Welcome to the World of Tomorrow: An Exploration of Cell-Based Meats and How the FDA and USDA May Protect Intellectual Property Rights

Lab-grown meats are ready to be sold in United States markets. However, the meat product needs approval from regulators such as the Food and Drug Administration (“FDA”) and the United States Department of Agriculture (“USDA”). The regulation approval process takes a significant amount of time. This approval period will cut into the lab-grown meat producers’ patent time, rendering a period of the patent ineffective. This Comment analyzes the effect of, and possible changes to, our current laws on the emerging lab-grown meat market. To look at this problem, this Comment compares FDA and USDA regulations, analyzes the Hatch-Waxman Act, and looks at what individual states have done in response to lab-grown meats. The current federal plan is to establish a joint regulation of the industry by the FDA and the USDA through a memorandum of understanding. However, this Comment shows that the FDA is better suited to handle the regulatory side of the process because of its current experience in regulating other genetically modified organisms and experience with the Hatch-Waxman Act. This Comment also explores the idea that Hatch-Waxman may need to be modified by Congress to adequately incorporate patent extensions to lab-grown meat producers. By incorporating the ideas in this Comment, the federal government will be able to effectively and efficiently handle this emerging market from a regulatory perspective, and lab-grown meat patent holders will not be negatively impacted as they wait for approval from regulatory authorities

Robust Detection of Dynamic Community Structure in Networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations (`optimization variance') and over randomizations of network structure (`randomization variance'). Because the modularity quality function typically has a large number of nearly-degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.Comment: 18 pages, 11 figure

How can we demonstrate the economic value of Precision Agriculture (PA) practices to New Zealand Agriculture service providers and arable farmers?

The amount of data collected has become a major challenge to the uptake of PA practices in New Zealand. There is a lack of clear value propositions around some PA practices, e.g. variable rate seeding (VRS). The importance of calibrating yield monitors, collecting yield data and mapping results has not been realised by farmers. The goal of the study is to provide economic evidence through yield data mining to encourage the adoption of PA

High-angle-of-attack stability characteristics of a 3-surface fighter configuration

A wind tunnel investigation was conducted to study the low speed, high angle of attack stability characteristics of a three surface fighter concept based on the F-15 configuration. Static force data were measured over angle of attack and side-slip ranges of 0 to 85 and -10 and 10 deg, respectively. A force oscillation technique was used to obtain dynamic derivatives at angles of attack from 0 to 60 deg. The tests were conducted for several canard deflections and with the canards removed to investigate the effects of the close coupled canard on the high angle of attack stability characteristics of the configuration. A fuselage strake was developed which significantly improved static lateral directional stability characteristics at high angles of attack while also increasing the maximum lift of the configuration

Diffusion and social networks: revisiting medical innovation with agents

the classic study on diffusion of Tetracycline by Coleman, Katz and Menzel (1966). Medical Innovation articulates how different patterns of interpersonal communications can influence the diffusion process at different stages of adoption. In their pioneering study, individual network (discussion, friendship or advice) was perceived as a set of disjointed pairs, and the extent of influences were therefore, evaluated for pairs of individuals. Given the existence of overlapping networks and consequent influences on doctors’ adoption decisions, the complexity of actual events was not captured by pair analysis. Subsequent reanalyses (Burt 1987, Strang and Tuma 1993, Valente 1995, Van den Bulte and Lilien 2001) failed to capture the complexity involved in the diffusion process and had a static exposure of the network structure. In this paper, for the first time, we address these limitations by combining Agent-Based Modeling (ABM) and network analysis. Based on the findings of Coleman et. al. (1966) study, we develop a diffusion model, Gammanym. Using SMALLTALK programming language, Gammanym is developed with CORMAS platform under Visual Works environment. The medical community is portrayed in an 8 X 8 spatial grid. The unit cell captures three different locations for professional interactions: practices, hospitals, and conference centers, randomly located over the spatial grid. Two social agents- Doctor and Laboratory are depicted in the model. Doctors are the principal agents in the diffusion process and are initially located at their respective practices. A doctor’s adoption decision is influenced by a random friendship network, and a professional network created through discussions with office colleagues, or hospital visits or conference attendance. A communicating agent, Laboratory, on the other hand, influences doctors’ adoption decisions by sending information through multiple channels: medical representatives or detailman visiting practices, journals sent to doctors’ practices and commercial flyers available during conferences. Doctors’ decisions to adopt a new drug involve interdependent local interactions among different entities in Gammanym. The cumulative adoption curves (Figure 1) are derived for three sets of initial conditions, based on which network topology and evolution of uptake are analyzed. The three scenarios are specified to evaluate the degree of influences by different factors in the diffusion process: baseline scenario with one seed (initial adopter), one detailman and one journal; heavy media scenario with one seed but increasing degrees of external influence, with five detailman and four journals; and integration scenario with one seed, without any external influence from the laboratory