Photographs of Botanists

n/

Co-Op Living

Here is a brief summary of what some local people in two Iowa communities think about their co-ops. The authors recongize that the sample of Iowa farmers, businessmen, and civic leaders is far too small to be statistically accurate. But we think you will be interested in the observations. A more complete study, based on a state-wide sample, is now under way

Heat-killed probiotic bacteria differentially regulate colonic epithelial cell production of human β-defensin-2: dependence on inflammatory cytokines.

The inducible antimicrobial peptide human β-defensin-2 (hBD-2) stimulated by pro-inflammatory cytokines and bacterial products is essential to antipathogen responses of gut epithelial cells. Commensal and probiotic bacteria can augment such mucosal defences. Probiotic use in the treatment of inflammatory bowel disease, however, may have adverse effects, boosting inflammatory responses. The aim of this investigation was to determine the effect of selected probiotic strains on hBD-2 production by epithelial cells induced by pathologically relevant pro-inflammatory cytokines and the role of cytokine modulators in controlling hBD-2. Caco-2 colonic intestinal epithelial cells were pre-incubated with heat-killed probiotics, i.e. Lactobacillus casei strain Shirota (LcS) or Lactobacillus fermentum strain MS15 (LF), followed by stimulation of hBD-2 by interleukin (IL)-1β and tumour necrosis factor alpha (TNF-α) in the absence or presence of exogenous IL-10 or anti-IL-10 neutralising antibody. Cytokines and hBD-2 mRNA and protein were analysed by real-time quantitative polymerase chain reaction and enzyme-linked immunosorbent assay. LcS augmented IL-1β-induced hBD-2, whereas LF enhanced TNF-α- and suppressed IL-1β-induced hBD-2. LF enhanced TNF-α-induced TNF-α and suppressed IL-10, whereas augmented IL-1β-induced IL-10. LcS upregulated IL-1β-induced TNF-α mRNA and suppressed IL-10. Endogenous IL-10 differentially regulated hBD-2; neutralisation of IL-10 augmented TNF-α- and suppressed IL-1β-induced hBD-2. Exogenous IL-10, however, suppressed both TNF-α- and IL-1β-induced hBD-2; LcS partially rescued suppression in TNF-α- and IL-1β-stimulation, whereas LF further suppressed IL-1β-induced hBD-2. It can be concluded that probiotic strains differentially regulate hBD-2 mRNA expression and protein secretion, modulation being dictated by inflammatory stimulus and resulting cytokine environment

Probiotic bacterial strains differentially modulate macrophage cytokine production in a strain-dependent and cell subset-specific manner.

Gut mucosal macrophages play a pivotal role in driving mucosal immune responses, resulting in either activation of inflammatory immune responses to pathogenic challenge or tolerance to beneficial luminal contents such as food and commensal bacteria. Macrophage responses elicited are dependent on tissue environment and the resulting cell subset, where homeostatic macrophages resemble the M2 macrophage subset and inflammatory macrophages resemble M1s. Probiotics can modulate macrophage function with outcome dependent on subset present. Using a THP-1 monocyte cell line-derived model of CD14high/low M1 and M2 macrophages, the aim of this study was to investigate the immunomodulatory effects of a panel of heat-killed probiotic bacteria and their secreted proteins on the subset-specific inflammatory marker profile of TNFα, IL-6 and NFκB. M1 and M2 cells were generated by differentiation of monocyte stable transfectants for high and low CD14 expression with phorbol 12-myristate 13-acetate and vitamin D3, respectively, where the resulting CD14lo M2 and CD14hi M1s mimicked homeostatic and inflammatory mucosal macrophages. Subsets were stimulated by enteropathic lipopolysaccharides in the presence or absence of heat-killed (HK) or secreted proteins (SP) from a panel of probiotic bacteria. Regulation of cytokine expression was measured by ELISA and NFκB activity by reporter assay. HK probiotics suppress CD14lo and augment CD14hi M1 and M2 production of TNFα whereas SPs augmented CD14hi M1 TNFα and were generally suppressive in the other subtypes. M2 macrophage IL-6 production was suppressed by both HK and SPs and differentially regulated in CD14lo and CD14hi M1s. NFκB activation failed to parallel the regulatory profiles for TNFα and IL-6 which is suggestive of probiotic bacteria exerting their regulatory effects on these cytokines in an NFκB-independent manner. In conclusion, HK and SP probiotics differentially regulate macrophage cytokines and NFκB activation in a subset-dependent manner and suggest a cautionary approach to probiotic treatment of mucosal inflammation

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

A prototype system for observing the Atlantic Meridional Overturning Circulation - scientific basis, measurement and risk mitigation strategies, and first results

The Atlantic Meridional Overturning Circulation (MOC) carries up to one quarter of the global northward heat transport in the Subtropical North Atlantic. A system monitoring the strength of the MOC volume transport has been operating since April 2004. The core of this system is an array of moored sensors measuring density, bottom pressure and ocean currents. A strategy to mitigate risks of possible partial failures of the array is presented, relying on backup and complementary measurements. The MOC is decomposed into five components, making use of the continuous moored observations, and of cable measurements across the Straits of Florida, and wind stress data. The components compensate for each other, indicating that the system is working reliably. The year-long average strength of the MOC is 18.7±5.6 Sv, with wind-driven and density-inferred transports contributing equally to the variability. Numerical simulations suggest that the surprisingly fast density changes at the western boundary are partially linked to westward propagating planetary wave

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

Nonanalytic behavior of the spin susceptibility in clean Fermi systems

The wavevector and temperature dependent static spin susceptibility, \chi_s(Q,T), of clean interacting Fermi systems is considered in dimensions 1\leq d \leq 3. We show that at zero temperature \chi_s is a nonanalytic function of |Q|, with the leading nonanalyticity being |Q|^{d-1} for 1<d<3, and Q^2\ln|Q| for d=3. For the homogeneous spin susceptibility we find a nonanalytic temperature dependence T^{d-1} for 1<d<3. We give qualitative mode-mode coupling arguments to that effect, and corroborate these arguments by a perturbative calculation to second order in the electron-electron interaction amplitude. The implications of this, in particular for itinerant ferromagnetism, are discussed. We also point out the relation between our findings and established perturbative results for 1-d systems, as well as for the temperature dependence of \chi_s(Q=0) in d=3.Comment: 12pp., REVTeX, 5 eps figures, final version as publishe