Persistence of phytoplankton responses to different Si:N ratios under mesozooplankton grazing pressure: a mesocosm study with Northeast Atlantic plankton

We fertilised 12 mesocosms with NE Atlantic phytoplankton with different Si:N ratios (0:1 to 1:1). After 1 wk, we added mesozooplankton, mainly calanoid copepods at natural densities to 10 of the mesocosms; the remaining 2 mesocosms served as controls. A trend of increasing diatom dominance with increasing Si:N ratios and species-specific correlations of diatoms to Si:N ratios were not changed by the addition of mesozooplankton. Large unicellular and chain-forming diatoms, thin-walled dinoflagellates (Gymnodiniales) and ciliates were reduced by copepod grazing while armoured dinoflagellates remained unaffected. Nanoplanktonic flagellates and diatoms profited from the addition of copepods, probably through release from ciliate grazing

Weak localization with nonlinear bosonic matter waves

We investigate the coherent propagation of dilute atomic Bose-Einstein condensates through irregularly shaped billiard geometries that are attached to uniform incoming and outgoing waveguides. Using the mean-field description based on the nonlinear Gross-Pitaevskii equation, we develop a diagrammatic theory for the self-consistent stationary scattering state of the interacting condensate, which is combined with the semiclassical representation of the single-particle Green function in terms of chaotic classical trajectories within the billiard. This analytical approach predicts a universal dephasing of weak localization in the presence of a small interaction strength between the atoms, which is found to be in good agreement with the numerically computed reflection and transmission probabilities of the propagating condensate. The numerical simulation of this quasi-stationary scattering process indicates that this interaction-induced dephasing mechanism may give rise to a signature of weak antilocalization, which we attribute to the influence of non-universal short-path contributions.Comment: 67 pages, 19 figure

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Upper-rim monofunctionalisation in the synthesis of triazole- and disulfide-linked multicalix[4]- and -[6]arenes.

Covalently linked multiple calixarenes are valued in supramolecular chemistry. We report an easy and versatile synthetic route to covalently linked double and triple calix[4]arene and calix[6]arenes by a novel DMF‐controlled selective alkylation of a convenient and readily available upper‐rim dimethylaminomethyl‐substituted tetrahydroxy calix[4]arene and ‐[6]arenes. Synthetic routes to upper‐rim functionalised redox active disulfide‐linked double‐, tetra‐ and peptidohybrid‐calixarenes employing either redox chemistry (CH2SH) or thiolates (CH2S–) are also opened up from the same key starting material

Filarial Parasites Develop Faster and Reproduce Earlier in Response to Host Immune Effectors That Determine Filarial Life Expectancy

During larval development, filarial nematodes adjust their lifelong reproductive strategy to the presence of anti-parasitic immune cells that determine host resistance and experimental vaccine efficacy

On the Appropriateness of Negative Selection for Anomaly Detection and Network Intrusion Detection

The immune system is a complex system which protects humans and animals against diseases caused by foreign intruders such as viruses, bacteria and fungi. It appears as if the recognition and protection mechanism of the immune system can lead to the development of novel concepts and techniques for detecting intrusions in computer networks, particularly in the area of anomaly detection. In this thesis, the principle of "negative selection" as a paradigm for detecting intrusions in computer networks and anomaly detection is explored. Negative selection is a process of the immune system, which destroys immature antibodies which are capable of recognizing self-antigens. Antibodies which survive the negative selection process are self-tolerant and are capable of recognizing almost any foreign body substance. Roughly speaking one can say that the negative selection endows the immune system with an ability to distinguish between self and non-self. Abstracting the principle of negative selection, the coding antigens as bit-strings which represent network packets or as real-valued n-dimensional points and antibodies as binary detectors or as hyperspheres, one obtains an immune-inspired technique for use in the above mentioned areas of application. We are talking about artificial immune systems, when principles and processes of the immune system are abstracted and applied for solving problems. In this thesis, we explore the appropriateness of the artificial immune system negative selection for intrusion detection and anomaly detection problems. In the first instance, we describe the immune system negative selection principle, and the subsequent the artificial immune system negative selection principe. We then describe which network information are required to de- tect an intrusion. Results reveal that previous works that apply the negative selection for this application area, are not appropriate for real-world intrusion detection problems. Moreover we explore if a different antibody-antigen representations, i.e. real-valued n-dimensional points and high-dimensional hyperspheres are appropriate for anomaly detection problems. The results obtained, reveal that negative selection is not appropriate for anomaly detection problems, especially when compared to statistical anomaly detection methods. In summary, we can unfortunately state that negative selection, is not appropriate for network intrusion detection and anomaly detection problems

Is negative selection appropriate for anomaly detection

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that realvalue negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class