Assessing the role of soil chemoautotrophs in carbon cycling: An investigation into isotopically labelled soil microorganisms

Recently observed increases in atmospheric CO2 have created great interest in carbon capture technologies and natural sinks of this major component of the carbon cycle. Humic substances are a large, operationally defined fraction of soil organic matter. It was thought that humic substances consist of cross-linked macromolecular structures forming a distinct class of compounds. However, it was recently concluded by members of my research group that the vast majority of humic material in soils, are a complex mixture of microbial/plant biopolymers and degradation products, and not a distinct chemical category. The postulation that microbial inputs to soil carbon are greatly underestimated was put forward by my research group in 2007. Therefore, I have attempted to demonstrate the inputs made by soil chemoautotrophic bacteria. A method was developed where soil samples were measured for chemoautotrophic activity by subjecting them to a suite of scientific techniques. A growth chamber was used to propagate extant soil chemoautotrophic bacteria from different soils and subjected to an array of chemical and biological analyses. The growth chamber was used to measure CO2 concentrations and introduce stable isotopic 13CO2. Estimations of CO2 sequestration were made using direct measurements for Irish soils and one Eurasian soil. Isotope labelled DNA was isolated using cesium chloride gradient ultracentrifugation. The dominant chemoautotrophic bacteria uncovered were Thiobacillus denitrificans and Thiobacillus thioparus. Labelled biomass was isolated and described using GCMS-IRMS and NMR, where an array of PLFAs, protein/peptide, carbohydrates and aliphatics were observed. Finally, an attempt to mimic common agricultural practice was performed to measure soil chemoautotrophic activity. This demonstrated the capability of this approach to benefit carbon flux estimations and hopefully in the future help to elucidate carbon flow into soils for the greater environment

Flexible network reconstruction from relational databases with Cytoscape and CytoSQL

<p>Abstract</p> <p>Background</p> <p>Molecular interaction networks can be efficiently studied using network visualization software such as Cytoscape. The relevant nodes, edges and their attributes can be imported in Cytoscape in various file formats, or directly from external databases through specialized third party plugins. However, molecular data are often stored in relational databases with their own specific structure, for which dedicated plugins do not exist. Therefore, a more generic solution is presented.</p> <p>Results</p> <p>A new Cytoscape plugin 'CytoSQL' is developed to connect Cytoscape to any relational database. It allows to launch SQL ('Structured Query Language') queries from within Cytoscape, with the option to inject node or edge features of an existing network as SQL arguments, and to convert the retrieved data to Cytoscape network components. Supported by a set of case studies we demonstrate the flexibility and the power of the CytoSQL plugin in converting specific data subsets into meaningful network representations.</p> <p>Conclusions</p> <p>CytoSQL offers a unified approach to let Cytoscape interact with relational databases. Thanks to the power of the SQL syntax, this tool can rapidly generate and enrich networks according to very complex criteria. The plugin is available at <url>http://www.ptools.ua.ac.be/CytoSQL</url>.</p

Characteristics of free air carbon dioxide enrichment of a northern temperate mature forest

Tausz, M ORCiD: 0000-0001-8205-8561In 2017, the Birmingham Institute of Forest Research (BIFoR) began to conduct Free Air Carbon Dioxide Enrichment (FACE) within a mature broadleaf deciduous forest situated in the United Kingdom. BIFoR FACE employs large scale infrastructure, in the form of lattice towers, forming 'arrays' which encircle a forest plot of ~30 m diameter. BIFoR FACE consists of three treatment arrays to elevate local CO2 concentrations (e[CO2 ]) by +150 μmol mol-1 . In practice, acceptable operational enrichment (ambient [CO2 ] + e[CO2 ]) is ± 20% of the set-point 1-minute average target. There are a further three arrays that replicate the infrastructure and deliver ambient air as paired controls for the treatment arrays. For the first growing season with e[CO2 ] (April to November 2017), [CO2 ] measurements in treatment and control arrays show that the target concentration was successfully delivered, i.e.: +147 ± 21 μmol mol-1 (mean ± SD) or 98 ± 14% of set-point enrichment target. e[CO2 ] treatment was accomplished for 97.7% of the scheduled operation time, with the remaining time lost due to engineering faults (0.6% of the time), CO2 supply issues (0.6%), or adverse weather conditions (1.1%). CO2 demand in the facility was driven predominantly by wind speed and the formation of the deciduous canopy. Deviations greater than 10% from the ambient baseline CO2 occurred  80 μmol mol-1 (i.e., > 53% of the treatment increment) into control arrays accounted for < 0.1% of the enrichment period. The median [CO2 ] values in reconstructed 3-dimensional [CO2 ] fields show enrichment somewhat lower than the target but still well above ambient. The data presented here provide confidence in the facility setup and can be used to guide future next-generation forest FACE facilities built into tall and complex forest stands. This article is protected by copyright. All rights reserved

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health

Neurodegenerative Properties of Chronic Pain: Cognitive Decline in Patients with Chronic Pancreatitis

Chronic pain has been associated with impaired cognitive function. We examined cognitive performance in patients with severe chronic pancreatitis pain. We explored the following factors for their contribution to observed cognitive deficits: pain duration, comorbidity (depression, sleep disturbance), use of opioids, and premorbid alcohol abuse. The cognitive profiles of 16 patients with severe pain due to chronic pancreatitis were determined using an extensive neuropsychological test battery. Data from three cognitive domains (psychomotor performance, memory, executive functions) were compared to data from healthy controls matched for age, gender and education. Multivariate multilevel analysis of the data showed decreased test scores in patients with chronic pancreatitis pain in different cognitive domains. Psychomotor performance and executive functions showed the most prominent decline. Interestingly, pain duration appeared to be the strongest predictor for observed cognitive decline. Depressive symptoms, sleep disturbance, opioid use and history of alcohol abuse provided additional explanations for the observed cognitive decline in some of the tests, but to a lesser extent than pain duration. The negative effect of pain duration on cognitive performance is compatible with the theory of neurodegenerative properties of chronic pain. Therefore, early and effective therapeutic interventions might reduce or prevent decline in cognitive performance, thereby improving outcomes and quality of life in these patients