A Miscanthus plantation can be carbon neutral without increasing soil carbon stocks

National governments and international organizations perceive bioenergy, from crops such as Miscanthus, to have an important role in mitigating greenhouse gas (GHG) emissions and combating climate change. In this research, we address three objectives aimed at reducing uncertainty regarding the climate change mitigation potential of commercial Miscanthus plantations in the United Kingdom: (i) to examine soil temperature and moisture as potential drivers of soil GHG emissions through four years of parallel measurements, (ii) to quantify carbon (C) dynamics associated with soil sequestration using regular measurements of topsoil (0–30 cm) C and the surface litter layer and (iii) to calculate a life cycle GHG budget using site‐specific measurements, enabling the GHG intensity of Miscanthus used for electricity generation to be compared against coal and natural gas. Our results show that methane (CH₄) and nitrous oxide (N₂O) emissions contributed little to the overall GHG budget of Miscanthus, while soil respiration offset 30% of the crop's net aboveground C uptake. Temperature sensitivity of soil respiration was highest during crop growth and lowest during winter months. We observed no significant change in topsoil C or nitrogen stocks following 7 years of Miscanthus cultivation. The depth of litter did, however, increase significantly, stabilizing at approximately 7 tonnes dry biomass per hectare after 6 years. The cradle‐to‐farm gate GHG budget of this crop indicated a net removal of 24.5 t CO₂‐eq ha^?1 yr^?1 from the atmosphere despite no detectable C sequestration in soils. When scaled up to consider the full life cycle, Miscanthus fared very well in comparison with coal and natural gas, suggesting considerable CO₂ offsetting per kWh generated. Although the comparison does not account for the land area requirements of the energy generated, Miscanthus used for electricity generation can make a significant contribution to climate change mitigation even when combusted in conventional steam turbine power plants.     

Varieties of Representation in Evolved and Embodied Neural Networks

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem.     

Reducing greenhouse gas emissions and adapting agricultural management for climate change in developing countries: providing the basis for action

Agriculture in developing countries has attracted increasing attention in international negotiations within the United Nations Framework Convention on Climate Change for both adaptation to climate change and greenhouse gas mitigation. However, there is limited understanding about potential complementarity between management practices that promote adaptation and mitigation, and limited basis to account for greenhouse gas emission reductions in this sector. The good news is that the global research community could provide the support needed to address these issues through further research linking adaptation and mitigation. In addition, a small shift in strategy by the Intergovernmental Panel on Climate Change (IPCC) and ongoing assistance from agricultural organizations could produce a framework to move the research and development from concept to reality. In turn, significant progress is possible in the near term providing the basis for UNFCCC negotiations to move beyond discussion to action for the agricultural sector in developing countries.     

Methylglyoxal resistance in Bacillus subtilis: contributions of bacillithiol‐dependent and independent pathways

Methylglyoxal (MG) is a toxic by‐product of glycolysis that damages DNA and proteins ultimately leading to cell death. Protection from MG is often conferred by a glutathione‐dependent glyoxalase pathway. However, glutathione is absent from the low‐GC Gram‐positive Firmicutes, such as Bacillus subtilis. The identification of bacillithiol (BSH) as the major low‐molecular‐weight thiol in the Firmicutes raises the possibility that BSH is involved in MG detoxification. Here, we demonstrate that MG can rapidly and specifically deplete BSH in cells, and we identify both BSH‐dependent and BSH‐independent MG resistance pathways. The BSH‐dependent pathway utilizes glyoxalase I (GlxA, formerly YwbC) and glyoxalase II (GlxB, formerly YurT) to convert MG to d ‐lactate. The critical step in this pathway is the activation of the KhtSTU K⁺ efflux pump by the S‐lactoyl‐BSH intermediate, which leads to cytoplasmic acidification. We show that cytoplasmic acidification is both necessary and sufficient for maximal protection from MG. Two additional MG detoxification pathways operate independent of BSH. The first involves three enzymes (YdeA, YraA and YfkM) which are predicted to be homologues of glyoxalase III that converts MG to d ‐lactate, and the second involves YhdN, previously shown to be a broad specificity aldo‐keto reductase that converts MG to acetol.     

Discriminating power of localized three-dimensional facial morphology

Many genetic syndromes involve a facial gestalt that suggests a preliminary diagnosis to an experienced clinical geneticist even before a clinical examination and genotyping are undertaken. Previously, using visualization and pattern recognition, we showed that dense surface models (DSMs) of full face shape characterize facial dysmorphology in Noonan and in 22q11 deletion syndromes. In this much larger study of 696 individuals, we extend the use of DSMs of the full face to establish accurate discrimination between controls and individuals with Williams, Smith-Magenis, 22q11 deletion, or Noonan syndromes and between individuals with different syndromes in these groups. However, the full power of the DSM approach is demonstrated by the comparable discriminating abilities of localized facial features, such as periorbital, perinasal, and perioral patches, and the correlation of DSM-based predictions and molecular findings. This study demonstrates the potential of face shape models to assist clinical training through visualization, to support clinical diagnosis of affected individuals through pattern recognition, and to enable the objective comparison of individuals sharing other phenotypic or genotypic properties.     

The plasma peptidome

Background

It may be possible to discover new diagnostic or therapeutic peptides or proteins from blood plasma using LC–ESI–MS/MS to identify, with a linear quadrupole ion trap to identify, quantify and compare the statistical distributions of peptides cleaved ex vivo from plasma samples from different clinical populations.

Methods

A systematic method for the organic fractionation of plasma peptides was applied to identify and quantify the endogenous tryptic peptides from human plasma from multiple institutions by C18 HPLC followed nano electrospray ionization and tandem mass spectrometry (LC–ESI–MS/MS) with a linear quadrupole ion trap. The endogenous tryptic peptides, or tryptic phospho peptides (i.e. without exogenous digestion), were extracted in a mixture of organic solvent and water, dried and collected by preparative C18. The tryptic peptides from 6 institutions with 12 different disease and normal EDTA plasma populations, alongside ice cold controls for pre-analytical variation, were characterized by mass spectrometry. Each patient plasma was precipitated in 90% acetonitrile and the endogenous tryptic peptides extracted by a stepwise gradient of increasing water and then formic acid resulting in 10 sub-fractions. The fractionated peptides were manually collected over preparative C18 and injected for 1508 LC–ESI–MS/MS experiments analyzed in SQL Server R.

Results

Peptides that were cleaved in human plasma by a tryptic activity ex vivo provided convenient and sensitive access to most human proteins in plasma that show differences in the frequency or intensity of proteins observed across populations that may have clinical significance. Combination of step wise organic extraction of 200 μL of plasma with nano electrospray resulted in the confident identification and quantification ~?14,000 gene symbols by X!TANDEM that is the largest number of blood proteins identified to date and shows that you can monitor the ex vivo proteolysis of most human proteins, including interleukins, from blood. A total of 15,968,550 MS/MS spectra ≥?E4 intensity counts were correlated by the SEQUEST and X!TANDEM algorithms to a federated library of 157,478 protein sequences that were filtered for best charge state (2+ or 3+) and peptide sequence in SQL Server resulting in 1,916,672 distinct best-fit peptide correlations for analysis with the R statistical system. SEQUEST identified some 140,054 protein accessions, or some ~?26,000 gene symbols, proteins or loci, with at least 5 independent correlations. The X!TANDEM algorithm made at least 5 best fit correlations to more than 14,000 protein gene symbols with p-values and FDR corrected q-values of ~?0.001 or less. Log₁₀ peptide intensity values showed a Gaussian distribution from E8 to E4 arbitrary counts by quantile plot, and significant variation in average precursor intensity across the disease and controls treatments by ANOVA with means compared by the Tukey–Kramer test. STRING analysis of the top 2000 gene symbols showed a tight association of cellular proteins that were apparently present in the plasma as protein complexes with related cellular components, molecular functions and biological processes.

Conclusions

The random and independent sampling of pre-fractionated blood peptides by LC-ESI-MS/MS with SQL Server-R analysis revealed the largest plasma proteome to date and was a practical method to quantify and compare the frequency or log₁₀ intensity of individual proteins cleaved ex vivo across populations of plasma samples from multiple clinical locations to discover treatment-specific variation using classical statistics suitable for clinical science. It was possible to identify and quantify nearly all human proteins from EDTA plasma and compare the results of thousands of LC–ESI–MS/MS experiments from multiple clinical populations using standard database methods in SQL Server and classical statistical strategies in the R data analysis system.

     

Emissions of methane from northern peatlands: a review of management impacts and implications for future management options

Northern peatlands constitute a significant source of atmospheric methane (CH₄). However, management of undisturbed peatlands, as well as the restoration of disturbed peatlands, will alter the exchange of CH₄ with the atmosphere. The aim of this systematic review and meta‐analysis was to collate and analyze published studies to improve our understanding of the factors that control CH₄ emissions and the impacts of management on the gas flux from northern (latitude 40° to 70°N) peatlands. The analysis includes a total of 87 studies reporting measurements of CH₄ emissions taken at 186 sites covering different countries, peatland types, and management systems. Results show that CH₄ emissions from natural northern peatlands are highly variable with a 95% CI of 7.6–15.7 g C m^?2 year^?1 for the mean and 3.3–6.3 g C m^?2 year^?1 for the median. The overall annual average (mean ± SD) is 12 ± 21 g C m^?2 year^?1 with the highest emissions from fen ecosystems. Methane emissions from natural peatlands are mainly controlled by water table (WT) depth, plant community composition, and soil pH. Although mean annual air temperature is not a good predictor of CH₄ emissions by itself, the interaction between temperature, plant community cover, WT depth, and soil pH is important. According to short‐term forecasts of climate change, these complex interactions will be the main determinant of CH₄ emissions from northern peatlands. Drainage significantly (p < .05) reduces CH₄ emissions to the atmosphere, on average by 84%. Restoration of drained peatlands by rewetting or vegetation/rewetting increases CH₄ emissions on average by 46% compared to the original premanagement CH₄ fluxes. However, to fully evaluate the net effect of management practice on the greenhouse gas balance from high latitude peatlands, both net ecosystem exchange (NEE) and carbon exports need to be considered.     

Decoupling of greenhouse gas emissions from global agricultural production: 1970–2050

Since 1970 global agricultural production has more than doubled; contributing ~1/4 of total anthropogenic greenhouse gas (GHG) burden in 2010. Food production must increase to feed our growing demands, but to address climate change, GHG emissions must decrease. Using an identity approach, we estimate and analyse past trends in GHG emission intensities from global agricultural production and land‐use change and project potential future emissions. The novel Kaya–Porter identity framework deconstructs the entity of emissions from a mix of multiple sources of GHGs into attributable elements allowing not only a combined analysis of the total level of all emissions jointly with emissions per unit area and emissions per unit product. It also allows us to examine how a change in emissions from a given source contributes to the change in total emissions over time. We show that agricultural production and GHGs have been steadily decoupled over recent decades. Emissions peaked in 1991 at ~12 Pg CO₂‐eq. yr^?1 and have not exceeded this since. Since 1970 GHG emissions per unit product have declined by 39% and 44% for crop‐ and livestock‐production, respectively. Except for the energy‐use component of farming, emissions from all sources have increased less than agricultural production. Our projected business‐as‐usual range suggests that emissions may be further decoupled by 20–55% giving absolute agricultural emissions of 8.2–14.5 Pg CO₂‐eq. yr^?1 by 2050, significantly lower than many previous estimates that do not allow for decoupling. Beyond this, several additional costcompetitive mitigation measures could reduce emissions further. However, agricultural GHG emissions can only be reduced to a certain level and a simultaneous focus on other parts of the food‐system is necessary to increase food security whilst reducing emissions. The identity approach presented here could be used as a methodological framework for more holistic food systems analysis.