Nitrogen loss (NH3, N2O) patterns in bench-scale composting.

Nitrogen (N) losses during composting reduce the value of the end product as a fertilizer. Nitrogen is lost during composting mainly by ammonia (NH3) volatilization in the thermophilic phase. We used three bench-scale aerobic bioreactors with a controlled temperature difference (CDT) system as an experimental approach to investigate the pattern of N losses during composting. N2O peak emission occurred much earlier (7h) than NH3 volatilization (48-60h) during the thermophilic phase (~55°C) of bench-scale composting. The NH3 volatilization peak rate occurred following the greater biological activity (O2 consumed/CO2 evolved) at 40°C which could coincide with greater ammonification, but immobilization of NH4+/NH3 also occurs at this point affecting NH3 volatilization. Differences in temperature curves and accumulated NH3-N were related to the biological activity in each vessel. Therefore, O2 consumed/CO2 evolved measurements must be part of the evaluation of composting in further studies for comparisons of techniques to reduce NH3 volatilizatio

Historical trends in pH and carbonate biogeochemistry on the Belize Mesoamerican barrier reef system

Coral reefs are important ecosystems that are increasingly negatively impacted by human activities. Understanding which anthropogenic stressors play the most significant role in their decline is vital for the accurate prediction of future trends in coral reef health and for effective mitigation of these threats. Here we present annually resolved boron and carbon isotope measurements of two cores capturing the past 90 years of growth of the tropical reef‐building coral Siderastrea siderea from the Belize Mesoamerican Barrier Reef System. The pairing of these two isotope systems allows us to parse the reconstructed pH change into relative changes in net ecosystem productivity and net ecosystem calcification between the two locations. This approach reveals that the relationship between seawater pH and coral calcification, at both a colony and ecosystem level, is complex and cannot simply be modeled as linear or even positive. This study also underscores both the utility of coupled δ11B‐δ13C measurements in tracing past biogeochemical cycling in coral reefs and the complexity of this cycling relative to the open ocean

Size-dependent response of foraminiferal calcification to seawater carbonate chemistry

Michael J. Henehan acknowledges financial support from the Yale Peabody Museum.The response of the marine carbon cycle to changes in atmospheric CO2 concentrations will be determined, in part, by the relative response of calcifying and non-calcifying organisms to global change. Planktonic foraminifera are responsible for a quarter or more of global carbonate production, therefore understanding the sensitivity of calcification in these organisms to environmental change is critical. Despite this, there remains little consensus as to whether, or to what extent, chemical and physical factors affect foraminiferal calcification. To address this, we directly test the effect of multiple controls on calcification in culture experiments and core-top measurements of Globigerinoides ruber. We find that two factors, body size and the carbonate system, strongly influence calcification intensity in life, but that exposure to corrosive bottom waters can overprint this signal post mortem. Using a simple model for the addition of calcite through ontogeny, we show that variable body size between and within datasets could complicate studies that examine environmental controls on foraminiferal shell weight. In addition, we suggest that size could ultimately play a role in determining whether calcification will increase or decrease with acidification. Our models highlight that knowledge of the specific morphological and physiological mechanisms driving ontogenetic change in calcification in different species will be critical in predicting the of foraminiferal calcification to future change in atmospheric pCO2.Publisher PDFPeer reviewe

siDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi), mediated by 21-nucleotide (nt)-length small interfering RNAs (siRNAs), is a powerful tool not only for studying gene function but also for therapeutic applications. RNAi, requiring perfect complementarity between the siRNA guide strand and the target mRNA, was believed to be extremely specific. However, a recent growing body of evidence has suggested that siRNA could down-regulate unintended genes whose transcripts possess complementarity to the 7-nt siRNA seed region. This off-target gene silencing may often provide incongruous results obtained from knockdown experiments, leading to misinterpretation. Thus, an efficient algorithm for designing functional siRNAs with minimal off-target effect based on the mechanistic features is considered of value.</p> <p>Results</p> <p>We present siDirect 2.0, an update of our web-based software siDirect, which provides functional and off-target minimized siRNA design for mammalian RNAi. The previous version of our software designed functional siRNAs by considering the relationship between siRNA sequence and RNAi activity, and provided them along with the enumeration of potential off-target gene candidates by using a fast and sensitive homology search algorithm. In the new version, the siRNA design algorithm is extensively updated to eliminate off-target effects by reflecting our recent finding that the capability of siRNA to induce off-target effect is highly correlated to the thermodynamic stability, or the melting temperature (Tm), of the seed-target duplex, which is formed between the nucleotides positioned at 2-8 from the 5' end of the siRNA guide strand and its target mRNA. Selection of siRNAs with lower seed-target duplex stabilities (benchmark Tm < 21.5°C) followed by the elimination of unrelated transcripts with nearly perfect match should minimize the off-target effects.</p> <p>Conclusion</p> <p>siDirect 2.0 provides functional, target-specific siRNA design with the updated algorithm which significantly reduces off-target silencing. When the candidate functional siRNAs could form seed-target duplexes with Tm values below 21.5°C, and their 19-nt regions spanning positions 2-20 of both strands have at least two mismatches to any other non-targeted transcripts, siDirect 2.0 can design at least one qualified siRNA for >94% of human mRNA sequences in RefSeq. siDirect 2.0 is available at <url>http://siDirect2.RNAi.jp/</url>.</p

Agronomic Management of Indigenous Mycorrhizas

Many of the advantages conferred to plants by arbuscular mycorrhiza (AM) are associated to the ability of AM plants to explore a greater volume of soil through the extraradical mycelium. Sieverding (1991) estimates that for each centimetre of colonized root there is an increase of 15 cm3 on the volume of soil explored, this value can increase to 200 cm3 depending on the circumstances. Due to the enhancement of the volume of soil explored and the ability of the extraradical mycelium to absorb and translocate nutrients to the plant, one of the most obvious and important advantages resulting from mycorrhization is the uptake of nutrients. Among of which the ones that have immobilized forms in soil, such as P, assume particular significance. Besides this, many other benefits are recognized for AM plants (Gupta et al, 2000): water stress alleviation (Augé, 2004; Cho et al, 2006), protection from root pathogens (Graham, 2001), tolerance to toxic heavy metals and phytoremediation (Audet and Charest, 2006; Göhre and Paszkowski, 2006), tolerance to adverse conditions such as very high or low temperature, high salinity (Sannazzaro et al, 2006), high or low pH (Yano and Takaki, 2005) or better performance during transplantation shock (Subhan et al, 1998). The extraradical hyphae also stabilize soil aggregates by both enmeshing soil particles (Miller e Jastrow, 1992) and producing a glycoprotein, golmalin, which may act as a glue-like substance to adhere soil particles together (Wright and Upadhyaya, 1998). Despite the ubiquous distribution of mycorrhizal fungi (Smith and Read, 2000) and only a relative specificity between host plants and fungal isolates (McGonigle and Fitter, 1990), the obligate nature of the symbiosis implies the establishment of a plant propagation system, either under greenhouse conditions or in vitro laboratory propagation. These techniques result in high inoculum production costs, which still remains a serious problem since they are not competitive with production costs of phosphorus fertilizer. Even if farmers understand the significance of sustainable agricultural systems, the reduction of phosphorus inputs by using AM fungal inocula alone cannot be justified except, perhaps, in the case of high value crops (Saioto and Marumoto, 2002). Nurseries, high income horticulture farmers and no-agricultural application such as rehabilitation of degraded or devegetated landscapes are examples of areas where the use of commercial inoculum is current. Another serious problem is quality of commercial available products concerning guarantee of phatogene free content, storage conditions, most effective application methods and what types to use. Besides the information provided by suppliers about its inoculum can be deceiving, as from the usually referred total counts, only a fraction may be effective for a particular plant or in specific soil conditions. Gianinazzi and Vosátka (2004) assume that progress should be made towards registration procedures that stimulate the development of the mycorrhizal industry. Some on-farm inoculum production and application methods have been studied, allowing farmers to produce locally adapted isolates and generate a taxonomically diverse inoculum (Mohandas et al, 2004; Douds et al, 2005). However the inocula produced this way are not readily processed for mechanical application to the fields, being an obstacle to the utilization in large scale agriculture, especially row crops, moreover it would represent an additional mechanical operation with the corresponding economic and soil compaction costs. It is well recognized that inoculation of AM fungi has a potential significance in not only sustainable crop production, but also environmental conservation. However, the status quo of inoculation is far from practical technology that can be widely used in the field. Together a further basic understanding of the biology and diversity of AM fungi is needed (Abbott at al, 1995; Saito and Marumoto, 2002). Advances in ecology during the past decade have led to a much more detailed understanding of the potential negative consequences of species introductions and the potential for negative ecological consequences of invasions by mycorrhizal fungi is poorly understood. Schwartz et al, (2006) recommend that a careful assessment documenting the need for inoculation, and the likelihood of success, should be conducted prior to inoculation because inoculations are not universally beneficial. Agricultural practices such as crop rotation, tillage, weed control and fertilizer apllication all produce changes in the chemical, physical and biological soil variables and affect the ecological niches available for occupancy by the soil biota, influencing in different ways the symbiosis performance and consequently the inoculum development, shaping changes and upset balance of native populations. The molecular biology tools developed in the latest years have been very important for our perception of these changes, ensuing awareness of management choice implications in AM development. In this context, for extensive farming systems and regarding environmental and economic costs, the identification of agronomic management practices that allow controlled manipulation of the fungal community and capitalization of AM mutualistic effect making use of local inoculum, seem to be a wise option for mycorrhiza promotion and development of sustainable crop production

Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques