produce organization (Brooks and Wiley 1988). In contrast to collections of isolated elements that generate Gaussian (normal) distributions that can be understood and modeled in the traditional manner, a complex system is identiﬁed by the signature “relations among components, whether static or dynamic, that constitute a composite unity as a unity of a particular kind” (Maturana 1980). The rich interactions between real complex adaptive systems and their environment also mean that because a given domain “is connected to other domains in various ways, the e ff ects of those changes might propagate through the system and out into other domains in the world, inducing changes of various degrees on all scales… . Those e ff ects might eventually travel back and lead to the disappearance of the original domain or transform its dynamics” (Chu et al. 2003).Therefore only complex dynamical systems theory and its related disciplines and tools - network theory, agent-based modeling - provide the appropriate prism through which interdependent systems such as social groups can be understood, and coherent, integrated policy recommended.BoundariesA complex dynamical system’s internal structure consists in the patterns that result from particular objects and the interactions among them. But unlike those systems characterized by linear processes that can be e ff ectively isolated from environmental inﬂuence, the external structure or boundary conditions of complex systems are as much as part of the complex system as the internal structure; the interactions between the components and the environment, that is, “the set of all [interactions not components of the system] that act or are acted on by components of [the system]” (Bunge 1979) provides the system with a causally e ff ective external structure. Although the environment of interest is thus not the total environment but the environment that a ff ects and is a ff ected by the thing in question, the feedback provides complex systems with a contextual embeddedness that makes the boundaries of complex systems typically fuzzy and di ffi cult to demarcate.From a complexity science point of view, therefore, ethnic cliques and situations such as “knife crime”, understood as dynamic “structures of process,” are not bounded by physical or geographic boundaries. In the case of ethnicity, for example, the dynamic structure of a group no doubt extends spatially into both the group’s diaspora a well as the local communities; insofar as ancient traditions, rites and rituals continue to inform and inﬂuence present practices, the dynamical system we identify as an ethnic group also extends back in time to pre-diaspora and tribal culture.The Causality of Complex SystemsThis deep contextual embeddedness of complex systems presents additionaldi ffi culties for researchers: feedback and interactions to/from embedding domains can spread causally (not as e ffi cient causes but as context-sensitive constraints), thereby expanding the domain of the system in question and propagating unforeseen side-e ff ects uncontrollably (Chu et al.). Due to the interactions that constitute them, complex adaptive systems show not only nonlinear e ff ects, but also what is often called causal spread (Wheeler and Clark 1999), a form of causalitydi ff erent from that of the more commonly understood e ffi cient causality.The connectivity and interaction required for complex systems to self-organize, and which provides them with their contextuality and causal e ffi cacy, are best understood in terms of context-sensitive constraints (Juarrero 1999) not classical billiard-ball-like (e ffi cient) causality. First order, context-dependent constraints such as nonlinear interactions like positive feedback loops and catalysts make individuals or particles strongly interdependent by altering their marginal probability. Feedback relations with the environment recalibrate the internal dynamics of complex systems to incoming signals. Doing so embeds the system in its contextual setting by e ff ectively importing the environment into the system’s very dynamical structure. Positive feedback is a temporal context-dependent constraint insofar as it incorporates the past into a system’s present structure. Because the presence of a catalyst changes the probability of a reaction’s occurrence, catalysts also function as contextual constraints insofar as they incorporate the environment into a system’s present structure. Thus individuals or organizations who play the role of social catalysts and serve as media for feedback loops are physical embodiments of bottom-up constraints that link other individuals and organizations together and embed –tightly link—their dynamic organization to its environment and its history such that the newly formed global structure is no longer independent of either.By embodying context-sensitive dependencies, feedback and catalysts are bottom-up constraints that render a system constrained by its own past experience and its environment. Complex dynamical systems thus embody the initial conditions under which they were created; their origin and trajectory constrains their future development and evolution. Because such exquisite sensitivity to initial conditions is one of the hallmarks of complex adaptive systems, these dynamical processes are also essentially historical; in Prigogine’s words, “they carry their history on their backs,” that is, their internal structure reﬂects their history. Accordingly, self-organizing networks are “path-dependent.” Any methodology that purports to understand a given complex system while at the same time ignoring or not fully understanding either its trajectory or the overall context in which it is embedded is bound to fail. The e ff ects of context-dependent constraints, therefore, are described by conditional, not marginal, probabilities. They are, in other words, functional constraints.Once closure of ﬁrst-order context sensitive constraints occurs, the resulting global dynamics presents characteristics that aggregates or sums of individuals do not; in technical terms, context-sensitive constraints are enabling constraints insofar as they precipitate the emergence of a global dynamics with an expanded phase space. The dynamic whole has greater degrees of freedom than its components individually – a narrative can tell you more than a Q&A form can. Self-organizing networks described in stories are thus multi-level dynamical systems with emergent properties that are irreducible to their component particles. These characteristics will be ignored and missed if the analytic focus is limited solely to compartmentalized components studied in isolation from each other.Qua emergent wholes, complex systems function as the boundary conditions that actively inﬂuence the behavior of their components. Insofar as individuals – children or adults - envision themselves as caught up in a particular narrative structure, we will be able to foresee their constrained behavior. Top down, narratives act as limiting constraints that restrict the degrees of freedom of their components. Whereas from a traditional mechanistic, atomistic point of view such inﬂuence was impossible, complex dynamical systems theory allows us to understand such interlevel causal relationships – ubiquitous in social systems - in a scientiﬁcally respectable way. In complex adaptive systems, interactions among individuals weave together a story; and once a narrative coalesces in the minds of an individual, or a culture in turn, and as a global system, it actively inﬂuences the behavior of the components that make it up. Only complexity science theory provides the tools to understand this kind of bottom-up and top-down causation typical of the collective behavior of human organizations. When combined with narratives as Cognitive-Edge’s SenseMaker® allows, policy makers acquire an indispensible tool with which to map current social patterns and anticipate future trends. Without an appreciation of such global dynamics it is impossible to fully understand the inter-level organizational dynamics of social groups: interacting individuals create stories which then loop back down and alter the behavior of the very individuals that constitute them.Power LawsThe relationship between (on the one hand) the context-sensitive constraints that make complex self organization possible, and the power laws that describe such systems on the other has become clearer thanks to the research of e.g. Barabasi (2002, 2003). Since many complex systems give evidence of the same dynamics atwork on multiple levels of organization (i.e., they tend to be self-similar across levels), scalability is often a central element of complexity science. Through children’s narratives it is therefore possible to capture the dynamics of an overall ethnic or social group. Because power laws are frequently “indicative of correlated, cooperative phenomena between groups of interacting agents” (Cook et al. 2004), students of complex human systems recognize that in lieu of Gaussian statistics, linear regression models, normal distributions, etc., they must model their subject matter using the more unfamiliar tools of organizational dynamics, including Pareto distributions, fractal geometries, and the like. Since extreme cases and situations are much more important than average cases and situations to most students of the human sciences, managers, policy makers, analysts and social scientists ignore power laws (which show fat or long tails, inﬁnite variance, unstable conﬁdence intervals, etc.) at their own peril.Game TheoryApplying game theory to human complex systems, exploring rational choice strategies over time, and investigating the basis of social cooperation, are just a few examples of the increasing pervasiveness of the complexity approach. In each case, the situation is treated as an evolving dynamical system with global properties that emerge from the local interactions among the participants, and between the participants and the context in which they are embedded. Such simulation modeling can capture otherwise intractable nonlinear e ff ects and thereby reveal global patterns that would have been previously out of reach.Once the usefulness of simulation models became clear, the Asian Development Bank, for example, dropped its opposition to a centuries-old management practice when Lansing’s computer model of the complex Balinese irrigation system showed the functional role of traditional water temples bore a “close resemblance to computer simulations of optimal solutions” (Lansing 2000).AttractorsAttractors are typical patterns of dynamical, interdependent behaviors of limited dimensionality and carved out from a much larger space of possible patterns and dimensions. These global structural patterns, which emerge from interactions among the system’s components through phase space, can be characterized as emergent collectives. Social networks can be characterized and studied as attractors.Ergodic behavior patterns describe what are called a system’s attractors. Only two attractors were thought to exist: (1) The dynamics of a grandfather clock’s pendulum describe a point attractor that draws the bob to a single point in phase space regardless of its original position. Equilibrium models assume that all systems they describe are of this sort; traditional economic models were equilibrium models. Not all processes can be understood as near-equilibrium and drawn into a point attractor; ecological research revealed that predator-prey relationships described a di ff erent type of attractor, (2) a periodic attractor. Unlike phenomena characterized by point attractors, predator-prey distribution, for example, typically repeat regularly in a continuous, periodic loop. It was not until the last quarter of the twentieth century that a third type of attractor, so-called strange, chaotic or complex attractors, were discovered: patterns of behavior so convoluted that it is di ffi cult todiscern any order at all; complex human systems can often be characterized as complex attractors, of which social networks are one example.Complex attractors surprised scientists when they discovered that far from being chaotic in the old sense of the word, these complex systems are characterized by a high-dimensional degree of order. Never exactly repeating, the trajectories they trace nevertheless stay within certain bounds. Far from being chaotic in the old sense of the term, these complex behavior patterns provide evidence of highly complex, context-dependent dynamic forms of organization.Attractor LandscapesIn the 1930s biologist Sewall Wright (1932) developed a model of ﬁtness landscapes intended to capture the processes natural selection by visualizing the “switch and trigger mechanisms” that precipitate a change in a system’s evolutionary trajectory. More recently, thanks to the development of computer simulation models, the dependencies and constraints embodied by attractors can also be visualized as three dimensional adaptive landscapes depicting a series of changes in a system’s relative stability and instability over time. The increased probability that a system will occupy a particular state can be represented visually as a landscape’s wells, dips or valleys that embody attractor states and behaviors; the deeper the valley the greater the propensity of its being visited and the stronger the entrainment its attractor represents. In contrast sharp peaks are saddle points representing states and behaviors from which the system shies away. These landscape features capture the impact of context-sensitive constraints over time. The set of all states that end up in a particular attractor constitutes the attractor basin; di ff erent basins are separated from each other by basin boundaries or separatrices. A system’s identity at a particular point in time captures the signature probability distribution of its dynamics – its unique adaptive landscape, so to speak. The most useful image of complex systems is its phase space portrait: its state space carved up into basins of attraction and changing over time.Since all social phenomena are complex systems it becomes extremely important for makers of social policy to be able to map these convoluted relationships as accurately as possible. Doing so allows policy makers to map a situation’s relative volatility, as well as to explore which changes to which parameters will make the situation more or less stable. Complex dynamical mapping of this sort thus provides an invaluable visual aid in phase shift prediction. Although by their very naturecomplex systems resist precise predictability,dynamical landscapes and the mathematicalsoftware that create these visual aids alsoshow decision-makers the range of “adjacentpossible” successor states an unstablesituation is likely to tip into.Dynamic landscapes depicting a series ofchanges of relative stability and instabilityover time provide a very useful way ofvisualizing the contextual and historicalconstraints embodied in the convolutedbehavior patterns described by strangeattractors. By tweaking the various parametersand ﬁlters that produce the landscapes,dynamical mapping with SenseMaker®software can provide decision-makers, forexample, with evidence of the presence of “astable pattern overall, except for those groupsthat rank high on the combination of two scales, “retributive justice” and “anger.” These dynamical landscapes also provide evidence of probable and improbable “successor states” to a given situation, information that can be invaluable, for example, for designing a particular governmental advertisement campaigns on crime prevention etc. Dynamical mapping can prove that the intended network is possible, that it can be built; it can also providing guidance on the most appropriate criteria with which to design the most e ff ective network – or disrupt a noxious one. For example, one city’s current landscape might show that it is possible to build a particular network that assists community leaders in precipitating a particular desirable phase change with respect to criminal activity– or, conversely, it can provide decision makers with information that aids and enhances the status quo. Because complex dynamical systems are uniquely individuated, dynamical systems mapping can also provide decision makers with information about whether or not the same advertisement campaign will be as e ff ective in a di ff erent city, or a di ff erent country.If a system could access every alternative with the same frequency as every other – that is, randomly – its landscape would be smooth and ﬂat, portraying an object or a situation with no propensities or dispositions, that is, with no attractors. In contrast, the increased probability that a real system will occupy a particular state can be represented as wells – dips or valleys in a landscape – that embody attractor states and behaviors that the system is more likely to occupy. The deeper the valley the greater the propensity of being visited and the stronger the entrainment of its attractor. Dynamic landscapes thus provide governmental leaders with information about how entrenched a set of attitudes or behavior patterns are, and how best to go about preserving or changing them.Topologically, ridges separating basins of attraction are called separatrices or repellers. Sharp peaks are saddle points representing states and behaviors from which the system shies away and in all likelihood will not access; the probability of their occurrence is low or nonexistent. But if a decision-maker discovers that a system is perched on a saddle point, he can rest assured that it won’t remain in that condition very long. The height of the saddle point separating one attractor from another thus also represents the unlikelihood that the system will switch to another attractor given its history, current dynamics, and the environment. Landscape valleys thus provide decision-makers with a very good indication of whether or not a systemis locked-in to that particular condition, and what the likely “adjacent possibles” might be. The steeper the attractor’s separatrix walls, the greater the improbability of the system’s making the transition. On the other hand, the deeper the valley, the stronger the attractor’s pull, and so the stronger the perturbation that would be needed to dislodge the system from that behavior pattern. Similarly, the broader the ﬂoor of a valley the greater the variability in states and behaviors that the attractor allows under its control; conversely, the narrower the valley the more speciﬁc the attractor, that is, the fewer the states and behaviors it countenances.Complex systems theory tells us that a landscape’s valleys and peaks are neither static givens nor external control mechanisms through which we can force change. They are not determinants operating as Newtonian forces. Instead they represent constrained pathways that have been constructed and continue to be modiﬁed as a result of persistent interactions between the dynamical system and its environment. Landscapes that incorporate dynamics also provide decision makers with information about the likely direction of change, and of the critical parameters that can inﬂuence the direction of that change.Co-evolutionPredator-prey relationships taught us that the dynamical landscape of a complex system, to continue with the topographical metaphor, is not ﬁxed. A predator will evolve better eyesight to see its prey, but the prey will evolve a disguise, negating the eyesight advantage. Thus “the landscape peak the predator attempted to climb has moved from under its feet, the ﬁtness peak has shifted, the landscape has deformed due to the changes in the prey. This “coevolution” means that the ﬁtness landscape seen by one creature is a dynamic, ever changing map dependent upon the actions of everything else in its surroundings. This is true for occupants of an ecosystem or a social group. It is a highly non-linear, closely coupled system - attractors that vary in both shape and position over time” (Lucas). Co-evolution with their natural and social environment is even more so of human systems than it is of animals. In the case of human beings we are always referring, therefore, to complex adaptive systems.In other words, since ﬁtness is a relative term (relative to an environmental niche), changes in a (natural, social) niche alter the ﬁtness of the individuals and species within it; in turn, changes in the relative distribution of types of individuals and species within a niche will alter the characteristics of the niche. Thus complex adaptive systems are best characterized as adapting and co-evolving with their environment.Stability versus Resilience: The Importance of Micro-diversityComplex dynamical systems theory explains the di ff erence between stability and resilience. A stable system ﬂuctuates minimally outside its stable attractor, to which it quickly returns when perturbed. Stable systems are typically brittle; they disintegrate if highly stressed. Resilient systems, on the other hand, might ﬂuctuate wildly but have the capacity to modify their structure so as to adapt and evolve. Resilient, robust systems are also called meta-stable. Co-evolution selects for resilience, not stability.Complex adaptive systems are typically resilient. And notoriously robust to random perturbations – but exquisitely vulnerable to targeted interventions, as we will see below.Understanding what causes resilience or robustness is a central issue for analysts and policy makers. For purposes of Cultural Mapping it is particularly important to understand which speciﬁc features of the dynamical relationships that make up the knife crime statistics in the city of XYZ make the situation robust or resilient; it is important, that is, to identify the system’s dynamics that allow likely participants to adapt in response to either their own dynamics or perturbations from the outside, and thereby to evolve and persist as a network, despite the removal or incarceration of many of their members. This understanding also points to avenues for intervention by the appropriate authorities. Although still a young science, complex adaptive systems theory has begun to make inroads into understanding (1) the conditions that allow these structures evolve over time in response both to their own internal dynamics and in interaction with the environment; (2) the conditions that facilitate robustness and resilience; and (3) the most e ff ective points of intervention. Jackson & Watts (2002) note that in a network context, path resistance or network resilience is equivalent to “how many errors or mutations are needed to get from some given network to an improving path leading to another network.” Peter Allen deﬁnes microdiversity more broadly than simply errors or mutations, as “a measure of the number of qualitatively di ff erent types of entity present corresponding to individuals with di ff erent attributes.” (Garnsey & McGlade 2006, 23). Chu et al. (2003) call such systemic di ff erentiation “inhomogeneity”; they too consider it a hallmark of complexity, as do Carlson & Doyle (2002). In an important article that echoes this general point, the U.S. Naval Academy’s Robert Artigiani demonstrates through two military examples that the best way to deal with unpredictable complex systems is by organizing the system so it is maximally adaptive – when leadership cannot solve the problem in advance because no one knows what the problems will be, it is important to build systems that can solve the problem for themselves. Microdiversity in the sense of internal di ff erentiation is one way to do just that. Allen, who worked extensively with Nobel Laureate Ilya Prigogine in Brussels during the earliest years of this science, has also extensively studied how micro-diversity within a natural or social system drives the qualitative changes that occur in these systems and structures over time. Allen demonstrates that if a particular variation increases an organism’s ﬁtness, natural selection will favor that variation; following the landscape metaphor, evolutionary change – i.e., increased adaptation to the environment - is tantamount to hill-climbing.Allen’s early experiments demonstrated that hill-climbing occurs “as a result of processes of ‘di ff usion’ in character space. Using di ff usion models, Allen’s research also establishes that it is micro-diversity or internal di ff erentiation that confers resilience. Further experiments conducted by Allen and his team at Cranﬁeld University (UK) subsequently conﬁrmed that successful “evolution will be driven by the amount of diversity generation to which it leads. Evolution selects for an appropriate capacity to evolve [more exploration and innovation in novel situations; less exploration in established conditions], and this will be governed by the balance between the costs of experimental ‘failures’… and the improved performance capabilities discovered by the exploration.” The conclusion Allen draws from the research is that “organizations or individuals that can adapt and transform themselves, do so as a result of the generation of micro-diversity and the interactions with micro-contextualities” (emphasis added). The system’s complex regulatory feedback and dynamics also stop cascading failures and enable thesystem to survive (Carlson & Doyle 2002). Incorporating narrative research into dynamical landscapes is a unique and powerful tool to understand and inﬂuence social systems.Understanding how information and inﬂuence disseminate throughout a social group is a key component that cuts across storylines and issues. To the extent thatdi ff usion processes identify a property that is the inverse of robustness (both pertain to the way inﬂuence or information disseminates through, or is blocked, within particular communities by components that are di ff erent – by social mavericks, ine ff ect), identifying features in a social landscape that promote desirable robustness and resilience is a central task of any decision-maker’s mission.Fail-Safe versus Safe-FailThinkers in the ﬁeld of public policy have traditionally counseled what might be called a fail-safe strategy. From Plato to Marx, the goal was always to design forms of social organization that, because they were ideal, would remain forever in equilibrium. The traditional goal of public policy makers, in other words, has been stability, the minimization of ﬂuctuations. In stark contrast to this approach, ecologist C. S. Holling argues convincingly that if the notion of resilience applies to society at all, it counsels instead a safe-fail strategy that assumes from the outset that failures will occur despite the best-laid plans. A safe-fail strategy is one “that optimizes a cost of failure and even assures that there are periodic ‘minifailures’ to prevent evolution of inﬂexibility” (Holling 1976; Juarrero-Roque 1991). It is clear that Allen’s thesis - that evolution evolves to maximize evolvability - is another way of making the same point. Social policy should pursue a goal of resilience, not stability. As this new science develops, valuable lessons are derived from studying dynamical landscapes and the networks described in cluster graphs for the way weak ties, high betweenness links, micro-diversity, and other similar features contribute to the robustness and resilience of complex adaptive networks. In turn, these insights are can inform social organization management.。

全基因组关联分析（GWAS）取样策略

全基因组关联分析（GWAS）取样策略GWAS要想做得好，材料选择是至关重要的一环。

So，小编查阅了上百篇GWAS文献，精心梳理了一套GWAS的取样策略，是不是很贴心呢？赶紧来学习一下吧！一、常见经济作物样本选择对于经济作物来说，一般都有成百上千个品系，其中包括野生种、地方栽培种、驯化种及商业品种。

一般选择多个品系来确保群体遗传多样性。

文献中常见的经济作物的样本收集于全国或者全世界各地。

表1 常见经济作物样本收集二、常见哺乳动物样本选择对于哺乳动物，一般选择雄性个体作为研究对象（除研究产奶、产仔等性状外），并且要求所研究的对象年龄相近。

下表是我们统计的一些已发表的哺乳动物取材案例，供大家参考。

表2 常见哺乳动物样本收集三、常见家禽类样本选择对于家禽而言，一般会选择家系群体（全同胞家系或半同胞家系）。

为了增加分析内容，可以构建多个家系群体进行研究。

此外，尽量使群体所有个体生长环境以及营养程度保持一致，同时家禽的年龄也尽量保持一致，这对表型鉴定的准确性有很大的帮助。

表3 常见家禽类样本收集四、林木类样本选择对于林木类，一般选择同一物种的多个样本，多个样本做到表型丰富。

表4 林木类样本收集五、其他物种样本选择对于原生生物以及昆虫等的取样策略，可以参考表5中已发表的文献。

表5 其他物种样本收集有这么多文献支持，各位看官是不是已经整明白了GWAS该如何取材呢？最后，小编再温馨提示一句，根据文献统计及项目经验，一般来说，GWAS的样本大小要不少于300个才是极好的。

参考文献[1] Jia G, Huang X, Zhi H, et al. A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica)[J]. Nature Genetics, 2013, 45(8):957-61.[2] Zhou L, Wang S B, Jian J, et al. Identification of domestication-related loci associated with flowering time and seed size in soybean with the RAD-seqgenotyping method[J]. Scientific reports, 2015, 5.[3]Zhou Z, Jiang Y, Wang Z, et al. Resequencing 302 wild and cultivated accessions identifies genes related to domesticatio n and improvement in soybean[J]. Nature Biotechnology, 2015, 33(4):408-414.[4] MorrisG P, Ramu P, Deshpande S P, et al. Population genomic and genome-wide association studies of agroclimatic traits in sorghum[J].Proceedings of the National Academy of Sciences, 2013, 110(2): 453-458.[5] Yano K, Yamamoto E, Aya K,et al. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice[J]. Nature Genetics, 2016, 48(8).[6] Wang X, Wang H, Liu S, et al. Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings[J]. Nature Genetics, 2016.[7] Pryce J E, Bolormaa S, Chamberlain A J, et al. A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes[J]. Journal of dairy science, 2010, 93(7):3331-3345.[8] Hayes B J, Pryce J, Chamberlain A J, et al. Genetic architecture of complex traits and accuracy of genomic prediction:coat colour, milk-fat percentage, and type in Holstein cattle as contrastingmodel traits[J]. PLoS Genet, 2010, 6(9): e1001139.[9] Heaton M P, Clawson M L, Chitko-Mckown C G,et al. Reduced lentivirus susceptibility in sheep with TMEM154 mutations[J].PLoS Genet, 2012, 8(1): e1002467.[10] Tsai K L, Noorai R E, Starr-Moss A N, et al. Genome-wide association studies for multiple diseases of the German Shepherd Dog[J]. Mammalian Genome, 2012, 23(1-2): 203-211.[11] Petersen J L, Mickelson J R, Rendahl A K, et al. Genome-wide analysis reveals selection for important traits in domestic horse breeds[J]. PLoS Genet, 2013,9(1): e1003211.[12] Do D N, Strathe A B, Ostersen T, et al. Genome-wide association study reveals genetic architecture of eating behaviorin pigs and its implications for humans obesity by comparative mapping[J]. PLoS One, 2013, 8(8).[13] Daetwyler H D, Capitan A, Pausch H, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic andcomplex traits in cattle[J]. Nature genetics, 2014, 46(8): 858-865.[14] Wu Y, Fan H, Wang Y, et al. Genome-Wide Association Studies Using Haplotypes and Individual SNPs in Simmental Cattle[J]. PLoS One,2014,9(10): e109330.[15] Parker C C, Gopalakrishnan S, Carbonetto P,et al.Genome-wide association study of behavioral, physiological and gene expression traits in outbred CFW mice[J]. Nature Genetics, 2016.[16] Gu X, Feng C, Ma L, et al. Genome-wide association study of body weight in chicken F2 resource population[J]. PLoS One, 2011, 6(7): e21872.[17] Xie L, Luo C, Zhang C, et al. Genome-wide association study identified a narrow chromosome 1 region associated with chicken growth traits[J]. PLoS One, 2012, 7(2): e30910.[18] Liu R, Sun Y, Zhao G, et al. Genome-Wide Association Study Identifies Loci and Candidate Genes for Body Composition and Meat Quality Traits in Beijing-You Chickens[J]. Plos One, 2012, 8(4):-.[19] Evans L M, Slavov G T, Rodgers-Melnick E, et al. Population genomics of Populus trichocarpa identifies signatures of selection and adaptive trait associations[J]. Nature genetics, 2014.[20] Porth I, Klapšte J, Skyba O,et al. Genome‐wide association mapping for wood characteristics in Populus identifiesan array of candidate single nucleotide polymorphisms[J]. New Phytologist,2013, 200(3): 710-726.[21] Van Tyne D, Park D J, Schaffner S F, et al. Identification and functional validation of the novel antimalarial resistance locus PF10_0355 in Plasmodium falciparum[J]. PLoS Genet, 2011, 7(4): e1001383.[22] Ke C, Zhou Z, Qi W, et al. Genome-wide association study of 12 agronomic traits in peach[J]. Nature Communications,2016, 7:13246.[23] Miotto O, Amato R, Ashley E A, et al. Genetic architecture of artemisinin-resistant Plasmodium falciparum[J]. Naturegenetics, 2015, 47(3): 226-234.[24] Spötter A, Gupta P, Nürnberg G, et al. Development of a 44K SNP assay focussing on the analysis of a varroa‐specific defence behaviour in honey bees (Apis mellifera carnica)[J]. Molecular ecology resources, 2012, 12(2): 323-332.重测序业务线靳姣姣丨文案武苾菲丨编辑。

2019年6月Catti英语二级笔译真题与精编翻译答案

2019年6月Catti英语二级笔译真题与精编翻译答案2019年6月Catti英语二级笔译真题与精校翻译答案一、英译汉首篇文章In 2009, Time magazine hailed an online math program piloted at three New York City public schools, as one of the year’s 50 best innovations. Each day, the software generated individualized math “playlists” for students who then chose the “modality” in which they wished to learn —software, a virtual teacher or a flesh-and-blood one. A different algorithm sorted teache rs’ specialties and schedules to match a student’s needs. “It generates the lessons, the tests a nd it grades the tests,” one veteran instructor marveled.2009年，美国《时代》周刊对三所位于纽约市的公立学校所试行的一项在线数学课程盛赞不已，并将其称为是该年度的50大最佳创新成果之一。

该数学课程软件每天都会为学生量身定制个性化的数学课“播放列表”，而后，学生便可根据自身想要学习的内容选择适合的“模式”。

该软件因此也就成为了一名虚拟的老师，但却又展现出有血有肉的真情实感。

借助一种不同寻常的算法，该课程软件可对所有老师的专业课程与排课日程进行分门别类，以迎合每位学生的个体需求。

一名资深的老师曾为此惊叹不已并称道：“该软件不但能为学生生成课程，还能出具测验内容，更可对测验结果进行打分”。

故宫博物院简介英语作文

故宫博物院简介英语作文Introduction to the Palace MuseumThe Palace Museum, located in the heart of Beijing, China, is a magnificent architectural complex that served as the imperial palace during the Ming and Qing dynasties. It is not only a significant historical site but also one of the largest museums in the world, showcasing the rich cultural heritage of China.History:The construction of the Palace Museum, also known as the Forbidden City, began in 1406 and was completed in 1420. It was the residence of 24 emperors and played a central role in the political, cultural, and social life of China for nearly 500 years. In 1925, the palace was transformed into a museum, opening its doors to the public and allowing people to explore its vast collection of art, history, and architecture.Architecture:The Palace Museum covers an area of 180 acres and consists of 90 palaces, 8707 rooms, and countless courtyards and gardens. The entire complex is enclosed by a 26-foot-high wall and a 170-foot-wide moat, symbolizing the power and grandeur of the imperial dynasty. The architecture showcases the finest examples of traditional Chinese design, with intricate carved beams, vibrant colors, and elaborate roof decorations.Collections:The museum's collection is unparalleled, with over 1 million artifacts ranging from paintings, calligraphy, ceramics, jade, bronze, and ivory carvings to imperial robes, thrones, and everyday items used by the imperial family. Some of the most famous exhibits include the Yuanming Bowl, the largest piece of carved jade in China, and the Qing Dynasty imperial throne.Highlights:One of the main highlights of the Palace Museum is the Hall of Supreme Harmony, the largest and most important hall in the complex. It was used for imperial ceremonies, such as the coronation of emperors and the celebration of important festivals. Another must-see is the Imperial Garden, a tranquil oasis with ancient trees, pavilions, and a fish pond.Visiting:The Palace Museum is open to visitors year-round, except for Mondays. It is recommended to allocate at least half a day to explore the vast complex. Guided tours are available in various languages, providing insights into the history and significance of the museum's exhibits.Conclusion:The Palace Museum is a treasure trove of Chinese history and culture, offering visitors a glimpse into the opulence and splendor of the imperialera. Its impressive architecture, vast collections, and rich heritage make it an unforgettable experience for anyone interested in the art, history, and culture of China.。

故宫建筑英文作文

故宫建筑英文作文The Forbidden City, also known as the Palace Museum, is a magnificent architectural complex located in the heart of Beijing, China. It served as the imperial palace for 24 emperors during the Ming and Qing dynasties. The grandeurof its buildings and the exquisite craftsmanship displayedin every detail are truly awe-inspiring.Walking through the massive red walls and entering the Forbidden City feels like stepping into a different era.The architecture is a perfect blend of traditional Chinese design and imperial grandeur. The roofs adorned withintricate carvings and colorful paintings are a testamentto the skill and craftsmanship of ancient Chinese artisans.As I explore the different halls and courtyards, I am struck by the sheer size of the complex. The Forbidden City covers an area of 180 acres and is home to over 9,000 rooms. Each building has its own unique purpose, whether it's the Hall of Supreme Harmony, where important ceremonies wereheld, or the Palace of Heavenly Purity, where the emperor would rest and handle daily affairs.One of the most fascinating aspects of the Forbidden City is its symbolism. The number nine, considered the most auspicious number in Chinese culture, is prevalent throughout the complex. For example, the main halls are arranged in groups of three, representing heaven, earth, and humanity. The use of colors is also significant, with yellow being reserved for the emperor, symbolizing his supreme power.The Forbidden City is not only a testament to China's rich history and culture but also a treasure trove of artifacts and artworks. The Palace Museum houses a vast collection of over 1.8 million pieces, including imperial treasures, ceramics, paintings, and calligraphy. It is a window into the lives of the emperors and the imperial court, offering a glimpse into a bygone era.Visiting the Forbidden City is like taking a journey back in time. It is a place where history comes alive, andone can't help but be in awe of the grandeur and beauty that surrounds them. The intricate architecture, the symbolism, and the rich cultural heritage make it a must-visit destination for anyone interested in Chinese history and architecture.In conclusion, the Forbidden City is a remarkable architectural masterpiece that showcases the grandeur and opulence of China's imperial past. Its unique design, symbolism, and vast collection of artifacts make it a cultural gem that should not be missed.。

grub代码架构

grub代码架构(中英文实用版)英文文档：The Grub code architecture is a complex system that serves as the boot loader for Linux-based operating systems.It is responsible for loading the kernel into memory and initializing the system"s hardware.The architecture of Grub is designed to be modular and extensible, allowing for a wide range of features and compatibility with various hardware platforms.At the core of the Grub architecture is the Grub core, which is responsible for handling the basic functions of the boot loader, such as loading the kernel and initializing the system"s hardware.The Grub core is written in C and is designed to be lightweight and efficient.Around the Grub core, there are a number of modules that can be loaded to extend the functionality of Grub.These modules can be used to add support for different file systems, network booting, and other features.Some of the key modules included in the Grub code architecture are:- Grub menu: This module provides a graphical user interface for selecting the operating system to boot.It allows users to choose between different kernels and operating systems, as well as to access various system tools and configuration options.- Grub command line: This module provides a text-based interface for interacting with Grub.It allows users to manually load kernels and operating systems, as well as to perform various system maintenance tasks.- Grub modules: These are additional modules that can be loaded to provide support for specific hardware or file systems.They can be used to add support for older processors, to enable network booting, or to allow Grub to access files on different types of file systems.The Grub code architecture is designed to be flexible and adaptable, allowing users to customize the boot loader to suit their specific needs.With its modular design, Grub can be extended to support a wide range of hardware and file systems, making it an essential component of the Linux-based operating system.中文文档：Grub代码架构是一个复杂的系统，它作为基于Linux的操作系统启动加载器。

英语作文中考故宫

英语作文中考故宫The Forbidden City, also known as the Palace Museum, is a symbol of China's rich history and cultural heritage. Located in the heart of Beijing, this magnificent complex served as the imperial palace for 24 emperors during the Ming and Qing dynasties. With its grand architecture, beautiful gardens, and priceless artifacts, the Forbidden City is a must-visit destination for anyone interested in Chinese history and culture.As you enter the Forbidden City through the Meridian Gate, you are immediately struck by the sheer size and scale of the complex. Covering an area of 180 acres and consisting of nearly 1,000 buildings, the Forbidden City is the largest palace complex in the world. The layout of the complex is designed according to ancient Chinese principles of feng shui, with the main buildings aligned along a north-south axis and surrounded by walls and moats for protection.One of the most impressive features of the Forbidden City is its architecture. The buildings are constructed in traditional Chinese style, with yellow roofs and red walls adorned with intricate carvings and decorations. The Hall of Supreme Harmony, the Hall of Central Harmony, and the Hall of Preserving Harmony are just a few of the many stunning buildings that you will encounter as you explore the complex. Each building has its own unique history and significance, reflecting the power and grandeur of the imperial court.In addition to its architectural beauty, the Forbidden City is also home to a vast collection of cultural artifacts. The Palace Museum houses over 1.8 million works of art, including paintings, calligraphy, ceramics, jade, and furniture. Many of these artifacts date back to the Ming and Qing dynasties and provide valuable insights into the daily life and customs of the imperial court.As you wander through the halls and courtyards of the Forbidden City, you can't help but be awed by the sheer opulence and luxury of the imperial residence. From the exquisite furnishings in the living quarters to the elaborate ceremonies held in the mainhalls, every aspect of the Forbidden City reflects the wealth and power of the emperors who once ruled here.In conclusion, a visit to the Forbidden City is a journey back in time to a period of splendor and majesty. The grandeur of the architecture, the beauty of the gardens, and the richness of the cultural artifacts all combine to create an unforgettable experience. Whether you are a history buff, an art lover, or simply a curious traveler, the Forbidden City is sure to leave a lasting impression on you.。

《英语建筑介绍》课件

The uppermost part of a building that protects it from the elements.
Building Materials
Concrete Steel Wood
Strong and versatile, concrete is commonly used in construction.
Career Opportunities in Architecture
Architect
Designing and planning buildings, coordinating with clients and contractors.
Interior Designer
Creating functional and aesthetically pleasing interior spor public spaces such as government buildings, museums, and educational institutions.
Different Styles of Architecture
Traditional Chinese Architecture
Characteristics
Neoclassical architecture draws inspiration from classical Greek and Roman designs, with symmetrical facades, columns, and pediments.
Famous Examples
Urban Planner
Developing strategies to shape the growth and development of cities and communities.

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2 SMALL-WORLD NETWORKS
In ref. [16] Watts and Strogatz have shown that the connection topology of some biological, social and technological networks is neither completely regular nor completely random. These networks, that are somehow in between regular and random networks, have been named small worlds in analogy with the small world phenomenon empirically observed in social systems more than 30 years ago [12, 13]. In the mathematical formalism developed by Watts and Strogatz a generic network is represented as an unweighted graph G with N nodes (vertices) and K edges (links) between nodes. Such a graph is described by the adjacency matrix {aij}, whose entry aij is either 1 if there is an edge joining vertex i to vertex j, and 0 otherwise. The mathematical characterization of the small-world behavior is based on the evaluation of two quantities, the characteristic path length L and the clustering coeﬃcient C.
Santa Fe Institute.
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The Architecture of Complex Systems
Vito Latora1 and Massimo Marchiori2 1) Dipartimento di Fisica e Astronomia, Universita` di Catania, and INFN sezione di Catania, Catania, Italy 2) W3C and Lab. for Computer Science, Massachusetts Institute of Technology, Cambridge, USA
2.1 THE CHARACTERISTIC PATH LENGTH
The characteristic path length L measures the typical separation between two generic nodes of a graph G. L is deﬁned as:
Santa Fe Institute.
February 1, 2008 9:04 p.m. latora page 3
Vito Latora and Massimo Marchiori
3
with a ﬁnite number of steps, then dij is ﬁnite ∀i = j and also L is a ﬁnite number. For a non-connected graph, L is an ill-deﬁned quantity, because it can diverge. This problem is avoided by using Eglob in place of L.
February 1, 2008 9:04 p.m. latora page 2
2

The Architecture of Complex Systems
In the last few years the research on networks has taken diﬀerent directions producing rather unexpected and important results. Researchers have: 1) proposed various global variables to describe and characterize the properties of real-world networks; 2) developed diﬀerent models to simulate the formation and the growth of networks as the ones found in the real world. The results obtained can be summed up by saying that statistical physics has been able to capture the structure of many diverse systems within a few common frameworks, though these common frameworks are very diﬀerent from the regular array, or the random connectivity, previously used to model the network of a complex system.
L(G)
=
1 N (N −
1)
dij
i=j∈G
where dij is the shortest path length between i and j, i.e. the minimum number of edges traversed to get from a vertex i to another vertex j. By deﬁnition dij ≥ 1, and dij = 1 if there exists a direct link between i and j. Notice that if G is connected, i.e. there exists at least one path connecting any couple of vertices
Here we present a list of some of the global quantities introduced to characterize a network: the characteristic path length L, the clustering coeﬃcient C, the global eﬃciency Eglob, the local eﬃciency Eloc, the cost Cost, and the degree distribution P (k). We also review two classes of networks proposed: smallworld and scale-free networks. We conclude with a possible application of the nonextensive thermodynamics formalism to describe scale-free networks.
1The deﬁnition may seem somewhat fuzzy and generic: this is an indication that the notion of a complex system is still not precisely delineated and diﬀers from author to author. On the other side, there is complete agreement that the “ideal” complex systems are the biological ones, especially those which have to do with people: our bodies, social systems, our cultures [1].
2.2 THE CLUSTERING COEFFICIENT
The clustering coeﬃcient C is a local quantity of G measuring the average cliquishness of a node. For any node i, the subgraph of neighbors of i, Gi is considered. If the degree of i, i. e. the number of edges incident with i, is equal to ki, then Gi is made of ki nodes and at most ki(ki − 1)/2 edges. Ci is the fraction of these edges that actually exist, and C is the average value of Ci all over the network (by deﬁnition 0 ≤ C ≤ 1):
Interdisciplinary Applications of Ideas from Nonextensive. . .
edited by Murray Gell-Mann and Constantino Tsallis, Oxford University Press.