Accumulation of blue-green algae in the surface water of the northern Baltic, 6 August 1975, generated from the CCT-tape MSS 5 (ID 2196-0917200) by a Hertz ink-jet plotter connected to a APDP 11/40 computer at FOA 3

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The Baltic Sea

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Dynamics and energy flows in the Baltic ecosystems: Remote sensing

There are no author-identified significant results in this report

Novel roles of mechanistic target of rapamycin signaling in regulating fetal growth

Mechanistic target of rapamycin (mTOR) signaling functions as a central regulator of cellular metabolism, growth, and survival in response to hormones, growth factors, nutrients, energy, and stress signals. Mechanistic TOR is therefore critical for the growth of most fetal organs, and global mTOR deletion is embryonic lethal. This review discusses emerging evidence suggesting that mTOR signaling also has a role as a critical hub in the overall homeostatic control of fetal growth, adjusting the fetal growth trajectory according to the ability of the maternal supply line to support fetal growth. In the fetus, liver mTOR governs the secretion and phosphorylation of insulin-like growth factor binding protein 1 (IGFBP-1) thereby controlling the bioavailability of insulin-like growth factors (IGF-I and IGF-II), which function as important growth hormones during fetal life. In the placenta, mTOR responds to a large number of growth-related signals, including amino acids, glucose, oxygen, folate, and growth factors, to regulate trophoblast mitochondrial respiration, nutrient transport, and protein synthesis, thereby influencing fetal growth. In the maternal compartment, mTOR is an integral part of a decidual nutrient sensor which links oxygen and nutrient availability to the phosphorylation of IGFBP-1 with preferential effects on the bioavailability of IGF-I in the maternal-fetal interface and in the maternal circulation. These new roles of mTOR signaling in the regulation fetal growth will help us better understand the molecular underpinnings of abnormal fetal growth, such as intrauterine growth restriction and fetal overgrowth, and may represent novel avenues for diagnostics and intervention in important pregnancy complications

Model Selection in High-Dimensional Block-Sparse Linear Regression

Model selection is an indispensable part of data analysis dealing very frequently with fitting and prediction purposes. In this paper, we tackle the problem of model selection in a general linear regression where the parameter matrix possesses a block-sparse structure, i.e., the non-zero entries occur in clusters or blocks and the number of such non-zero blocks is very small compared to the parameter dimension. Furthermore, a high-dimensional setting is considered where the parameter dimension is quite large compared to the number of available measurements. To perform model selection in this setting, we present an information criterion that is a generalization of the Extended Bayesian Information Criterion-Robust (EBIC-R) and it takes into account both the block structure and the high-dimensionality scenario. The analytical steps for deriving the EBIC-R for this setting are provided. Simulation results show that the proposed method performs considerably better than the existing state-of-the-art methods and achieves empirical consistency at large sample sizes and/or at high-SNR.Comment: 5 pages, 2 figure

SimTek - an edX-based platform for Mathematical Multi-physics Simulation

[No abstract available

European farming and post-2013 CAP measures. A quantitative impact assessment

Following the paradigm for reforming the current CAP, the first objective of this study is to give insights into the economic impact of post-2013 CAP measures at different levels of aggregation (e.g. EU, Member State and region). The post-2013 CAP measures included are directed towards income for the farmers, competitiveness, valuable areas and ecosystem services. The second objective is to analyse the impact of a scenario that combines the above mentioned post-2013 CAP measures. This study can be seen as a first attempt to quantify the transition to a CAP with more targeted measures at the European level and reveals considerable methodological and data challenges. A key finding is that the impact of the various measures is very different with regard to various economic indicators.Agricultural and Food Policy,

Reconstructing phylogenetic level-1 networks from nondense binet and trinet sets

Binets and trinets are phylogenetic networks with two and three leaves, respectively. Here we consider the problem of deciding if there exists a binary level-1 phylogenetic network displaying a given set T of binary binets or trinets over a taxon set X, and constructing such a network whenever it exists. We show that this is NP-hard for trinets but polynomial-time solvable for binets. Moreover, we show that the problem is still polynomial-time solvable for inputs consisting of binets and trinets as long as the cycles in the trinets have size three. Finally, we present an O(3^{|X|} poly(|X|)) time algorithm for general sets of binets and trinets. The latter two algorithms generalise to instances containing level-1 networks with arbitrarily many leaves, and thus provide some of the first supernetwork algorithms for computing networks from a set of rooted 1 phylogenetic networks