Effect of peas and pea products in diets for broiler chickens with consideration of the intestinal microbiota

In addition to the whole white-flowered pea, pea protein concentrates and pea hulls can be utilized in animal nutrition. In particular, fermentable carbohydrates and fibers in peas and pea products seem to contribute to intestinal health and health maintenance in poultry, due to their prebiotic effect on the intestinal microbiota. This study was conducted to investigate the effect of different proportions of peas (P), pea protein concentrate (PPC) and pea hulls (PH) in complete feed mixtures for broilers on growth and slaughter performance as well as intestinal microbiota. Twenty diets with varying proportions of peas and pea products were fed to male broilers from d 1 to 34. Short-chain fatty acid analysis and 16S sequencing were used to examine the ileal and cecal microbiota for selected feeding groups. Overall, the attained fattening performances were at a high level. The use of peas and pea products did not affect body weight on d 34 or slaughter performance. The use of pea hulls up to 6% resulted in the highest overall feed intake and overall feed conversion ratio (P < 0.001). Microbiota composition and ileal bacterial metabolites were unchanged. Microbiota changes in the cecum were found between dietary treatments for several subdominant microbial genera that preferentially ferment carbohydrates. This study has shown that peas and pea products are well-suited as feedstuffs for feeding broilers when used appropriately. Furthermore, the intestinal microbiota responded with an increased abundance of nonpathogenic genera that may help maintain intestinal microbial homeostasis

Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects

Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach

Introduction. The objects of investigation of this work are micro-level behaviors in stock markets. We aim at better understanding which strategies of market participants drive stock markets. The problem is that micro-level data from real stock markets are largely unobservable. We take an estimation perspective to obtain daily time series of fractions of chartists and fundamentalists among market participants. We estimate the heterogeneous agent-based financial market model introduced by Lux and Marchesi [1] to the S&P 500. This model has more realistic time series properties compared to less complex econometric and other agent-based models. Such kinds of models have a rather complex dependency between micro and macro parameters that have to be mapped to empirical data by the estimation method. This poses heavy computational burdens. Our contribution to this field is a new method for indirectly estimating time-varying micro-parameters of highly complex agent-based models at high frequency. Related work. Due to the high complexity, few authors have published on this topic to date (e.g., [2], [3], and [4]). Recent approaches in directly estimating agent-based models are restricted to simpler models, make simplifying assumptions on the estimation procedure, estimate only non-time varying parameters, or estimate only low frequency time series. Approach and computational methods. The indirect estimation method we propose is based on estimating the inverse model of a rich agent-based model that derives realistic macro market behavior from heterogeneous market participants’ behaviors. Applying the inverse model, which maps macro parameters back to micro parameters, to widely available macro-level financial market data, allows for estimating time series of aggregated real world micro-level strategy data at daily frequency. To estimate the inverse model in the first place, a neural network approach is used, as it allows for a large degree of freedom concerning the structure of the mapping to be represented by the neural network. As basis for learning the mapping, micro and macro time series of the market model are generated artificially using a multi-agent simulation based on RePast [5]. After applying several pre-processing and smoothing methods to these time series, a feed-forward multilayer perceptron is trained using a variant of the Levenberg-Marquardt algorithm combined with Bayesian regularization [6]. Finally, the trained network is applied to the S&P 500 to estimate daily time series of fractions of strategies used by market participants. Results. The main contribution of this work is a model-free indirect estimation approach. It allows estimating micro-parameter time series of the underlying agent-based model of high complexity at high frequency. No simplifying assumptions concerning the model or the estimation process have to be applied. Our results also contribute to the understanding of theoretical models. By investigating fundamental depen¬den¬cies in the Lux and Marchesi model by means of sensitivity analysis of the resulting neural network inverse model, price volatility is found to be a major driver. This provides additional support to findings in [1]. Some face validity for concrete estimation results obtained from the S&P 500 is shown by comparing to results of Boswijk et al. [3]. This is the work which comes closest to our approach, albeit their model is simpler and estimation frequency is yearly. We find support for Boswijk et al.’s key finding of a large fraction of chartists during the end of 1990s price bubble in technology stocks. Eventually, our work contributes to understanding what kind of micro-level behaviors drive stock markets. Analyzing correlations of our estimation results to historic market events, we find the fraction of chartists being large at times of crises, crashes, and bubbles. See also http://www.whodrivesthemarket.com for some continuously updated and derived live-results

Performance of the AMS-02 Transition Radiation Detector

For cosmic particle spectroscopy on the International Space Station the AMS experiment will be equipped with a Transition Radiation Detector (TRD) to improve particle identification. The TRD has 20 layers of fleece radiator with Xe/CO2 proportional mode straw tube chambers. They are supported in a conically shaped octagon structure made of CFC-Al-honeycomb. For low power consumption VA analog multiplexers are used as front-end readout. A 20 layer prototype built from final design components has achieved proton rejections from 100 to 2000 at 90% electron efficiency for proton beam energies up to 250 GeV with cluster counting, likelihood and neural net selection algorithms.Comment: 11 pages, 25 figures, espcrc2.sty (elsevier 2-column

Use of In Vivo-Induced Antigen Technology (IVIAT) to Identify Genes Uniquely Expressed During Human Infection with Vibrio Cholerae

In vivo-induced antigen technology is a method to identify proteins expressed by pathogenic bacteria during human infection. Sera from 10 patients convalescing from cholera infection in Bangladesh were pooled, adsorbed against in vitro-grown El Tor Vibrio cholerae O1, and used to probe a genomic expression library in Escherichia coli constructed from El Tor V. cholerae O1 strain N16961. We identified 38 positive clones in the screen, encoding pili (PilA and TcpA), cell membrane proteins (PilQ, MshO, MshP, and CapK), methyl-accepting chemotaxis proteins, chemotaxis and motility proteins (CheA and CheR), a quorum-sensing protein (LuxP), and four hypothetical proteins. Analysis of immune responses to purified PilA and TcpA in individual patients demonstrated that the majority seroconverted to these proteins, confirming results with pooled sera. These results suggest that PilA and its outer membrane secretin, PilQ, are expressed during human infection and may be involved in colonization of the gastrointestinal tract. These results also demonstrate substantial immune responses to TcpA in patients infected with El Tor V. cholerae O1. In vivo-induced antigen technology provides a simple method for identifying microbial proteins expressed during human infection, but not during in vitro growth

Experimental Induction of Odontoblast Differentiation and Stimulation During Preparative Processes

In vivo implantation experiments have shown that ethylenediaminetetraaceticacid(EDTA)-soluble frac tions of dentin stimulate reparative dentinogenesis . When isolated embryonic dental papillae were cultured in the presence of these dentin constituents, odontoblast cytological and functional differentiation could be initiated and maintained in the absence of an enamel organ. These effects were attributed to the presence of TGF-/1- related molecules [TGF-/11 or bone morphogenetic protein -2a (BMP-2a)] which had to be used in combination with an EDT A-soluble fraction of dentin in order to specifically affect competent preodontoblasts . These EDT A-soluble constituents present in dentin could be replaced by heparin or fibronectin which both have been reported to interact with TGF-/1. The association of such defined matrix components with a TGF-/1-related molecule represents a biologically active complex triggering odontoblast functional differentiation. In response to caries, odontoblasts modulate their secretory activity and are stimulated to elaborate reactionary dentin. This might be induced by active molecules such as IGF, TGF-6 or BMP which are liberated from dentin consecutively to the demineralization process. Reparative dentinogenesis is distinct from reactionary dentinogenesis and more complex since it implicates the differentiation of precursor cells present in the dental papilla. The developmental history of these cells is different from that of the physiological predontoblasts in developing teeth. The nature of these stem cells and the mechanism of their induction still remain open questions

Reynolds-number Dependence of Streamwise Velocity Fluctuations in Turbulent Pipe Flow

Statistics of the streamwise velocity component in fully-developed pipe flow are examined for Reynolds numbers in the range 5.5 x 10^4 < Re_D < 5.7 x 10^6. The second moment exhibits two maxima: one in the viscous sublayer is Reynolds-number dependent while the other, near the lower edge of the log region, is also Reynolds-number dependent and follows roughly the peak in Reynolds shear stress. The behaviour of both peaks is consistent with the concept of inactive motion which increases with increasing Reynolds number and decreasing distance from the wall. No simple scaling is apparent, and in particular, so-called "mixed" scaling is no better than wall scaling in the viscous sublayer and is actually worse than wall scaling in the outer region. The second moment is compared with empirical and theoretical scaling laws and some anomalies are apparent. The scaling of spectra using y, R and u_τ is examined. It appears that even at the highest Reynolds number, they exhibit incomplete similarity only: while spectra do collapse with either inner or outer scales for limited ranges of wave number, these ranges do not overlap. Thus similarity may not be described as complete and any apparent k_1^(-1) range does not attract any special significance and does not involve universal constants. It is suggested that this is because of the influence of inactive motion. Spectra also show the presence of very long structures close to the wall