James J. Collins

Bio: James J. Collins is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Population. The author has an hindex of 151, co-authored 669 publications receiving 89476 citations. Previous affiliations of James J. Collins include Baylor College of Medicine & University at Albany, SUNY.

Derivation of Noncancer Reference Values for Acrylonitrile

Abstract: Dose-response assessments were conducted for the noncancer effects of acrylonitrile (AN) for the purposes of deriving subchronic and chronic oral reference dose (RfD) and inhalation reference concentration (RfC) values. Based upon an evaluation of available toxicity data, the irritation and neurological effects of AN were determined to be appropriate bases for deriving reference values. A PBPK model, which describes the toxicokinetics of AN and its metabolite 2-cyanoethylene oxide (CEO) in both rats and humans, was used to assess the dose-response data in terms of an internal dose measure for the oral RfD values, but could not be used in deriving the inhalation RfC values. Benchmark dose (BMD) methods were used to derive all reference values. Where sufficient information was available, data-derived uncertainty factors were applied to the points of departure determined by BMD methods. From this assessment, subchronic and chronic oral RfD values of 0.5 and 0.05 mg/kg/day, respectively, were derived. Similarly, subchronic and chronic inhalation RfC values of 0.1 and 0.06 mg/m 3 , respectively, were derived. Confidence in the reference values derived for AN was considered to be medium to high, based upon a consideration of the confidence in the key studies, the toxicity database, dosimetry, and dose-response modeling.

Portable, low-cost pathogen detection and strain identification platform

Abstract: Methods for detecting the presence of a pathogen infection are described. In particular, this document provides a method of detecting target nucleic acids, such as pathogen-specific RNA, in a biological sample obtained from a subject, where the method comprises using one or more toehold switch sensors and an isothermal amplification step to detect the target nucleic acid. Methods specific for detecting and identify the presence of a virus such as Zika virus are also provided.

Recombinant human insulin-like growth factor-1 therapy for 6 months improves growth but not motor function in boys with Duchenne muscular dystrophy.

Abstract: INTRODUCTION Recombinant human insulin-like growth factor-1 (rhIGF-1) is a growth factor and has anabolic effects on muscle. We investigated whether rhIGF-1 therapy: 1) improves or preserves muscle function; and 2) improves growth in boys with Duchenne muscular dystrophy (DMD). METHODS In this study we compared prepubescent, ambulatory, glucocorticoid-treated boys with DMD (n = 17) vs controls (glucocorticoid therapy only, n = 21) in a 6-month-long, prospective, randomized, controlled trial of subcutaneous rhIGF-1 therapy. The primary outcome was 6-minute walk distance (6MWD). Secondary outcomes included height velocity (HV), change in height standard deviation score (ΔHtSDS), motor function, cardiopulmonary function, body composition, insulin sensitivity, quality of life, and safety. RESULTS Change in 6MWD was similar between groups (rhIGF-1 vs controls [mean ± SD]: 3.4 ± 32.4 vs -5.1 ± 50.2 meters, P = .53). Treated subjects grew more than controls (HV: 6.5 ± 1.7 vs 3.3 ± 1.3 cm/year, P < .0001; 6-month ΔHtSDS: 0.25, P < .0001). Lean mass and insulin sensitivity increased in treated subjects. DISCUSSION In boys with DMD, 6 months of rhIGF-1 therapy did not change motor function, but it improved linear growth.

Collective dynamics of small-world networks

Abstract: 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.

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

Abstract: 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。

Statistical mechanics of complex networks

Abstract: 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.