Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

/pdf/collective-dynamics-of-small-world-networks-205zhem3gm.pdf

Collective dynamics of small-world networks

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

/pdf/statistical-mechanics-of-complex-networks-1zfjpvxipu.pdf

Statistical mechanics of complex networks

Imagined communities: Reflections on the origin and spread of nationalism

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61766-8.pdf

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

Urban Language & Literacies

The study described in the article byHauptmann et al. (1) has severalstrengths, including large size, long fol-low-up, and attempts to control for po-tentially important confounding factors.However, the study does not provideconclusive evidence of a causal associ-ation between formaldehyde exposureand leukemia for several reasons. In par-ticular, the large relative risks (RRs) re-ported for the internal comparison strat-iﬁed by exposure category need to bereconciled with the external compari-sons in which the standardized mortalityratio (SMR) for all lymphohematopoi-etic neoplasms in formaldehyde-ex-posed workers is 0.8 (95% conﬁdenceinterval [CI] 0.7 to 0.9). We haveestimated SMRs for leukemia and otherlympohematopoietic malignancies forthe peak formaldehyde exposure catego-ries shown in table 3 of Hauptmann etal. (1). These SMRs (Table 1) suggestthat mortality from lymphohematopoi-etic malignancies is not higher thanwould be expected in those workerswith a peak formaldehyde exposure of 4ppm or more, but rather is lower thanwould be expected in workers in thelowest exposure category of less than 2ppm (SMR 0.6, 95% CI 0.4 to 0.7).Similar conclusions may be drawn fromthe leukemia ﬁndings.Additionally, we point out that theassignment of peak exposure was basedprimarily on professional judgment, noton actual measurements. Such an as-signment makes this exposure the weak-est of the four exposure metrics used inthe study by Hauptmann et al. (1) rela-tive to supporting data, and this expo-sure is typically the most difﬁcult expo-sure metric to estimate in the absence ofdetailed measurements.Hauptmann et al. (1) brieﬂy discussthe biologic evidence relevant to theirhypothesis of formaldehyde-inducedlymphohematopoietic cancer, but theyconclude that the evidence is inconsis-tent. However, this conclusion is inconﬂict with substantial experimentaldata showing that, under controlledexposures, there is no increase in theconcentration of formaldehyde in theblood of humans (2 ppm), monkeys (6ppm), or rats (15 ppm) (2,3) and thatformaldehyde does not appear to in-duce cancer via inhalation at sitesother than the respiratory tract (4).These results strongly suggest that in-haled formaldehyde is rapidly metab-olized in the respiratory tract, does notreach the bone marrow, and is there-fore unlikely to induce distant-site tox-icity including leukemia.Finally, discrepancies in the datafrom available industrial studies sug-gest that the ﬁndings of Hauptmann etal. (1) may be due to chance, someuncontrolled confounding exposure, oran inappropriate comparison group.For example, a large study of workersexposed to formaldehyde in the U.K.(5) reports no increased risk for leuke-mia in the entire study cohort (SMR0.9, 95% CI 0.6 to 1.3) or amongworkers with the highest formalde-hyde exposures (SMR 0.7, 95% CI 0.3 to 1.4).Thus, we believe that the ﬁndingsof Hauptmann et al. (1) need to becritically assessed in light of externalcomparisons, existing biologic evi-dence, the ﬁndings of other studies,and a more complete understanding ofthe exposure metrics and classiﬁcationparameters used. Until these issues aremeaningfully addressed, questionswill continue to be raised about theoverall signiﬁcance of the ﬁndingsreported.M

Re: Mortality From Lymphohematopoietic Malignancies Among Workers in Formaldehyde Industries

Editorial overview: Antimicrobials: grappling with the complexities of antibiotics and resistance.

To evaluate the mortality experience of workers at a major chemical manufacturing site and to examine brain and liver cancers excesses reported at this site in previous studies. This study included 9,730 employees at the Texas City location who worked between 1940 and 2001. Standardized mortality ratios and confidence intervals were calculated. There was less than expected deaths due to all cause mortality and no increase in all cancer mortality. Brain tumor mortality, which did exceed expected rates in past years, was at expected levels in recent years. Liver cancer mortality was greater than expected. Brain tumor mortality was either due to chance in the early years or the cause of the brain tumors has been eliminated. Mortality due to liver cancer is higher than expected but only among men hired before 1950.

James J. Collins

Papers

Urban Language & Literacies

Re: Mortality From Lymphohematopoietic Malignancies Among Workers in Formaldehyde Industries

Editorial overview: Antimicrobials: grappling with the complexities of antibiotics and resistance.

Mortality among chemical workers at Texas City plant: 1940-2001.

A linguistic perspective on minority education : discourse analysis and early literacy