The inkjet technique has the capability of generating droplets in the picoliter volume range, firing thousands of times in a few seconds and printing in the noncontact manner. Since its emergence, inkjet technology has been widely utilized in the publishing industry for printing of text and pictures. As the technology developed, its applications have been expanded from two-dimensional (2D) to three-dimensional (3D) and even used to fabricate components of electronic devices. At the end of the twentieth century, researchers were aware of the potential value of this technology in life sciences and tissue engineering because its picoliter-level printing unit is suitable for depositing biological components. Currently inkjet technology has been becoming a practical tool in modern medicine serving for drug development, scaffold building, and cell depositing. In this article, we first review the history, principles and different methods of developing this technology. Next, we focus on the recent achievements of inkjet printing in the biological field. Inkjet bioprinting of generic biomaterials, biomacromolecules, DNAs, and cells and their major applications are introduced in order of increasing complexity. The current limitations/challenges and corresponding solutions of this technology are also discussed. A new concept, biopixels, is put forward with a combination of the key characteristics of inkjet printing and basic biological units to bring a comprehensive view on inkjet-based bioprinting. Finally, a roadmap of the entire 3D bioprinting is depicted at the end of this review article, clearly demonstrating the past, present, and future of 3D bioprinting and our current progress in this field.
This paper explores the bioenergetics and potential co-evolution of denitrification and aerobic respiration. The advantages and disadvantages of combining these two pathways in a single, hybrid respiratory chain are discussed and the experimental evidence for the co-respiration of nitrate and oxygen is critically reviewed. A scenario for the co-evolution of the two pathways is presented. This article is part of a Special Issue entitled: The evolutionary aspects of bioenergetic systems.
With modern technology development, functional data are being observed frequently in many scientific fields. A popular method for analyzing such functional data is ``smoothing first, then estimation.'' That is, statistical inference such as estimation and hypothesis testing about functional data is conducted based on the substitution of the underlying individual functions by their reconstructions obtained by one smoothing technique or another. However, little is known about this substitution effect on functional data analysis. In this paper this problem is investigated when the local polynomial kernel (LPK) smoothing technique is used for individual function reconstructions. We find that under some mild conditions, the substitution effect can be ignored asymptotically. Based on this, we construct LPK reconstruction-based estimators for the mean, covariance and noise variance functions of a functional data set and derive their asymptotics. We also propose a GCV rule for selecting good bandwidths for the LPK reconstructions. When the mean function also depends on some time-independent covariates, we consider a functional linear model where the mean function is linearly related to the covariates but the covariate effects are functions of time. The LPK reconstruction-based estimators for the covariate effects and the covariance function are also constructed and their asymptotics are derived. Moreover, we propose a $L^2$-norm-based global test statistic for a general hypothesis testing problem about the covariate effects and derive its asymptotic random expression. The effect of the bandwidths selected by the proposed GCV rule on the accuracy of the LPK reconstructions and the mean function estimator is investigated via a simulation study. The proposed methodologies are illustrated via an application to a real functional data set collected in climatology.Comment: Published at http://dx.doi.org/10.1214/009053606000001505 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org
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