ARGOS - Modelling the Economic, Environmental, and Social Implications for New Zealand from Different Scenarios Relating to the Demand and Supply of Organic Products

This paper reports on some of the initial findings of the ARGOS (Agricultural Research Group on (Sustainability) programme, a 6 year quasi-experimental research project with the aim to model the economic, environmental, and social differences between organic, environmentally friendly and conventional systems of production. In the first section the paper reviews the development of organic markets, details the production costs and reports some preliminary results from ARGOS. The information is then used to develop potential future scenarios relating to the organic sector, which are assessed using the Lincoln Trade and Environment Model (LTEM), a partial equilibrium trade model that differentiates between organic and conventional production methods. This paper concentrates upon the difference between organic and conventional production, consumption and trade.sustainability, New Zealand, organic markets, ARGOS, Demand and Price Analysis, F18, Q17,

Research rationale for the economic objective, ARGOS

The primary interest of an economist is the allocation of scarce resources to satisfy infinite“wants”, and several theories on how this can be done has been developed. The mostprominent and well known of these theories are Marxism and the neo-classical approach.The dominating theme in the post war economics has been financial growth and until the1960’s, environmental concerns were of secondary importance. From then and onwards, agrowing awareness of the social and environmental costs of financial growth has fuelled anongoing debate and contributed to approaches in economics that explicitly recognises socialand environmental aspects of the economic context.Also these “new” theories differ in the way they propose society should go about allocatingour scarce resources to different uses. However, in our opinion, they share a commonobjective, i.e. to maximize societies welfare, with welfare very broadly defined, definitelyincluding such things as clean air and nice views as well as financial aspects

Microbiology for chemical engineers - from macro to micro scale

Recent developments in microbial techniques (such as PCR, GE, FISH) have allowed researchers to detect, identify and quantify microorganisms without the limitation of culture-dependent methods. This has given both engineers and scientists a more fundamental understanding about systems containing microorganisms. These techniques can be used to monitor bacteria in wastewater treatment systems, soil and sea, industrial fermentation, food technology, and improve floccability, etc. However, despite these techniques being readily available and relatively cheap, they are not widely used by engineers. Hence, the aim of this paper is to introduce these techniques, and their applications, to chemical engineers. Two different studies related to industrial wastewater treatment, but applicable to general microorganism systems, will be presented: (1) microbial stability of pure cultures, and (2) bioreactor population shifts during alternating operational conditions. In (1), two bioreactors, inoculated with two different pure cultures, (A) Xanthobacter aut GJ10 and (B) Bulkholderia sp JS150, degrading 1,2-dichloroethane (DCE) and monochlorobenzene (MCB), respectively, were followed over time (Emanuelsson et al ., 2005). Specific and universal 16S rRNA oligonucleotide probes were used to identify the bacteria. It was found that bioreactor (A) remained pure for 290 days, whereas bioreactor (B) became contaminated within one week. The difference in behaviour is attributed to the pathway required to degrade DCE. In (2), the stability of a bacterial strain, which was isolated on the basis of its capability to degrade 2-fluorobenzoate from contaminated soil, in three different, up-flow fixed bed reactors operated under shock loads and starvation periods, was followed by denaturing gradient gel electrophoresis (DGGE) (Emanuelsson et al ., 2006). All bioreactors were rapidly colonised by different bacteria; however, the communities remained fairly stable over time, and shifts in bacterial populations were mainly found during the starvation periods

A universal mechanism generating clusters of differentiated loci during divergence-with-migration

Genome-wide patterns of genetic divergence reveal mechanisms of adaptation under gene flow. Empirical data show that divergence is mostly concentrated in narrow genomic regions. This pattern may arise because differentiated loci protect nearby mutations from gene flow, but recent theory suggests this mechanism is insufficient to explain the emergence of concentrated differentiation during biologically realistic timescales. Critically, earlier theory neglects an inevitable consequence of genetic drift: stochastic loss of local genomic divergence. Here we demonstrate that the rate of stochastic loss of weak local differentiation increases with recombination distance to a strongly diverged locus and, above a critical recombination distance, local loss is faster than local 'gain' of new differentiation. Under high migration and weak selection this critical recombination distance is much smaller than the total recombination distance of the genomic region under selection. Consequently, divergence between populations increases by net gain of new differentiation within the critical recombination distance, resulting in tightly-linked clusters of divergence. The mechanism responsible is the balance between stochastic loss and gain of weak local differentiation, a mechanism acting universally throughout the genome. Our results will help to explain empirical observations and lead to novel predictions regarding changes in genomic architectures during adaptive divergence. This article is protected by copyright. All rights reserved

Biotreatment of industrial wastewaters under transient-state conditions: process stability with fluctuations of organic load, substrates, toxicants, and environmental parameters

Biotreatment of industrial wastewater is often challenged by operation under transient states with respect to organic loads, pollutants, and physical characteristics. Furthermore, the potential presence of inhibitory compounds requires careful monitoring and adequate process design. This review describes difficulties encountered in biological treatment of wastewater with highly variable influent characteristics. Typical design aspects of biological processes are presented and discussed with respect to their success in treating highly fluctuating wastewaters. In general, biomass retention is a key factor for dealing with highly fluctuating and/or inhibitory wastewater, but the how it operates also affects the stability of performance, as it was shown that dynamic operation instead of operation at a constant flow enhances biodegradation onset and more evenly distributed activity. Although ultimately stable effluent quality must be achieved, the microbial population stability is not necessarily high, as it was shown that microbial diversity and flexibility may play a critical role in functional stability.info:eu-repo/semantics/acceptedVersio

Treatment of halogenated organic compounds and monitoring of microbial dynamics in up-flow fixed bed reactors under sequentially alternating pollutant scenarios

Two up-flow fixed bed reactors (UFBR) were operated for 8 months treating a model synthetic wastewater containing 2-fluorobenzoate (2-FB) and dichloromethane (DCM). The stability of the reactors under dynamic conditions, that is, sequentially alternating pollutants (SAP), shock loads, and starvation periods was assessed. Two support materials were used: expanded clay (EC) that does not adsorb 2-FB or DCM, and granular-activated carbon (GAC) that adsorbs 180 mg gg⁻¹ of 2-FB and 390 mg gg⁻¹ of DCM. The reactors were inoculated with a 2-FB-degrading strain (FB2) and a DCM degrader (TM1). 2-FB was fed at organic loads ranging from 0 to 800 mg L⁻¹ d⁻¹, while DCM was fed at 0–250 mg L⁻¹ d⁻¹. 2-FB or DCM were never detected at the outlet of the GAC reactor, while in the EC reactor outlet small amounts were observed. Nevertheless, the highest biological elimination capacity was observed in the EC reactor (over 700 mg L⁻¹ d⁻¹ of 2-FB). DGGE analysis revealed a fairly stable bacterial community with the largest shifts occurring during starvation periods and changes in feed composition. Several bacterial strains isolated from the reactors showed capacity for 2-FB degradation, while only strain TM1 degraded DCM

Isolation of a Xanthobacter sp. degrading dichloromethane and characterization of the gene involved in the degradation

A bacterial strain able to degrade dichloromethane (DCM) as the sole carbon source was isolated from a wastewater treatment plant receiving domestic and pharmaceutical effluent. 16S rDNA studies revealed the strain to be a Xanthobacter sp. (strain TM1). The new isolated strain when grown aerobically on DCM showed Luong type growth kinetics, with lmax of 0.094 h-1 and Sm of 1,435 mg l-1. Strain TM1 was able to degrade other aromatic and aliphatic halogenated compounds, such as halobenzoates, 2-chloroethanol and dichloroethane. The gene for DCM dehalogenase, which is the key enzyme in DCM degradation, was amplified through PCR reactions. Strain TM1 contains type A DCM dehalogenase (dcmAa), while no product could be obtained for type B dehalogense (dcmAb). The sequence was compared against 12 dcmAa from other DCM degrading strains and 98% or 99% similarity was observed with all other previously isolated DCM dehalogenase genes. This is the first time a Xanthobacter sp. is reported to degrade DCM.info:eu-repo/semantics/acceptedVersio

Biodegradation of 2-fluorobenzoate in upflow fixed bed bioreactors operated with different growth support materials

Three upflow fixed bed bioreactors treating an aqueous stream containing 2-fluorobenzoate were operated for a period of 7months, during which they were exposed to high organic loading rates and starvation. The reactors contained granular activated carbon (GAC), polyethylene (PE) particles and expanded clay (EC) respectively as growth support for microbial biofilms. The performance of the reactors was compared and the biofilm microbial population was followed by cell counting and denaturing gradient gel electrophoresis (DGGE). The reactor containing GAC always had 100% removal efficiency owing to the adsorption properties of thematerial combined with biodegradation. The GAC reactor also recovered better after starvation periods in the sense that it showed more stable behaviour than the reactors containing EC and PE. The highest biological elimination capacity was observed for the reactor containing EC, which reached 200mg day−1 L−1 during reactor start-up, but during long-termoperation the reactor containing GAC showed the highest biological elimination capacity, 140mg day−1 L−1. DGGE analysis indicated that starvation periods seemed to be responsible for shifts in the microbial population

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks