Spatial pattern and process at the plant neighbourhood scale: insights from communities dominated by the clonal grass Elymus repens (L.) Gould.

Question: How do clonal traits of a locally dominant grass (Elymus repens (L.) Gould.) respond to soil heterogeneity and shape spatial patterns of its tillers? How do tiller spatial patterns constrain seedling recruitment within the community?Locations: Artificial banks of the River Rhone, France.Material and Methods: We examined 45 vegetation patches dominated by Elymus repens. During a first phase we tested relationships between soil variables and three clonal traits (spacer length, number of clumping tillers and branching rate), and between the same clonal traits and spatial patterns (i.e. density and degree of spatial aggregation) of tillers at a very fine scale. During a second phase, we performed a sowing experiment to investigate effects of density and spatial patterns of E. repens on recruitment of eight species selected from the regional species pool.Results: Clonal traits had clear effects - especially spacer length - on densification and aggregation of E. repens tillers and, at the same time, a clear response of these same clonal traits as soil granulometry changed. The density and degree of aggregation of E. repens tillers was positively correlated to total seedling cover and diversity at the finest spatial scales.Conclusions: Spatial patterning of a dominant perennial grass responds to soil heterogeneity through modifications of its clonal morphology as a trade-off between phalanx and guerrilla forms. In turn, spatial patterns have strong effects on abundance and diversity of seedlings. Spatial patterns of tillers most probably led to formation of endogenous gaps in which the recruitment of new plant individuals was enhanced. Interestingly, we also observed more idiosyncratic effects of tiller spatial patterns on seedling cover and diversity when focusing on different growth forms of the sown species.

Evaluation of process parameters in the industrial scale production of fish nuggets

This work reports the use of experimental design for the assessment of the effects of process parameters on the production of fish nuggets in an industrial scale environment. The effect of independent factors on the physicochemical and microbiological parameters was investigated through a full 24 experimental design. The studied factors included the temperature of fish fillet and pulp in the mixer, the temperature of the added fat, the temperature of water and the ratio of protein extraction time to emulsion time. The physicochemical analyses showed that the higher temperature of the pulp and fillet of fish, the lower the protein in the final product. Microbiological analyses revealed that the counting of Staphylococcus coagulase positive, total and thermo-tolerant coliforms were in accordance with the current legislation.

MODELING DYNAMICS OF OZONE AND NITROGEN OXIDES AT SUMMIT, GREENLAND WITH A 1-D PROCESS-SCALE MODEL

This work presents a 1-D process scale model used to investigate the chemical dynamics and temporal variability of nitrogen oxides (NOx) and ozone (O3) within and above snowpack at Summit, Greenland for March-May 2009 and estimates surface exchange of NOx between the snowpack and surface layer in April-May 2009. The model assumes the surface of snowflakes have a Liquid Like Layer (LLL) where aqueous chemistry occurs and interacts with the interstitial air of the snowpack. Model parameters and initialization are physically and chemically representative of snowpack at Summit, Greenland and model results are compared to measurements of NOx and O3 collected by our group at Summit, Greenland from 2008-2010. The model paired with measurements confirmed the main hypothesis in literature that photolysis of nitrate on the surface of snowflakes is responsible for nitrogen dioxide (NO2) production in the top ~50 cm of the snowpack at solar noon for March – May time periods in 2009. Nighttime peaks of NO2 in the snowpack for April and May were reproduced with aqueous formation of peroxynitric acid (HNO4) in the top ~50 cm of the snowpack with subsequent mass transfer to the gas phase, decomposition to form NO2 at nighttime, and transportation of the NO2 to depths of 2 meters. Modeled production of HNO4 was hindered in March 2009 due to the low production of its precursor, hydroperoxy radical, resulting in underestimation of nighttime NO2 in the snowpack for March 2009. The aqueous reaction of O3 with formic acid was the major sync of O3 in the snowpack for March-May, 2009. Nitrogen monoxide (NO) production in the top ~50 cm of the snowpack is related to the photolysis of NO2, which underrepresents NO in May of 2009. Modeled surface exchange of NOx in April and May are on the order of 1011 molecules m-2 s-1. Removal of measured downward fluxes of NO and NO2 in measured fluxes resulted in agreement between measured NOx fluxes and modeled surface exchange in April and an order of magnitude deviation in May. Modeled transport of NOx above the snowpack in May shows an order of magnitude increase of NOx fluxes in the first 50 cm of the snowpack and is attributed to the production of NO2 during the day from the thermal decomposition and photolysis of peroxynitric acid with minor contributions of NO from HONO photolysis in the early morning.

Tree phenology and carbon dioxide fluxes - use of digital photography for process-based interpretation at the ecosystem scale

Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric–atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005–2007 we analyzed seasonal phenological development of 2 temperate mixed forests using tower-based imagery from standard RGB cameras. Phenological information was jointly analyzed with gross primary productivity (GPP) derived from net ecosystem exchange data. Automated image analysis provided reliable information on vegetation developmental stages of beech and ash trees covering all seasons. A phenological index derived from image color values was strongly correlated with GPP, with a significant mean time lag of several days for ash trees and several weeks for beech trees in early summer (May to mid-July). Leaf emergence dates for the dominant tree species partly explained temporal behaviour of spring GPP but were also masked by local meteorological conditions. We conclude that digital cameras at flux measurement sites not only provide an objective measure of the physiological state of a forest canopy at high temporal and spatial resolutions, but also complement CO2 and water exchange measurements, improving our knowledge of ecosystem processes.

Bench-scale evaluation of the activated sludge process for treatment of a high -strength chemical plant wastewater

A bench-scale treatability study was conducted on a high-strength wastewater from a chemical plant to develop an alternative for the existing waste stabilization pond treatment system. The objective of this study was to determine the treatability of the wastewater by the activated sludge process and, if treatable, to determine appropriate operating conditions, and to evaluate the degradability of bis(2-chloroethyl)ether (Chlorex) and benzene in the activated sludge system. Four 4-L Plexi-glass, complete mixing, continuous flow activated sludge reactors were operated in parallel under different operating conditions over a 6-month period. The operating conditions examined were hydraulic retention time (HRT), sludge retention time (SRT), nutrient supplement, and Chlorex/benzene spikes. Generally the activated sludge system treating high-strength wastewater was stable under large variations of organic loading and operating conditions. At an HRT of 2 days, more than 90% removal efficiency with good sludge settleability was achieved when the organic loading was less than 0.4 g BOD$\sb5$/g MLVSS/d or 0.8 g COD/g MLVSS/d. At least 20 days of SRT was required to maintain steady operation. Phosphorus addition enhanced the performance of the system especially during stressed operation. On the average, removals of benzene and Chlorex were 73-86% and 37-65%, respectively. In addition, the low-strength wastewater was treatable by activated sludge process, showing more than 90% BOD removal at a HRT of 0.5 days. In general, the sludge had poor settling characteristics. The aerated lagoon process treating high-strength wastewater also provided significant organic reduction, but did not produce an acceptable effluent concentration. ^

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Psychosocial stressors perceived in the process of social reintegration and compliance time remaining in prison sentence

Introduction: This study shows the results obtained from evaluating the main psychosocial stressors perceived in the process of social reintegration and their relation to a remaining sentence time in prison. Material and methods: A questionnaire based on an ad hoc design was administered, using a Likert scale, with a total of 383 inmates serving sentences in southeast Spain. Results: Findings show that inmates with a remaining sentence period of more than one year, like those who had served more than a year of their sentence, showed greater concern about possible economic difficulties. Conclusions: The psychosocial stressors studied might provide relevant information to facilitate the process of social reintegration after the completion of a prison sentence.

The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification

A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice ) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented. Copyright (C) 2009 L. H. A. Monteiro et al.

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.

Enzymatic Resolution of Racemic Sulcatol by Lipase from Candida Antarctica in a Large Scale

Large scale enzymatic resolution of racemic sulcatol 2 has been useful for stereoselective biocatalysis. This reaction was fast and selective, using vinyl acetate as donor of acyl group and lipase from Candida antarctica (CALB) as catalyst. The large scale reaction (5.0 g, 39 mmol) afforded high optical purities for S-(+)-sulcatol 2 and R-(+)-sulcatyl acetate 3, i.e., ee > 99 per cent and good yields (45 per cent) within a short time (40 min). Thermodynamic parameters for the chemoesterification of sulcatol 2 by vinyl acetate were evaluated. The enthalpy and Gibbs free energy values of this reaction were negative, indicating that this process is exothermic and spontaneous which is in agreement with the reaction obtained enzymatically.

Effect of Fill Time on the Performance of Pilot-scale ASBR and AnSBBR Applied to Sanitary Wastewater Treatment

Many lab-scale studies have been carried out regarding the effect of feed strategy on the performance of anaerobic sequencing batch reactors (ASBR); however, more detailed pilot-scale studies should be performed to assess the real applicability of this type of operation. Therefore, the objective of this work was to assess the effect of feed strategy or fill time in a 1-m(3) mechanically stirred pilot-scale sequencing batch reactor, treating 0.65 m(3) sanitary wastewater in 8-h cycles at ambient temperature. Two reactor configurations were used: one containing granular biomass (denominated ASBR) and the other immobilized biomass on polyurethane foam as inert support (denominated anaerobic sequencing batch biofilm reactor (AnSBBR)). The reactors were operated under five distinct feed strategies, namely: typical batch and fed-batch for 25%, 50%, 75%, and 100% of the cycle length. Stirring frequency in the ASBR was 40 rpm with two flat-blade turbine impellers and 80 rpm in the AnSBBR with two helix impellers. The results showed that both the ASBR and AnSBBR when operated under typical batch, fed-batch for 50% and 75% of the cycle length, presented improved organic matter removal efficiencies, without significant differences in performance, thus showing important operational flexibility. In addition, the reactors presented operation stability under all conditions.

Post-treatment of effluents from the sulfate reduction process by anaerobic sequencing batch biofilm reactors

The main objective of this research was to evaluate the potential use of a bench-scale anaerobic sequencing batch biofilm reactor (ASBBR) containing mineral coal as inert support for removal Of Sulfide and organic matter effluents from an ASBBR (1.2 m(3)) utilized for treatment of sulfate-rich wastewater. The cycle time was 48 h, including the steps of feeding (2 h), reaction with continuous liquid recirculation (44 h) and discharge (2 h). COD removal efficiency was up to 90% and the effluents total sulfide concentrations (H(2)S, HS(-), S(2-)) remained in the range of 1.5 to 7.5 mg.l(-1) during the 50 days of operation (25 cycles). The un-ionized Sulfide and ionized sulfides were converted by biological process to elemental sulfur (S(0)) under oxygen limited conditions. The results obtained in the bench-scale reactor were used to design an ASBBR in pilot scale for use in post-treatment to achieve the emission standards (sulfide and COD) for sulfate reduction. The pilot-scale reactor, with a total volume of 0.43 m(3), the COD and total sulfide removal achieved 88% and 57%, respectively, for a cycle time of 48 h (70 days of operation or 35 cycles).

Biogas production and valorization by means of a two-step biological process

The scope of this research work was to investigate biogas production and purification by a two-step bench-scale biological system, consisting of fed-batch pulse-feeding anaerobic digestion of mixed sludge, followed by methane enrichment of biogas by the use of the cyanobacterium Arthrospira platensis. The composition of biogas was nearly constant, and methane and carbon dioxide percentages ranged between 70.5-76.0% and 13.2-19.5%, respectively. Biogas yield reached a maximum value (about 0.4 m(biogas)(3)/kgCOD(i)) at 50 days-retention time and then gradually decreased with a decrease in the retention time. Biogas CO(2) was then used as a carbon source for A. platensis cultivation either under batch or fed-batch conditions. The mean cell productivity of fed-batch cultivation was about 15% higher than that observed during the last batch phase (0.035 +/- 0.006 g(DM)/L/d), likely due to the occurrence of some shading effect under batch growth conditions. The data of carbon dioxide removal from biogas revealed the existence of a linear relationship between the rates of A. platensis growth and carbon dioxide removal from biogas and allowed calculating carbon utilization efficiency for biomass production of almost 95%. (C) 2009 Elsevier Ltd. All rights reserved.

Turbulent Time and Length Scale Measurements in High-Velocity Open Channel Flows

In high-velocity open channel flows, the measurements of air-water flow properties are complicated by the strong interactions between the flow turbulence and the entrained air. In the present study, an advanced signal processing of traditional single- and dual-tip conductivity probe signals is developed to provide further details on the air-water turbulent level, time and length scales. The technique is applied to turbulent open channel flows on a stepped chute conducted in a large-size facility with flow Reynolds numbers ranging from 3.8 E+5 to 7.1 E+5. The air water flow properties presented some basic characteristics that were qualitatively and quantitatively similar to previous skimming flow studies. Some self-similar relationships were observed systematically at both macroscopic and microscopic levels. These included the distributions of void fraction, bubble count rate, interfacial velocity and turbulence level at a macroscopic scale, and the auto- and cross-correlation functions at the microscopic level. New correlation analyses yielded a characterisation of the large eddies advecting the bubbles. Basic results included the integral turbulent length and time scales. The turbulent length scales characterised some measure of the size of large vortical structures advecting air bubbles in the skimming flows, and the data were closely related to the characteristic air-water depth Y90. In the spray region, present results highlighted the existence of an upper spray region for C > 0.95 to 0.97 in which the distributions of droplet chord sizes and integral advection scales presented some marked differences with the rest of the flow.

Ecosystem process models at multiple scales for mapping tropical forest productivity

Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf-tree, plot-stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.